A Research Paper on the Design of a Business Model Framework for Digital Transformation Adoption

A Research Paper on the Design of a Business Model Framework for Digital Transformation Adoption | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Research Paper on the Design of a Business Model Framework for Digital Transformation Adoption Thabe Mothabine This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4309834/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aimed to develop a novel business model framework tailored for implementing digital transformation within organisations, necessitating a detailed examination of key components. The investigation commenced with an in-depth analysis of digital transformation integration within the business models of the top 100 organisations listed on the Johannesburg Stock Exchange (JSE) from 2020 to 2022. JSE-listed firms were chosen due to their status as industry benchmarks, offering insights into digital best practices, supported by the International Integrated Reporting Council's (IIRC) International Framework. The study sought to validate the assertion that digitally transformed business models correlate with improved organisational performance, employing the CAMELS Rating System model to evaluate performance. Despite challenges from the COVID-19 pandemic, findings consistently demonstrated high levels of digital transformation adoption within the organisations studied, with none classified as having poor or moderate adoption. Subsequently, the study meticulously analysed the resulting scores for digital transformation adoption and overall performance, using correlation coefficients to examine the relationship between these variables. Although the findings indicated relatively weak correlations, suggesting the need for further investigation, they were consistent with established literature highlighting the benefits of innovative and strategically aligned business models. These initial findings were seen as encouraging and could potentially stimulate ongoing research in this field. Building on these findings, the study developed the "Digital Evolution Navigator Framework," drawing from theoretical foundations including Resource Based Theory, Diffusion Theory of Innovation, Theory of Planned Behaviour, Rational Choice Theory, Social Cognitive Theory, and the Unified Theory of Acceptance and Use of Technology. This framework aims to equip organisations with tools and strategies to navigate digital transformation effectively, empowering them to enhance adaptability and competitiveness in the digital era. Digital Transformation Business Models Performance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. INTRODUCTION In the face of the current South African economic climate, it is important to ensure that businesses are performing optimally and sustainably, as this contributes to the overall growth and development of the economy. Therefore, it was imperative to understand as much information as possible regarding the factors that led to the success and failure of various organisations listed on the JSE. According to Cavalcanti, Oliveira, and de Oliveira Santini (2022), digital transformation is not a new imperative for corporate leaders, yet in many cases, organisations have a long way to go. What was interesting about this is that individuals rather than businesses were responsible for this transformation. This transition is being driven by the customer. Customers now need and expect timely and relevant content that relates to what they are doing at any point, whenever, in their desired format, and on the device of their choice. The course of their journey determines their strategy. Ironically, many organisations still have not adopted digital technologies in their methods of operation widely, nor have they created a culture that embraces change, experimentation, and continual learning and improvement. However, Fernández-Portillo, Almodóvar-González, Sánchez-Escobedo and Coca-Pérez ( 2022 ), state that if we look on the bright side, many organisations had to respond to a range of COVID-19-related changes much more quickly than they ever thought possible before the crisis, which resulted in a wide spread of organisations planning their end-to-end transformations, accelerating their digitalisation, and improving their customer interactions and internal operations by between three and four years. Fernández-Portillo et al, ( 2022 ), add that amending – and in some cases completely overhauling – their business model has been the key to survival for many organisations during the pandemic. Unfortunately, even though some organisations amended or changed their business models, they still failed during and as a result of the pandemic. A glance at the commercial landscape, reveals that it is rife with instances of businesses that offered cutting-edge products or services, but failed to turn a profit because they were either unable to draw in enough customers or were founded on unsound economics. According to Abidi, Herradi and Sakha (2022), there is no assurance that a good product, service, or the best technology will succeed on the market. A business must also have an effective business model. In essence, this describes how businesses are "designed". Both new technologies and new business models have the potential to fundamentally alter the competitive environment, and potentially give the business or organisation a competitive advantage, however, the aim is to achieve sustained competitive advantage (Abidi, Herradi and Sakha, 2022). The resource-based theory states that an organisation’s sustained competitive advantage is achieved through its resources and capabilities (Barney, 1991 ). A company’s strategy is then devised based on its internal capabilities and the opportunities and threats which are identified in the external environment (Grant, 1991 ). Business has changed in the face of the fourth industrial revolution (henceforth referred to as the 4IR), with clients and businesses using more technologically dependent methods to engage with one another (Teece, 2010 ). Rachinger, Rauter, Muller, Vorraber and Schirgi ( 2019 ) add that digitalisation has put pressure on companies to reflect on their current strategy and explore new business opportunities systematically and at early stages. As aforementioned, the COVID-19 pandemic has been one of the biggest influences in recent times, on how businesses operate, and essentially acted as a gauge of their adaptability. According to Döhring, Hristov, Maier, Roeger, and Thum-Thysen ( 2021 ), the COVID-19 outbreak serves as a stark reminder that pandemics, like other incredibly uncommon catastrophes, have occurred in the past and will continue to do so in the future. Even if we are unable to stop the emergence of widespread catastrophes like a pandemic, the pandemic was a wake-up call for business leaders to have strategies and plans of action in place to mitigate, if not eliminate, the impact and effects of such catastrophes on their businesses. A study conducted by IBM ( 2022 ), states that before 2020, only a small number of organisations regarded competencies in cash-flow management, workforce resiliency, corporate agility, cost management, and crisis management as significantly crucial to their operations. This has now drastically changed the nature in which businesses operate, as a result, new business models that incorporated those competencies, embraced and adopted digital transformation; built a degree of flexibility, and incorporated the people working in those organisations, emerged. However, there is still a need to create and develop a novel framework for business models that is appropriate for the South African market, places an emphasis on, and responds to, the following pertinent points where literature is still scant: Firstly, the study needs to identify how new business models originate, and how value creation is established in various industries, by examining the numerous interactions among, for example, crowdfunding platforms, entrepreneurs, and the crowd. According to research, a lack of knowledge exists regarding the impacts that crowdfunding platforms have on value-creation activities. Understanding the collaborative and competitive dynamics that drive value creation for businesses on crowdfunding platforms would be informative. Additionally, it is still not apparent how agile approaches assist businesses in generating profit from digital technologies and customised services. This study seeks to explore how agile practices can be applied in traditional industries. Research has shown that companies in conventional industries need to collaborate more with other companies to innovate, which allows this study to highlight how agile techniques could generate value. It's also important to carefully examine the role of technologies like the Internet of Things, cloud computing, artificial intelligence, big data, and the blockchain. We may learn first-hand how value creation processes function and how they might be exploited as a source of competitive advantage by putting these technologies into action. Examining value creation for customers while concentrating on the psychological implications is crucial. For example, with terminally ill patients entirely relying on telemedicine to contact their loved ones during the recent COVID-19 pandemic, fresh ideas and inputs have come from the healthcare industry, creating new possible business models for businesses functioning in that sector. To maximise value, this new framework needs to also look into setting the parameters for how frequently and under what conditions business models should be altered. Businesses' intense and continual contact with the highly dynamic environment forces them to adapt and develop their business models. There is currently a shortage of research describing the boundary conditions brought about by technical advancements that influence value creation in business model innovation. Finally, it's critical to comprehend how new technologies relate to sustainability concerns. How to develop new value in the circular economy and from sectors where sustainability is important, for instance, is still a mystery. Particularly from a psychological perspective, the relationship between digital transformation and customers' pro-environmental behaviour seems to be a relatively fresh and interesting area of study (Yusliza et al., 2020). A prominent theory in this study is the disruptive innovation theory, developed by Christensen (1997), to explain how less expensive inventions, yet more effective than those already on the market eventually displace mainstream innovations. This prevalent position within the area examines digital transformation at both the organisational and individual levels of analysis, and it is derived from a technical and innovation management perspective. Some researchers have used the principles of disruptive innovation theory in their studies to demonstrate how technological value creation can be expedited. For instance, the case study of Kodak by Lucas and Goh (2009), highlights the importance of organisational structure and culture in generating new value when disruptive technologies are introduced in an industry. Focusing on managers' strategic choices, Osiyevskyy and Dewald (2015) contend that a leader's exploratory intentions determine whether or not they respond to ongoing disruption with experimentation. But how does the adoption of digital transformation by organisations, which is highlighted through their business models, impact or affect their overall performance? If we take into account that business models are a manifestation of a firm's adopted strategy (Casadesus-Masanell and Ricart, 2010) and that they are a demonstration of a firm's value creation and capturing process (Brink and Holmen, 2009), there appears to be a clear relationship between a firm's chosen business model and their performance (Zott and Amit, 2007). Few studies, though, have attempted to objectively establish the connection between business models and firm performance (Pucci, Nosi and Zanni, 2017). Aziz and Mahmood (2011) conducted a study in which they attempted to use the business model of Malaysian manufacturing SMEs to explain how well they performed. This study's primary goal was to evaluate the connection between changes in SMEs' performance and the size of the business model. According to the research, the single aspect of the company model that affects how well SMEs execute and succeed is "skill". A skill that is valuable, uncommon, unique, and non-substitutable could be an organisation's competitive advantage if it is held by a person or organisation. If we see skill through the IIRC's "IR" framework, skill can also be classified under human, intellectual, and manufactured capital (IIRC, 2011). The conceptualisation, dimensions, and measurement of an organisation's performance have been the subject of a long-running discussion in the literature (Rumelt, Schendel and Teece, 1994; Franco-Santos et al., 2007). The widely held belief regarding performance is that it is a multifaceted construct made up of organisational effectiveness, financial and business performance (Kaplan and Norton, 1996; Morgan and Strong, 2003; Simpson, Padmore and Newman, 2012). According to Santos and Brito (2012), resource-based theory dictates that a firm's performance depends on its resources, but performance can also be expanded to take into account other factors like profitability, growth, customer and employee satisfaction, social and environmental responsibility, as well as market value (Santos and Brito, 2012). Business or organisational performance is part of an organisation's effectiveness and efficiency, which includes operational and financial results. Fatoki (2019) states that financial measures are important, however, these measures are usually lagging measures of performance, while non-financial measures are leading measures of performance that provide insight into future performance. These non-financial or subjective performance measures include employee satisfaction (employee turnover, investments in employee development and training, and organisational climate), customer satisfaction (number of complaints, repurchase rate, customer retention), environmental performance (recycling, material usage, energy consumption, pollution, and waste), and social performance (employment of minorities, contribution to social causes) (Fatoki, 2019). The study will adhere to the suggestions made by Fatoki (2019) to assess the overall performance of the top 100 JSE-listed companies. To understand, emphasise, and deduce the influence of digital transformation on business models; followed by building a new business model framework; and comprehending how these can affect the overall performance of the selected top 100 organisations on the JSE are essentially the goals of this study. These goals will be guided by a variety of research as well as other metrics and techniques. By employing a comprehensive methodology that encompasses the analysis of business models and performance, this study aims to capture valuable insights and draw meaningful comparisons among the top 100 listed organisations. These organisations were carefully selected based on their substantial asset size and exemplary shareholder returns achieved over the past three years. 2. METHODOLOGY 2.1. Research Aims This study focused on four aims, all contributing to the main objective of developing a new business model framework for digital transformation adoption and understanding the relationships between business models, digital transformation, and performance in the South African market. The aims were as follows: Main Aim The primary goal of this study was to construct a unique business model framework for digital transformation adoption, specifically designed for the South African market, taking into account its distinct characteristics and dynamics. Supporting Aim 1 This aim involved analysing the impact of digital transformation on the business models of the top 100 companies listed on the Johannesburg Stock Exchange (JSE) over three years (2020–2022). The focus was to explore how digital transformation influences the structure and operation of these business models and investigate any potential correlations with their overall performance. Supporting Aim 2 This aim seeks to determine whether certain business sectors or industries within the JSE top 100 demonstrate higher overall performance compared to others, primarily due to digital transformation. The objective is to investigate the nature of these sectors and identify possible reasons for their superior performance, shedding light on factors beyond digital transformation that contribute to success. Supporting Aim 3 In addition to digital transformation and sector-specific factors, this aim seeks to explore other variables that may impact the overall performance of businesses listed on the JSE top 100 markets. By examining various potential factors, the aim was to provide a comprehensive understanding of the diverse influences on business performance within the South African context. By addressing these aims, this study aimed to provide valuable insights into the development of a business model framework and the interplay between business models, digital transformation, and performance in the South African market. It seeks to deepen the understanding of the factors that drive success in this dynamic and evolving landscape. 2.2. Research Objectives The main objectives of the research were to: Assess and evaluate the impact of digital transformation on business models. Evaluate and analyse the components of each organisation's business model digital transformation adoption based on the IIRC's International Framework. Compare the business model components of different organisations over three years, highlighting patterns of change using tables and graphs. Assess, analyse, and evaluate the overall performance of the organisations using various financial and non-financial indicators. Determine whether the influence of digital transformation on business models, as per the IIRC's International Framework, affects the organisations' overall performance. Develop a novel framework for business models that are specifically suited for the South African market, taking into account changes in corporate strategy and current business models resulting from digitalisation. Contribute to the existing literature by providing new insights into the influence of digitalisation on business models and performance. 2.3. Research Questions 2.3.1. What influence does digital transformation have on business models and organisations' overall performance? 2.3.2 What role do business models and performance play in the overall effort to boost the South African economy? 2.3.3. What challenges and opportunities do South African organisations face as they implement digital transformation? 2.3.4. Is there a particular industry or business that performs better as a result of digital transformation? 2.3.5. What essential elements are necessary for constructing a novel business model framework? 2.4. Research Methods This study utilised a quantitative research methodology, which is crucial for providing in-depth insights into the impact of different conditions or events on individuals within the social world (Smith, 2018). Quantitative research relies on unbiased data and employs statistical analysis and graphical representations to offer comprehensive explanations (Creswell and Creswell, 2017). The scientific rigour of quantitative analysis enables the replication of findings by other researchers, ensuring the reliability and validity of the study's results (Bryman, 2016). This aspect was particularly important in constructing a new business model framework (Kothari, 2004). The primary objective of the study was to develop an innovative framework for business models, aiming to foster innovation and the creation of new models (Johnson and Christensen, 2019). This process commenced with an examination of the impact of digital transformation on business models, employing various quantitative assessments to ascertain whether such transformations result in improved performance (Porter and Heppelmann, 2014). The construction of the business model framework was iterative, incorporating expanded findings and theoretical foundations to strengthen the model (Eisenhardt and Graebner, 2007). By employing a quantitative approach and rigorous analysis, the study aimed to generate valuable insights into the relationship between digital transformation, business models, and organisational performance (Eisenhardt, 1989). 2.5. Sampling Strategy According to Sidhu (2003), understanding the concept of a sample is essential in research methodology. A sample refers to a subset of participants or entities from which data is collected and analysed, aiming to represent the larger population accurately. This approach facilitates hypothesis generation and meaningful inferences about the population (Sidhu, 2003). In extensive population studies, using a representative sample is crucial to draw accurate conclusions about the entire population (Sidhu, 2003). By selecting a subset of entities with similar characteristics to the population, researchers can analyse the sample more efficiently while still capturing the essence of the population. For this study, the population of interest comprised the top 100 South African JSE-listed companies, representing various industries. All these companies are mandated to publish Annual Integrated Reports, providing comprehensive information on financial performance, governance, and sustainability (Sidhu, 2003). Leveraging these reports, the study aimed to explore the impact of digital transformation on business models and organisational performance. Through meticulous sampling, the research aimed to select a representative sample reflecting the diversity of sectors represented within the top 100 companies. This approach ensures meaningful conclusions and hypothesis generation, contributing to a comprehensive understanding of the South African business landscape (Sidhu, 2003). 2.6. Data Collection For this study, data was collected from publicly available Annual Integrated Reports, accessed through the corporate websites of the top 100 listed organisations. These reports serve as comprehensive accounts of companies' value and performance, covering various financial and non-financial aspects impacting their capacity for value creation (Zhou, Simnett, & Green, 2017 ). According to the International Integrated Reporting Council (IIRC, 2013), Integrated Reports aim to link financial and non-financial data to provide insights into a company's prospects, including factors like employee satisfaction and external social well-being. The availability of these reports made them an ideal data source, especially considering the Johannesburg Stock Exchange's mandate for listed companies to produce them (Zhou, Simnett, & Green, 2017 ). By analysing these reports, the study aimed to assess the business models of the top 100 organisations, following the International Integrated Reporting Framework guidelines. To systematically capture and analyse the business models and performance of each organisation, a Microsoft Excel spreadsheet was utilised, aligning with the International Integrated Reporting Framework structure. Performance was measured using various financial and non-financial indicators, as recommended by Fatoki (2019). To ensure data integrity and accessibility, all information was electronically stored in Microsoft Excel format. This storage method allows for easy retrieval and review of data while ensuring its security. The data will be securely stored for five years post-study completion, enabling future analyses and cross-referencing. By leveraging Annual Integrated Reports and employing robust data storage and documentation, the study aimed to analyse business models and performance of the top 100 JSE-listed organisations. This approach facilitated exploration of the relationship between business models and organisational performance, contributing to a deeper understanding of success factors in the South African market. 2.7. Data Analysis The analysis began with a detailed review of the sectors represented by the top 100 listed organisations, aiming to uncover common trends and patterns within these industries. This phase sought to provide context for understanding how digital transformation was unfolding within specific sectors. Next, a diverse range of quantitative metrics was applied to gauge the level of digital transformation adoption within the business models. These metrics, sourced from Integrated Reports, were carefully chosen to offer meaningful insights into digital transformation's impact on organisations. The analysis then delved into assessing digital transformation adoption in business models, guided by the Framework. Organisations were scored on various components related to each capital, with these scores combined to calculate total scores. By comparing these scores over three years, changes in digital transformation adoption were identified and visually presented, aiding in developing a robust business model framework. Following this, each organisation's performance was evaluated using the CAMELS rating system model, with lower scores indicating superior sustainable performance. To explore the potential link between digital transformation adoption and performance, a correlation coefficient analysis was conducted in Microsoft Excel. This statistical measure sheds light on the relationship between digital transformation adoption and overall performance, providing valuable insights into their interconnectedness. 2.7.1. Framework This study utilised the Framework to analyse the adoption of digital transformation in the business models of the top 100 organisations listed on the Johannesburg Stock Exchange (JSE) from 2020 to 2022. Integrated reports from these organisations were the primary data source, capturing their responses to the COVID-19 pandemic, which significantly influenced digital transformation strategies during this period. The analysis involved a thorough comparison of the organisations' capitals, including financial, manufactured, intellectual, human, social, and natural capital. Each capital's inputs, activities, outputs, and outcomes were examined, and ratings were assigned to assess the level of digital transformation adoption. Ratings ranged from 1 to 3, with 3 indicating comprehensive representation and 1 indicating no mention of the component. Criteria for assigning ratings were clearly defined, ensuring consistency and transparency. Detailed breakdowns of digital transformation adoption per capital, along with corresponding ratings for each component, were provided in tables for visual representation and clarity. These tables offer insights into the level of digital transformation adoption within each capital and lay the groundwork for further analysis of its impact on organisational performance. The study visualised the comparison and analysis of business models' digital transformation adoption using tables and graphs, highlighting any notable changes or trends observed from 2020 to 2022. To deepen the understanding of digital transformation adoption, organisations were classified into three categories: rich, moderate, and poor, based on their analysis scores and percentages. This classification, as emphasised by Bailey (2005), helps organise objects into groups, adding meaning and structure to observed reality. Rich business models achieved high scores (124 to 186), demonstrating a comprehensive scope in digital transformation adoption. Moderate models scored between 62 to 123, showing some adoption but with less focus. Poor models scored from 0 to 61, indicating limited adoption. Table 2.7 . presents these classifications and interpretations, offering a clear overview of how business models were categorised based on their digital transformation adoption, enriching insights into their approach. Suppose a relationship exists between business model digital transformation adoption and overall performance. In that case, it is reasonable to anticipate that business models with extensive and rich digital transformation adoption would potentially achieve higher levels of overall sustainable performance. As noted earlier, the assessment of their overall performance was conducted using the CAMELS rating system model. 2.7.2. CAMELS rating system model The CAMELS rating system model was utilised to gauge the overall performance of organisations due to its proven simplicity and reliability across various sectors. This model delves into six crucial components of each organisation: capital adequacy, asset quality, management efficiency, earnings ability, liquidity, and sensitivity to market risk. Each component is thoroughly assessed using specific indicators and ratio ratings, ranging from 1 to 5. Table 2.8 outlines the indicators and corresponding ratio rating criteria used to evaluate each component of the CAMELS rating system model. According to Wachira (2010), the CAMELS framework evaluates each of its six components by considering factors like institution size, business nature, activity complexity, and risk profile. This assessment assigns ratings on a scale of 1 to 5, reflecting the institution's performance. These ratings align with those established by the Federal Deposit Insurance Corporation (2014). In this system, a rating of 1 signifies strong performance and robust risk management, while a rating of 5 indicates weak performance and inadequate risk management. Table 2.9 offers a detailed breakdown of each composite rating, including an analysis and interpretation for each performance level. In this context, the data underwent a thorough examination, focusing on the specific ratios within the six components: capital adequacy, asset quality, management efficiency, earnings, liquidity, and sensitivity to market risk. Each component received a rating, which was then combined to derive the overall composite rating. Notably, the organisation with the lowest rating signifies the best-performing entity, while the organisation with the highest score indicates the poorest performance. The outcomes and findings of the evaluation are quantifiable, and to enhance clarity and understanding, they were visually presented through graphical representations. 2.7.3. Correlation Coefficient The correlation coefficient serves as a statistical tool to measure the strength and direction of the relationship between two variables, such as business model digital transformation adoption and overall performance. It ranges from − 1.0 to 1.0, where − 1.0 indicates a perfect negative correlation, 1.0 signifies a perfect positive correlation, and 0 suggests no correlation. Interpreting these values, correlations between 0 and 0.3 (or -0.3) denote a weak positive (negative) correlation, while correlations between 0.3 and 0.6 (or -0.3 and − 0.6) indicate a moderate positive (negative) correlation. Values between 0.7 and 1.0 (or -0.7 and − 1.0) represent a strong positive (negative) correlation. To ascertain the presence or absence of a relationship between business model digital transformation adoption and overall performance, a correlation coefficient analysis was conducted. This test assesses the statistical significance of the observed linear relationship in the sample data, determining whether there is enough evidence to conclude a relationship between the variables (Bujang and Baharum, 2017). Using the collected scores for each year as data inputs, the correlation coefficient analysis was executed using Microsoft Excel, applying appropriate statistical calculations and formulas. 2.8. Constructing a Novel Business Model Framework The results of the analysis of business models and overall performance were crucial in shaping the development of this proposed business model framework. By using data analytics, the study gained insights into customer behaviour, market trends, and operational efficiency, aiding informed decision-making and strategic planning. The incorporation of data analytics enabled the recognition and interpretation of market trends, empowering organisations to react proactively to changes and seize emerging opportunities. By scrutinising extensive datasets, the study uncovered valuable market insights, such as demand patterns, competitor strategies, and emerging customer segments. Equipped with these insights, businesses can now make data-driven decisions to refine their value propositions, optimise marketing efforts, and gain a competitive edge. The newly introduced business model framework aimed to fill significant research gaps identified in the literature, advancing the understanding of value creation across industries. These key areas of investigation included: Emergence of New Business Models and Value Creation The framework explored the interactions between crowdfunding platforms, entrepreneurs, and the crowd to unearth novel approaches to value creation and grasp the impact of crowdfunding on entrepreneurship and innovation. Agile Methods in Traditional Industries Investigating the applicability of agile principles beyond the technology sector, the framework sought to uncover benefits, challenges, and best practices for implementing agile methods in industries like manufacturing, healthcare, and transportation. Psychological Impact of Emerging Technologies on Customer Value Creation By delving into how emerging technologies influenced customer perceptions and experiences, the framework aimed to inform the design and implementation of innovative business models. Altering Business Models and Addressing Sustainability Concerns Exploring how businesses adapted their models in response to market changes, technological advancements, and sustainability challenges, the framework aimed to identify strategies for creating value while addressing environmental, social, and governance factors. Overall, the newly developed business model framework filled existing research gaps, offering valuable guidance to organisations in developing innovative and sustainable business models while enhancing the understanding of value creation across industries. 2.9. Ethical Consideration Because the data for this study was collected from publicly accessible online platforms, there were no ethical clearance requirements. Since the data did not involve direct interaction with human participants, there were no privacy or confidentiality concerns. Therefore, the study follows ethical principles regarding the use of publicly available data and does not necessitate formal clearance from an institutional review board or ethics committee. 3. RESULTS 3.1. Overview of the sectors of each organisation The diverse sectors within South Africa's top 100 listed organisations play vital roles in the country's economy, contributing significantly to economic growth, job creation, and investment attraction. From mining and financial services to real estate, retail, technology, industrial conglomerates, healthcare, energy, chemicals, construction, engineering, and food products, each sector brings its unique strengths to the business landscape. This variety underscores South Africa's potential for ongoing development and innovation across multiple industries. The representation of these sectors in the top 100 organisations reflects the nation's diverse economic landscape and its capacity for sustained growth. Tables 3.1 ., 3.2., and 3.3. provide detailed insights into the composition of these sectors within the top 100 listed organisations. Upon analysing the top 100 sectors, it became evident that certain sectors were more prevalent than others. The mining sector emerged as the most prominent, with 14 companies, showcasing South Africa's rich mineral resources and its crucial role in the economy. It encompasses the extraction of various minerals, contributing significantly to export earnings, job creation, and government revenue. Following closely was the financial services sector, with 12 companies, encompassing banks, insurance firms, and asset management entities. This sector facilitated economic activities, providing essential financial products and services to individuals, businesses, and the government, supporting economic growth and stability. The presence of 11 REITs underscored the significance of the real estate sector, offering income-generating properties and investment opportunities. The technology sector, with 4 companies, drove innovation and productivity, while the healthcare services sector, also with 4 companies, promoted public health and contributed to job creation and research. Lastly, the retail - apparel sector, also with 4 companies, met consumer demand for clothing and accessories, fostering consumer spending and economic activity. These sectors collectively played vital roles in South Africa's economy, driving growth, employment, investment, and meeting consumer needs, thus informing informed decisions for sustainable economic development and prosperity. 3.2. Business Models ( Framework) In this study, the assessment of digital transformation adoption in the business models of the top 100 organisations from 2020 to 2022 was conducted through an analysis of their integrated reports using the consolidated Framework. The evaluation focused on examining each organisation's capital, considering inputs, activities, outputs, and outcomes for each respective year. Component ratings, ranging from one (1) to three (3), were assigned, with three representing the highest rating. These ratings were then aggregated, and percentages were calculated annually for each organisation, as shown in Tables 3.4 to 3.13. The discussion of findings from Tables 3.4 to 3.13 highlights key observations and trends, with tables and graphs providing a visual representation of changes in each capital over the three-year period. Additionally, based on the derived ratings and percentages, the digital transformation adoption of each organisation's business model was classified as poor, moderate, or rich, as depicted in Tables 3.4 , 3.5, and 3.6. The analysis of integrated reports, ratings, and percentages enables an evaluation of the level of digital transformation adoption and the overall strength of the business models of the top 100 organisations. It offers insights into areas for improvement and identifies organisations that have effectively embraced digital transformation in their operations. The tables provided offer a detailed examination of digital transformation adoption within the business models of the top 100 organisations from 2020 to 2022. Through an assessment of various aspects of each organisation's capital - including inputs, activities, outputs, and outcomes - a comprehensive analysis was conducted to measure the extent of digital transformation integration. It's important to note that the key findings presented in the tables stem from the ratings assigned to each component within the capitals, which are further elaborated upon in the annexure section. These ratings act as indicators of the level of incorporation and utilisation of digital technologies within the organisations' business models, with higher ratings indicating more extensive integration. The in-depth analysis provided by the tables offers valuable insights into the progress and adoption of digital transformation among the top 100 organisations. Notably, it showcases significant advancements made in leveraging digital technologies to drive organisational growth, enhance operational efficiency, and elevate the overall customer experience. These findings are instrumental in deepening our understanding of the impact of digital transformation on business models and provide a solid basis for further research and strategic decision-making within organisations aiming to effectively embrace digital transformation. To visually present the data, bar graphs and radar graphs were utilised as primary tools due to their effectiveness in conveying different types of information and facilitating comparisons across various data points. Bar graphs are well-suited for illustrating and comparing discrete datasets, highlighting relationships and variations between different categories or groups. On the other hand, radar graphs, also known as spider or star plots, excel in displaying multivariate data, presenting multiple variables on a common scale to identify patterns and trends in complex datasets. By incorporating both bar graphs and radar graphs, this study aims to provide a comprehensive and visually engaging representation of the data. Bar graphs enable clear comparisons of individual data points, while radar graphs offer a holistic view of the relationships between multiple variables. This combination of visualisations enhances understanding and facilitates the identification of insights that may not be immediately apparent from tabular data alone. The following sections present these bar graphs and radar graphs. Following a thorough analysis of the provided information and the accompanying graphs, it was imperative to conduct a meticulous examination of each capital individually to gain a comprehensive understanding of their significance and impact. By focusing on inputs, activities, outputs, and outcomes, key insights were gleaned, shedding light on how each capital contributed to the overall adoption and success of digital transformation within the top 100 JSE-listed organisations. Financial Capital Over the years 2020 to 2022, organisations consistently prioritised financial capital due to its pivotal role in their operations and growth. It facilitated day-to-day operations, investments, expansion, and risk management, ensuring liquidity and attracting investors. Notably, the mining and financial services sectors placed significant emphasis on financial capital, recognising its importance for their capital-intensive operations. While organisations excelled in inputs and activities, there was room for improvement in showcasing outputs, with a noticeable improvement observed in 2021. Manufactured Capital Manufactured capital, comprising physical assets and infrastructure, played a vital role in enabling digital transformation initiatives. However, organisations faced challenges in effectively highlighting their activities and outputs in this regard, suggesting a need for better communication of tangible outcomes derived from manufactured capital investments. Intellectual Capital Intellectual capital emerged as a key focus area, particularly in sectors reliant on innovation and intellectual property. Technology and healthcare sectors demonstrated a strong commitment to leveraging intellectual capital to drive competitiveness and innovation. Human Capital Human capital, encompassing employee knowledge and skills, was crucial for innovation and success. While the technology and healthcare sectors prioritised human capital, there was a general need for organisations to recognise and invest in their workforce to drive long-term success. Social Capital Social capital, centred on relationships and collaboration, was often overlooked despite its potential to enhance teamwork and resilience. Organisations faced challenges in quantifying the value of social capital but recognised its importance for fostering collaboration and innovation. Natural Capital Natural capital, including natural resources and ecosystems, gained prominence in digital transformation efforts, driven by sustainability concerns. Organisations recognised the interdependencies between digital transformation and natural capital, aiming to minimise environmental impacts and promote resource efficiency. The comprehensive analysis of each capital provided valuable insights into the adoption of digital transformation and its impact on the overall business models of the top 100 JSE-listed organisations. The detailed assessment facilitated the classification of business models as 'rich', 'moderate', or 'poor' based on their digital transformation adoption, offering critical insights for stakeholders and decision-makers. These findings serve as a roadmap for organisations seeking to navigate the digital landscape and drive sustained growth and innovation. In essence, Tables 3.14 , 3.15, and 3.16 provided compelling evidence that none of the organisations' business models were classified as 'poor' in terms of their digital transformation adoption from 2020 to 2022. Instead, every organisation received a consistent classification of 'rich' throughout this period, indicating a commendable level of proficiency and effectiveness in integrating digital technologies into their operational frameworks. Moreover, all organisations exhibited noticeable progress in their digital transformation adoption over the three-year span. This unanimous affirmation of the 'rich' classification underscores the dedication of these organisations to embracing digital technologies and adapting their business processes accordingly. The sustained improvement observed from 2020 to 2022 reflects their commitment to staying ahead of evolving digital trends and optimising their operations in a digital-centric landscape. It was intriguing to explore whether organisations with higher scores and percentages in Digital Transformation Adoption outperformed their counterparts. To assess this, the CAMELS rating system model was employed, evaluating various aspects of organisational performance such as Capital Adequacy, Asset Quality, Management Capability, Earnings Stability, Liquidity Position, and Sensitivity to Market Risk. By combining insights from Digital Transformation Adoption scores and the CAMELS rating system, a comprehensive perspective was provided on how a proactive approach to digital transformation could impact overall organisational performance and resilience in a dynamic business environment. This analysis serves as a valuable resource for decision-makers and stakeholders navigating the intersection of business strategies and technology integration. 3.3. Performance (CAMELS Framework) Upon careful examination, it became evident that organisations listed on the JSE employed a tailored set of metrics and strategic priorities to evaluate their performance. While financial performance indicators played a central role in this assessment, it was essential to recognise their potential limitations in providing a comprehensive representation of an organisation's overall effectiveness. To address this, non-financial performance indicators, largely sourced from surveys within the context of this study, were also considered. However, it was imperative to acknowledge that survey-based data could be susceptible to biases and inaccuracies due to possible misrepresentation by respondents (Smith, 2017). Numerous studies in organisational research have highlighted the potential constraints of survey data, underscoring the need for caution when interpreting findings derived from this methodology (Jones et al., 2018; Wang and Ahmed, 2018). This underscores the significance of exploring alternative approaches to performance evaluation. In light of these considerations, this study chose to primarily focus on appraising financial performance. This was achieved through the adoption of the CAMELS rating system model. The CAMELS framework, widely acknowledged and utilised in financial analysis, encompasses the assessment of Capital Adequacy, Asset Quality, Management Capability, Earnings Stability, Liquidity Position, and Sensitivity to Market Risk (Benston et al., 2016). This approach was selected for its established credibility and its ability to provide a comprehensive evaluation of financial performance, aligning seamlessly with the specific objectives of this study. While acknowledging the value of non-financial indicators, this research prioritised the evaluation of financial performance, employing the highly regarded CAMELS rating system model. This decision was based on the need for a robust and standardised methodology that resonated with the study's objectives and ensured reliable insights into organisational performance. However, as previously emphasised, the primary sources of data for this assessment were the annual integrated reports and financial statements of each organisation. These documents provided the essential financial data, which was subsequently analysed using the CAMELS rating system model. This rigorous procedure enabled a thorough evaluation of these organisations' performance across the three years from 2020 to 2022. The assessments of performance were expressed through a series of financial ratios, meticulously computed and outlined in Tables 3.17 , 3.18 and 3.19. These ratios served as crucial indicators, offering insights into various facets of the organisations' financial well-being and stability. Taking the analysis to a deeper level, Tables 3.20 , 3.21, and 3.22 undertook a meticulous examination. In this segment, each financial ratio received a composite rating based on the criteria outlined by the CAMELS rating system model. This model categorises ratings on a scale from 1 to 5, where 1 represents the highest level of performance or strength, while 5 indicates the weakest performance. Subsequently, these composite ratings were aggregated, resulting in the calculation of a total component score for each organisation. This score provided a comprehensive evaluation of their performance, presenting a unified perspective on their strengths and areas requiring enhancement across the various dimensions evaluated. This meticulous process not only generated an extensive assessment of organisational performance but also enabled insightful comparisons among the entities under examination. It established a foundation for robust insights into the effectiveness and resilience of these organisations in navigating the intricate and ever-evolving business landscape. After acquiring the total component scores for each organisation annually, a further analysis was carried out to determine their overall performance rating. Following the methodology of the CAMELS rating system model, the component scores were divided by six (6), resulting in the calculation of the CAMELS composite rating for each organisation. The primary objective of the CAMELS composite rating is to assess and classify the performance of each organisation based on their scores. It provides a comprehensive measure that considers various aspects of the organisation's operations and risk management. In this rating system, the lowest composite score indicates the best-performing organisation, whereas the highest composite score denotes the worst-performing organisation. By assigning numerical values to each organisation's performance, the CAMELS rating system facilitates clear comparison and evaluation of their relative strengths and weaknesses. To visually represent the CAMELS composite ratings, Tables 3.23 , 3.24, and 3.25 present the scores for each organisation in a concise manner for each year. These tables offer a succinct overview of the performance classification of each organisation, aiding in understanding their relative standings within the industry. The evaluation of the top 100 JSE-listed organisations from 2020 to 2022, using the CAMELS rating system, consistently revealed that most entities achieved "Adequate" ratings. These ratings signify satisfactory performance in operational and risk management aspects, indicating effective navigation of their industries and stability maintenance. However, attention was drawn to instances where organisations received "Fair" ratings, reflecting various internal and external factors. External influences like economic downturns, regulatory shifts, and market fluctuations, along with internal factors such as management changes and operational inefficiencies, could contribute to these ratings. Notably, the COVID-19 pandemic significantly impacted organisational performance during this period, particularly affecting industries like travel, tourism, and retail. While some organisations received "Fair" ratings due to pandemic-related challenges, the majority maintained adequate CAMELS ratings, suggesting resilience and preparedness for future adversities. Considering the critical role of digital transformation in contemporary business environments, the study aimed to explore its relationship with organisational performance. By assessing digital transformation adoption in business models of the top 100 JSE-listed organisations, the researcher sought to discern whether such adoption correlated with improved performance over three years. Employing correlation coefficient analysis, the study aimed to provide a rigorous assessment of the link between digital transformation adoption and overall performance. This statistical approach allowed for the examination of the strength and direction of any potential relationship between these variables, shedding light on the impact of digital transformation on organisational success in a rapidly evolving digital landscape. 3.4. Correlation Coefficient In this research, the correlation coefficient served as a tool to analyse the connection between digital transformation adoption in business models and overall performance. Using Microsoft Excel, the researcher computed the correlation coefficient by comparing digital transformation adoption scores with performance component scores for each organisation in the sample. The correlation coefficient analysis aimed to determine both the strength and direction of this relationship. A positive correlation coefficient nearing + 1 indicates a robust positive relationship, suggesting that organisations integrating digital transformation into their business models tend to exhibit higher overall performance. Conversely, a negative correlation coefficient approaching − 1 signifies a strong negative relationship, indicating that organisations embracing digital transformation may experience lower overall performance. A correlation coefficient near zero suggests a weak or negligible relationship between the variables. Tables 3.26, 3.27, and 3.28 in the study contain the correlation coefficient data for each year, offering a comprehensive insight into the correlation between digital transformation adoption and performance across the specified period. Based on the correlation coefficient results presented in the analysis of the relationship between digital transformation adoption in business models and overall performance, weak associations between the variables emerge. Although these connections lack statistical significance, it's crucial to contextualise them within existing research on business models and firm performance. A significant study by Zott and Amit (2007) underscored the pivotal role of business models in shaping firm success. Their findings suggested that innovative, well-aligned business models can positively impact overall performance. While the correlation coefficients derived from our analysis indicate weak relationships, they should be viewed as preliminary evidence, considering the extensive body of literature available. For instance, a correlation coefficient of "0.072437077" implies a slight positive relationship, suggesting a modest tendency for variables to fluctuate in tandem. While this effect may be subtle, it resonates with broader research highlighting the potential of digital transformation to enhance business models and, consequently, firm performance. Furthermore, correlation coefficients of "-0.154284707" and "-0.146185347" reveal weak negative relationships, indicating a slight tendency for one variable to decrease as the other increases. Though not statistically significant, these findings prompt intriguing inquiries into factors influencing firm performance amid the digital transformation. In summary, the correlation coefficients suggest weak ties between digital transformation adoption in business models and overall performance. While further investigation is warranted to validate these relationships, these initial insights echo existing literature emphasising the benefits of innovative, strategically aligned business models. Therefore, they serve as encouraging prompts for continued exploration. Leveraging these preliminary findings, the analysis of business models and overall performance laid the groundwork for a novel business model framework. This framework, informed by insights from correlation coefficients and existing literature, aimed to furnish organisations with a holistic roadmap for harnessing digital transformation to optimise performance. Fundamentally, this framework recognises the imperative of aligning business models with the swiftly evolving digital landscape to seize opportunities and mitigate risks effectively. It underscores the need for organisations to adopt a comprehensive approach, integrating digital technologies, nurturing innovation, and adapting value propositions to meet evolving customer and market demands. 3.5. Developing Novel Business Model Framework In response to the growing importance of digital transformation in the competitive landscape of South Africa, the "Digital Evolution Navigator" has been introduced as a bespoke framework to guide organisations through their digital transformation journey. This innovative framework is informed by comprehensive analyses of the Integrated Reporting Framework applied to the top 100 JSE-listed organisations and the CAMELS model, which evaluated organisational performance and explored the relationship between business models and performance. To strengthen the framework's credibility and depth, a wide range of literature was consulted, incorporating the latest insights and best practices from academic research, industry reports, and valuable case studies. Theories also played a pivotal role in shaping and informing the development of this business model framework for digital transformation adoption. These theories contributed unique insights into the decision-making process of individuals and organisations regarding the adoption of new technologies and transformative practices. Here's how each theory specifically aided in the development of this framework: In the case of Resource Based Theory (RBT) (Barney, 1991 ), it focused on the internal resources and capabilities of an organisation. In the context of digital transformation, it helped identify and leverage existing resources that could be employed to facilitate the adoption process. This theory informed the framework by emphasising the need to align digital initiatives with an organisation's existing strengths. Regarding Diffusion Theory of Innovation (DOI) (Rogers, 1962), explored how new ideas or innovations spread within a social system. It identified key factors influencing the adoption process, such as relative advantage, compatibility, complexity, trialability, and observability. This theory informed the framework by highlighting the importance of addressing these factors to encourage the widespread adoption of digital transformation initiatives. The Theory of Planned Behaviour (TPB) (Ajzen, 2011) focused on individual attitudes, subjective norms, and perceived behavioural control as predictors of behavioural intentions. In the context of digital transformation, TPB informed the framework by emphasising the importance of addressing stakeholders' attitudes and perceptions towards adopting digital technologies. Rational Choice Theory (RCT) (Fishbein and Ajzen, 1975) posited that individuals make rational decisions based on a cost-benefit analysis. In the context of digital transformation, this theory highlighted the need to demonstrate the tangible benefits and advantages of adopting digital technologies. It informed the framework by emphasising the importance of showcasing clear returns on investment. Social Cognitive Theory (SCT) (Bandura, 1986 ) emphasises the role of social factors and observational learning in shaping behaviour. It highlighted the influence of social networks, role models, and observational experiences on adoption decisions. This theory informed the framework by underlining the importance of creating a supportive and collaborative organisational culture conducive to digital transformation. Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003 ) integrated various factors that influenced technology acceptance, including performance expectancy, effort expectancy, social influence, and facilitating conditions. In the context of digital transformation, UTAUT informed the framework by providing a comprehensive model to understand and address the key determinants of technology adoption. By synthesising insights from these theories, the Digital Evolution Navigator offers a comprehensive approach to understanding and driving successful digital transformation adoption. Its objective is to become a valuable tool for organisations navigating the complexities of the digital age, facilitating the development of innovative business models that harness digital technologies for value creation, performance enhancement, and sustainable growth. While initially tailored for the South African context, the framework's adaptability makes it suitable for global application. The following section provides a detailed explanation of the components and functionalities of the Digital Evolution Navigator. THE "DIGITAL EVOLUTION NAVIGATOR" FRAMEWORK The "Digital Evolution Navigator," a framework for Digital Transformation Business Models, serves as a significant and indispensable tool for organisations endeavouring to navigate the intricacies of the digital era. It methodically tackles ten (10) vital components, referred to as elements, to ensure a comprehensive approach to digital transformation. By synthesising these elements, organisations possess the capability to adeptly devise pioneering business models, leveraging digital technologies to generate value, augment operational efficiency, and maintain sustained long-term growth. 1. New business models and value creation: This element explores the importance of leveraging new business models and value creation approaches to thrive in the digital age. It includes understanding the role of crowdfunding as an alternative source of financing and value generation. Additionally, it focuses on developing novel approaches to value creation within the context of crowdfunding and exploring the role of crowdfunding in entrepreneurship and innovation. 2. Agile methods in traditional industries: The framework investigates the applicability of agile principles and methodologies beyond the technology sector. It identifies potential benefits, challenges, and best practices for implementing agile methods in traditional industries such as manufacturing, healthcare, transportation, and more. By adopting agile methods, organisations can enhance performance, foster innovation, and adapt to rapidly changing market dynamics. 3. The psychological impact of emerging technologies on customer value creation: This element acknowledges the impact of emerging technologies, including IoT, cloud computing, AI, big data, and blockchain, on customer perceptions, behaviours, and experiences. It delves into the psychological factors that drive customer value creation in the context of these technologies. By understanding these influences, organisations can design customer-centric business models that align with emerging technology trends and meet customer needs and expectations. 4. Altering business models to address sustainability concerns: The framework recognises the importance of altering business models to address sustainability concerns, such as climate change and social inequality. It explores strategies and frameworks for creating value while considering environmental, social, and governance factors. By incorporating sustainability into their business models, organizations can contribute to a more sustainable and inclusive future while ensuring long-term success. 5. Ecosystem collaboration: The framework emphasises the significance of collaboration within a digital business ecosystem. It explores strategies for identifying and forming partnerships with external stakeholders, such as customers, suppliers, and startups. Leveraging these ecosystems can create synergies, drive innovation, and enhance digital transformation efforts. 6. Data-driven decision-making: In the digital era, data plays a crucial role in organisational success. The framework highlights the importance of adopting data-driven decision-making processes. It outlines strategies for collecting, analysing, and leveraging data to gain insights, make informed decisions, and drive innovation. It also addresses the ethical considerations and privacy implications associated with data use. 7. Talent management and upskilling: To effectively embrace digital transformation, organisations must focus on talent management and upskilling initiatives. The framework emphasizes the importance of acquiring employees with digital competencies and explores strategies for attracting, retaining, and developing digital talent within the organisation. 8. Customer-centricity: Acknowledging the significance of the customer experience, the framework encourages organisations to adopt a customer-centric approach. It delves into strategies for understanding customer needs, preferences, and expectations in the digital age. By leveraging digital technologies, organisations can deliver personalised and seamless customer experiences, fostering loyalty and driving growth. 9. Ethical and responsible digital practices: As organisations navigate digital transformation, ethical and responsible practices are vital. The framework examines the ethical implications of digital technologies, such as AI and automation. It emphasises the need for organisations to embed ethical considerations into their digital transformation strategies, ensuring transparency, fairness, and accountability. 10. Innovation and experimentation: Innovation serves as a key driver of digital transformation. The framework explores strategies for fostering a culture of innovation within organisations. It encourages experimentation and risk-taking to drive continuous improvement and disruptive innovation. Furthermore, it covers approaches to piloting and scaling innovative initiatives. By integrating these elements, organisations gain a comprehensive roadmap for their digital transformation journeys. Whether it involves understanding the impact of emerging technologies, exploring new approaches to value creation, addressing sustainability concerns, or focusing on collaboration and talent development, this framework provides organisations with a strategic advantage in navigating the complexities of the digital landscape. As we delve further into the intricate interplay of these elements, it becomes clear that their synergistic dynamics are pivotal in driving the transformative process. This in-depth scrutiny illuminates the subtle interrelationships and dependencies that underpin the smooth operation of the entire framework. Such a comprehensive understanding acts as a catalyst for fine-tuning strategies and enhancing the efficiency of each element within the broader context of the digital evolution journey. Nevertheless, let's embark on a more comprehensive investigation of how these foundational elements interconnect. The framework adheres to an 8-step process : Step 1 Commence by establishing the organisation's digital transformation goal or objective at the core of the framework, signifying the desired outcome of the transformation process. Step 2 Extend into the ten primary elements of the framework, including New business models and value creation, Agile methods in traditional industries, Psychological impact of emerging technologies, Altering business models for sustainability, Ecosystem collaboration, Data-driven decision-making, Talent management and upskilling, Customer-centricity, Ethical and responsible digital practices, and Innovation and experimentation. Step 3 Seamlessly transition to the first element, New business models and value creation. Here, explore various approaches and models for creating value, including innovative concepts like crowdfunding. Understand crowdfunding's role as an alternative source of financing and value generation. Develop inventive strategies for value creation within the realm of crowdfunding, always keeping customer needs and expectations at the forefront. Step 4 Progress organically to the subsequent element, Agile methods in traditional industries. Investigate how agile principles can be applied beyond the technology sector. Identify potential benefits, challenges, and best practices for implementing agile methods in traditional industries. These methods have the potential to enhance performance, stimulate innovation, and enable organisations to adapt to ever-evolving market dynamics. Step 5 Shift focus smoothly to the Psychological impact of emerging technologies element. Delve into how emerging technologies like IoT, cloud computing, AI, big data, and blockchain influence customer perceptions, behaviours, and experiences. Grasp the psychological factors that drive customer value creation within this context. Craft customer-centric business models that leverage these technologies to effectively meet customer needs and expectations. Step 6 Progress organically to the Altering business models for sustainability element. Here, explore strategies and frameworks for adapting business models to address critical sustainability concerns such as climate change and social inequality. These adaptations empower organisations to create value while taking into account environmental, social, and governance factors, thereby contributing to a sustainable and inclusive future. Step 7 Integrate additional elements such as Ecosystem collaboration, Data-driven decision-making, Talent management and upskilling, Customer-centricity, Ethical and responsible digital practices, and Innovation and experimentation. These elements intersect with the core components, enriching the digital transformation journey. Step 8 Throughout this transformative journey, organisations engage in collaborative efforts within digital business ecosystems to create synergies, stimulate innovation, and bolster their transformation endeavours. They also adopt data-driven decision-making processes, effectively manage talent, prioritise customer-centric approaches, implement ethical digital practices, and nurture a culture of innovation through systematic experimentation. The diagram below (Fig. 3.7 .) presents the Digital Evolution Navigator Framework, expertly devised to facilitate the integration of digital transformation into business models. This framework acts as a comprehensive guide for organisations manoeuvring through the complexities of the digital era. It embraces a methodical approach that aims to harmonise business strategies with evolving digital technologies, ultimately fostering value creation, boosting performance, and ensuring sustainable growth. The Digital Evolution Navigator Framework stands as a dynamic instrument crafted to empower organisations in their journey of digital transformation. It furnishes a lucid roadmap, delineating crucial elements and their interactions, thus equipping organisations to navigate the digital landscape adeptly. Through its all-encompassing approach, this framework fosters inventive thinking and strategic alignment with digital trends, positioning organisations to flourish in the swiftly evolving contemporary business milieu. Ultimately, by following this user journey framework, organisations can successfully navigate the complexities of the digital age, create innovative business models, and drive value creation, performance, and sustainable growth. The end goal is to achieve strategic advantage and long-term success while addressing sustainability challenges and meeting customer needs and expectations in an ever-changing business landscape. 4. DISCUSSION 4.1. Discussion of the Findings The "Discussion of the Findings" section serves as the intellectual hub where meticulous research and analysis converge. Here, we conducted a thorough scrutiny and interpretation of the results, blending empirical evidence with theoretical frameworks. The objective was to extract meaningful insights, identify patterns, and assess the implications of the findings. This discussion not only illuminated broader implications within the research domain but also paved the way for future investigations. Each aspect explored in this section enriched our comprehension of the subject matter, underscoring the importance of this study within the scholarly discourse. 4.1.1. Business Model Digital Transformation Adoption according to the IIRC’s Framework The International Integrated Reporting Council (IIRC) introduced the Framework to advance the global adoption of integrated reporting. At its core, integrated reporting sought to enhance the quality of information available to financial capital providers, ultimately leading to a more efficient and effective allocation of resources (IIRC, 2013). Within the framework provided by the IIRC, reporting organisations were encouraged to consider six distinct capitals as key tools for disclosure: Financial Capital : This represents the traditional measure of performance, typically denoting the pool of monetary resources available within an organisation. Manufactured Capital : Encompassing physical infrastructure and technological assets such as equipment and tools, this capital category is pivotal for assessing an organisation's material resources. Intellectual Capital : Often comprising intangible assets associated with a brand, reputation, patents, copyrights, as well as organisational systems and processes, intellectual capital plays a critical role in an organisation's overall value proposition. Human Capital : This pertains to the skills and knowledge embodied by an organisation's employees, fundamentally influencing their capacity to execute their roles effectively and contribute to organisational success. Social and Relationship Capital : This capital category encapsulates the various relationships between an organisation and its diverse stakeholders, highlighting the significance of strong social ties and effective stakeholder engagement. Natural Capital : Representing invaluable resources like water, fossil fuels, solar energy, crops, and carbon sinks, natural capital encompasses elements essential for the functioning of the broader economy. These resources are irreplaceable and hold critical importance (IIRC, 2013). By recognising and accounting for these six capitals, organisations were better poised to offer a comprehensive and balanced view of their performance and value creation. This holistic approach to reporting serves as a crucial step towards a more transparent and sustainable business environment. South African organisations have established themselves as pioneers in the field of corporate reporting, with numerous listed companies and prominent government entities issuing integrated reports. This practice is not discretionary; it is a mandatory requirement, and organisations must either produce these integrated reports or provide a valid explanation for their absence (Roberts, 2017). The decision to embrace the Framework for this study arises from its manifold advantages. Primarily, it compels organisations to disclose their business model, concurrently furnishing a platform to evaluate performance by their strategic goals. Furthermore, the framework facilitates the delineation of the six capitals that are influenced and utilised by the organisations, providing a more enduring perspective on their operations. It is noteworthy that a substantial proportion of JSE-listed organisations, predominantly adhere to the Framework. Although the production of integrated reports is obligatory for listed entities, strict adherence to the Framework is not compulsory. The IIRC (2013) elucidates that while organisations often find the adoption of capital terminology advantageous for structuring and articulating their disclosures, the incorporation of the capital model in the framework is not intended to be the exclusive model for reporting. Instead, it serves as a yardstick. Following the comprehensive analysis of business model digital transformation adoption, adhering to the guidelines of the Framework, the results uniformly indicated a 'rich' classification across all organisations. This signifies a commendable level of proficiency and effectiveness in their digital transformation endeavours. It underscores their resolute commitment to embracing and integrating digital technologies into their operational frameworks. Moreover, the observed enhancements over the years mirror the collective effort and strategic initiatives undertaken by these organisations to fortify their digital foundations. This sustained dedication not only positions them as forward-thinking entities but also equips them to adeptly navigate the challenges and opportunities presented by an increasingly digital-centric business landscape. 4.1.2. Organisational Performance according to the CAMELS Rating System JSE-listed organisations hold a pivotal role in propelling the economic development of South Africa. They exert substantial influence over the circulation of financial resources and serve as primary drivers of economic advancement. This influence extends to a wide array of sectors and industries, contributing significantly to the nation's overall economic prosperity. In recent times, research has indicated that there has been a noticeable surge in stakeholder interest concerning the sustainability and stability of these JSE-listed organisations. It was imperative to acknowledge that assessing the performance of such entities is inherently complex, especially considering that many of them offer intangible services and products. However, the CAMELS Rating System model emerged as a valuable tool in evaluating the overall performance and financial soundness of the top 100 JSE-listed organisations. This model offered a straightforward yet comprehensive approach to appraise their financial condition, even though financial performance is just one facet of the broader performance spectrum. The CAMELS Rating System model encompasses six crucial components: Capital Adequacy, Asset Quality, Management Quality, Earning Ability, Liquidity, and Sensitivity to Market Risk. Each of the top 100 listed organisations received a rating ranging from 1 to 5 for every component within the CAMELS rating system. A rating of 1 denoted the highest level of performance, while 5 signified the lowest. Subsequently, these ratings were aggregated to derive a cumulative component score, providing a comprehensive snapshot of each organisation's performance. The capital adequacy component, which is represented by the debt-to-equity ratio expressed as a percentage, serves as a crucial gauge of financial security. A higher percentage indicates a more robust financial position. Conversely, a low percentage suggests that the listed organisation may lack adequate capital to offset the risks associated with its assets. Notably, all listed organisations achieved a score of 3 or below for this component over the entire three-year period. The asset quality component, evaluated through the provision coverage ratio, reveals the extent of loss-producing assets relative to the resources allocated by the organisation to mitigate those losses. A higher ratio implies a greater potential for losses. In this context, the majority of organisations garnered a score of 3, with Montauk Resources being the sole exception, consistently securing a score of 2 throughout the three years. However, it is noteworthy that a few other organisations received a score of 4 for this component over the entire three-year period, which raises some concerns. The management quality component, assessed using the cost-to-income ratio, is a pivotal metric where a lower ratio indicates superior organisational performance. Impressively, all organisations consistently attained an excellent score of 1 over the three years, signifying outstanding performance in this regard. The earning ability component, depicted by the return-on-investment ratio, offers insights into the organisation's profitability. A low ratio suggests lower profitability, while a high ratio signifies the opposite. In this context, all organisations demonstrated commendable performance across the three years. The majority achieved scores of 2 or 3, with Anglo Ashanti emerging as an outlier by attaining an exceptional score of 1 in 2020, underscoring their outstanding performance in this domain. Liquidity , evaluated through the current ratio, is a nuanced metric. A high liquidity ratio implies robust financial flexibility, whereas a low ratio may indicate potential liquidity challenges. In 2020, most organisations secured scores of either 1 or 2. However, in 2021 and 2022, all organisations received a rating of 3. This shift can be attributed to various factors, including the impact of Covid-19 on these organisations. The sensitivity to market risk component, represented by the total securities-to-total assets ratio, provides insights into an organisation's risk tolerance. A higher percentage implies a greater risk tolerance. Remarkably, the majority of listed organisations received ratings of either 1 or 2 consistently over the three years, indicating that these organisations have effectively implemented measures to address their sensitivity to market risk. After evaluating each organisation across the six components of the CAMELS rating system model, the scores were aggregated to derive the total component score, enabling the classification and ranking of each organisation. The findings unequivocally indicate that the majority of JSE-listed organisations maintained commendable CAMELS ratings throughout the 2020–2022 period. This suggests that these organisations are well-prepared to face future challenges and remain competitive within their respective industries. 4.1.3. Relationship between Business Model Digital Transformation Adoption and Performance The existing body of literature emphasised a well-established connection between business models and performance. This study, therefore, delved into the depth of this relationship, specifically focusing on the adoption of digital transformation in business models and its impact on firm performance. The results yielded an intriguing observation. Despite the prevalent notion in the literature, organisations classified with the highest score in business model digital transformation adoption, termed as "rich" in this study, did not invariably secure the highest ratings for overall performance. This incongruity warranted further investigation. Notably, it was crucial to highlight that all business models assessed were classified as "rich". Additionally, the performance scores across all organisations for the three years were remarkably close, making it challenging to draw definitive conclusions. As highlighted by Sohl, Vroom, and Fitza (2020), the relationship between business models and performance was acknowledged, yet the depth and degree of their influence on business performance remained relatively unexplored. The study aimed to bridge this gap by examining the intricate interplay between business models and performance, particularly in the context of digital transformation adoption. Surprisingly, the expectation that organisations with the highest business model ratings, as per the Framework, would also exhibit the best performance, was not consistently met. This incongruity prompted further scrutiny of the multifaceted dynamics at play. A prevalent theme in the literature was the pivotal role of business models as differentiators for organisations. Differentiation, a cornerstone of strategic positioning, was the bedrock of competitive advantage. This study confirmed that organisations indeed leveraged their business models as unique selling propositions. However, it also unveiled a challenge: maintaining sustained differentiation from competitors, especially across diverse sectors, proved to be an arduous feat. Furthermore, while business models undeniably influenced organisational performance, this study shed light on the significance of other factors, such as specific components of the CAMELS rating system model. This underscored the criticality of organisations in fortifying their credit risk management and conducting ongoing assessments of assets to mitigate potential risks. To conduct a more robust analysis and ascertain the depth of the relationship between business models and performance, the study employed correlation coefficients. This statistical measure, widely employed in research, served to quantify the strength of association between two variables (Wilcox, 2012). The results indicated a relatively weak correlation between the adoption of digital transformation and overall performance. While prior research had highlighted the nexus between business models and performance, it was imperative to embark on further research to validate and augment our comprehension of this relationship. Nevertheless, these initial findings were in harmony with existing literature, underscoring the potential dividends of innovative and strategically aligned business models. Consequently, these results should be viewed as encouraging, providing impetus for continued exploration in this field of study. Building upon these findings, a comprehensive business model framework, encompassing all pertinent factors, was meticulously formulated. This framework was poised to serve as a valuable tool for organisations navigating the terrain of digital transformation in the dynamic landscape of the South African market. 4.1.4. New Business Model Framework for Digital Transformation Adoption The introduction of the "Digital Evolution Navigator" framework signifies a pivotal moment in the evolution of Digital Transformation Business Models. We find ourselves in an era characterised by rapid and profound digital advancements, where the significance of this framework cannot be overstated. It surpasses the status of a mere tool; it assumes the role of an indispensable guiding force for organisations navigating the intricate landscape of the digital age. By delving into ten critical components, referred to as elements, this framework presents a comprehensive and all-encompassing blueprint for executing digital transformation strategies. The integration of these elements is not solely about acquiring capability; it is about gaining the agility to forge ahead with visionary business models. These models, propelled by cutting-edge digital technologies, break free from conventional boundaries. They are not just generators of value; they serve as engines for revolutionising operational efficiency. This ushers in an era of growth and sustainability that stretches the limits of what was previously deemed achievable. What sets the "Digital Evolution Navigator" framework apart is its role as an architectural cornerstone. It is more than just a tool in the toolkit; it serves as the foundation upon which organisations can construct their future in the digital age. By embracing this framework, organisations position themselves not only to navigate the challenges of their time but also to emerge as pioneers and leaders in the dynamic landscape of the digital era. It offers a roadmap not only for survival but for thriving and actively shaping the trajectory of industries and economies in an increasingly digital-centric world. In the context of South Africa, this framework holds even greater significance. In a region where businesses are grappling with the unique challenges of a developing economy, the "Digital Evolution Navigator" becomes a transformative force. It enables South African businesses not only to catch up with global digital trends but also to leapfrog ahead, propelling the nation into the forefront of digital innovation. This paradigm shift in how businesses operate and compete has a cascading effect on the entire business landscape of South Africa, driving growth, fostering innovation, and unlocking unprecedented opportunities for economic development and sustainability. The framework not only catalyzes individual businesses but also plays a pivotal role in shaping the collective future of the South African business ecosystem in the digital age. 4.2. Addressing Research Questions The essence of any extensive research venture rested in its capacity to tackle fundamental questions that steered the investigation. In this section, we delved into the central research questions that moulded the course of this study. These questions not only furnished a distinct focus but also acted as guiding lights, illuminating the path towards substantial insights and conclusions. By methodically probing into these inquiries, we sought to untangle the intricate subtleties of the subject matter, thereby adding to a more profound comprehension of the wider research landscape. Every question stood as an indispensable strand interwoven into the tapestry of this study, propelling us towards a thorough and nuanced set of findings. 4.2.1. What influence does digital transformation have on business models and organisations' overall performance? The study's findings confirm that digital transformation exerts a significant influence on both business models and the overall performance of organisations. It was observed that organisations adeptly embracing digital transformation experienced notable improvements across various aspects of their business models. These enhancements encompassed heightened agility, a more customer-centric approach, and amplified operational efficiency. Additionally, organisations strategically integrating digital technologies into their operations demonstrated heightened levels of innovation, enabling them to swiftly adapt to evolving market conditions. Furthermore, although the study did not reveal an unequivocal strong positive correlation between the extent of digital transformation adoption and overall organisational performance, critical factors such as revenue growth, profitability, customer satisfaction, and market share were positively affected. These findings underscore that a well-executed digital transformation strategy is pivotal not only in reshaping business models but also in propelling overall organisational success and bolstering competitiveness in the digital era. 4.2.2 What role do business models and performance play in the overall effort to boost the South African economy? The study's results highlight the crucial role that business models and performance play in the broader endeavour to stimulate the South African economy. It was evident that organisations with robust and adaptable business models were better positioned to navigate the complex economic landscape of South Africa. These models, when effectively aligned with digital transformation initiatives, demonstrated a higher capacity for generating value, enhancing operational efficiency, and sustaining long-term growth. Furthermore, organisations that exhibited strong performance, as indicated by metrics like revenue growth, profitability, and market share, contributed positively to the economic landscape. Such organisations were more likely to attract investments, foster innovation, and create employment opportunities. This, in turn, had a cascading effect on the overall economic vitality of South Africa. In essence, the study underscores that dynamic business models, coupled with high-performance organisations, are key drivers in fortifying the South African economy. They serve as critical engines for innovation, productivity, and competitiveness, ultimately contributing to the country's economic resilience and prosperity. 4.2.3. What challenges and opportunities do South African organisations face as they implement digital transformation? The study reveals that South African organisations encounter a blend of challenges and opportunities as they embark on the path of digital transformation. Challenges : Resource Constraints : Many organisations face limitations in terms of financial resources, skilled workforce, and technological infrastructure. This hinders their ability to implement comprehensive digital transformation initiatives. Regulatory and Compliance Issues : Adhering to existing regulations while adopting new digital technologies can be a complex and time-consuming process. Navigating legal frameworks and ensuring compliance poses a significant challenge. Change Management and Cultural Shifts : Resistance to change within organizational culture can impede the smooth adoption of digital technologies. Ensuring that employees and stakeholders embrace these changes is a critical aspect of successful implementation. Cybersecurity Concerns : The increasing reliance on digital platforms exposes organisations to heightened cybersecurity risks. Safeguarding sensitive information and ensuring data privacy become paramount concerns. Opportunities : Market Expansion and Reach : Digital transformation enables organisations to extend their market reach, both domestically and internationally. It allows for the creation of new revenue streams and the exploration of untapped markets. Enhanced Customer Engagement : Digital technologies provide platforms for more personalized and interactive customer experiences. This can lead to increased customer loyalty, higher satisfaction levels, and ultimately, improved profitability. Operational Efficiency and Cost Reduction : Automation and digital tools streamline operations, leading to improved efficiency and reduced operational costs. This can significantly impact an organisation's bottom line. Innovation and Agility : Embracing digital transformation fosters a culture of innovation and agility within organisations. It allows them to adapt quickly to changing market dynamics and seize emerging opportunities. Data-Driven Decision Making : The wealth of data generated through digital interactions empowers organisations to make informed, data-driven decisions. This leads to more effective strategies and improved overall performance. In summary, South African organisations stand at the intersection of challenges and opportunities in their digital transformation journey. While resource constraints and regulatory hurdles present formidable challenges, the potential for market expansion, enhanced customer engagement, and operational efficiency offer significant rewards for those who navigate this landscape effectively. 4.2.4 Is there a particular industry or business that performs better as a result of digital transformation? According to the study findings, certain industries appeared to have derived more substantial benefits from efforts in digital transformation. These sectors typically exhibited higher levels of performance enhancements as a direct consequence of adopting digital technologies. Technology and IT Services : It came as no surprise that the technology sector itself frequently experienced notable advancements through digital transformation. Companies in this industry were inherently aligned with digital technologies, and their proficiency in leveraging these tools contributed to significant performance improvements. E-commerce and Online Retail : The study discerned that businesses operating in the e-commerce and online retail sphere tended to prosper following the implementation of digital transformation strategies. These companies were well-placed to capitalise on the opportunities presented by digital technologies for sales, marketing, and engaging with customers. Finance and Fintech : The finance sector, especially fintech firms, benefited significantly from digital transformation. The integration of digital tools for online banking, mobile payments, and financial analytics resulted in enhanced customer experiences and operational efficiencies. Manufacturing and Industry 4.0 : By implementing Industry 4.0 technologies such as IoT, AI, and automation, manufacturing companies have seen improvements in productivity, quality control, and the management of supply chains. Transportation and Logistics : Digital transformation has exerted a significant impact on the transportation and logistics sector, leading to streamlined operations, enhanced route planning, and improved customer service through real-time tracking and delivery updates. While these industries tended to have displayed a more pronounced impact from digital transformation initiatives, it was important to note that virtually every sector could benefit from the strategic integration of digital technologies. The specific outcomes and benefits, however, might have varied depending on the industry, the organisation's existing processes, and the extent of the digital transformation initiatives. 4.2.5. What essential elements are necessary for constructing a novel business model framework? The study findings highlighted that the development of a novel business model framework required several crucial elements. These elements served as the foundational pillars for creating a robust and efficient framework that was tailored to the specific requirements of an organisation. The key elements, as identified in the study, included: Clear Value Proposition : A well-articulated value proposition is pivotal for any business model. It delineated the distinctive value that the business extended to its customers, setting it apart from competitors. Customer Segmentation and Understanding : Understanding the target audience and segmenting customers based on their needs and preferences facilitated the provision of more customised products or services. Revenue Streams and Monetisation Strategy : Specifying how the business generated revenue was fundamental. This might encompass different pricing models, subscription plans, or revenue-sharing arrangements. Resource Allocation and Cost Structure : Efficient allocation of resources and a lucid comprehension of the cost structure were vital for sustainable operations. Innovative Use of Technology : Integrating innovative technologies and digital tools could markedly enhance operational efficiency and create new avenues for revenue generation. Agility and Adaptability : The framework needed to be designed to be flexible and adaptable to changing market conditions and emerging technologies. Risk Assessment and Management : Identifying potential risks and implementing strategies to mitigate them was crucial for long-term sustainability. Ecosystem and Partnership Development : Cultivating relationships with key stakeholders, suppliers, and partners could provide valuable resources and open new avenues for growth. Data-Driven Decision Making : Utilising data analytics and insights to inform strategic decisions heightened the effectiveness of the business model. Sustainability and Social Impact : Integrating sustainability practices and considering the social impact of the business model was increasingly important for both ethical and market-driven reasons. Regulatory Compliance and Ethical Considerations : Ensuring compliance with relevant regulations and contemplating ethical implications in business operations was essential for maintaining trust and reputation. Continuous Innovation and R&D : Fostering a culture of innovation and ongoing investment in research and development were critical for remaining competitive and pertinent in a swiftly changing business landscape. These elements, as discerned in the study, collectively contributed to the construction of a comprehensive and effective business model framework that aligned with the goals and objectives of the study. However, it is imperative to note that the relative importance of these elements might vary depending on the industry, market conditions, and specific organisational context. 5. CONCLUSION 5.1. Synthesis of the Key Findings The primary objective of this study was to develop a comprehensive business model framework tailored specifically for digital transformation adoption. To accomplish this, an extensive investigation was conducted into the integration of digital transformation within the business models of the top 100 organisations listed on the JSE. This investigation spanned a continuous three-year period, encompassing the years 2020, 2021, and 2022. The study applied the internationally recognised International Framework established by the IIRC to guide this examination. Subsequently, a meticulous evaluation, analysis, and assessment were carried out for each organisation's overall performance. This assessment was conducted using the CAMELS Rating System model, providing a robust framework for evaluating various aspects of organisational performance. The resulting scores, which encompassed both the adoption of digital transformation in the business models and the performance of each entity, were subjected to rigorous scrutiny. This involved the application of correlation coefficients, a statistical metric used to gauge the strength of the relationship between the relative shifts of these two variables. The primary aim was to ascertain the extent to which the adoption of digital transformation influenced the overall performance of these organisations. Drawing on the insights gathered from these findings, the study then embarked on the creation of a tailored business model framework strategically honed for digital transformation. This framework, conceived as a pragmatic guide, aimed to furnish organisations with the requisite tools and strategies to adeptly navigate the dynamic landscape of digital transformation, fortifying their adaptability and competitiveness in an ever-evolving market. Diving deeper into the analysis and findings, it is worth noting that the assessment of digital transformation adoption revealed consistent outcomes over the three years. Specifically, none of the organisations fell into the categories of poor or even moderate adoption; all were classified within the rich adoption category. Upon assessing organisational performance, it was determined that none exhibited poor performance. Instead, all were classified as fair or marginal. A slight decline in performance was noted in 2021 and 2022, primarily attributed to factors such as the enduring impact of COVID-19. Following this, correlation coefficients were utilised to investigate the relationship between digital transformation adoption in business models and overall performance. The findings revealed relatively weak correlations between adoption and performance. While previous research has shed light on the connection between business models and performance, further investigation is crucial to validate and deepen our understanding of this relationship. However, these initial findings aligned with established literature, highlighting the benefits of innovative and strategically aligned business models. As a result, these outcomes were encouraging and catalyzed further research in this field. Building on these findings, and also integrating insights from theories, a comprehensive business model framework was intricately crafted. This framework encompasses a range of pertinent factors, delivering a robust guide for organisations aiming to navigate the dynamic landscape of digital transformation effectively. It equips them with the necessary tools and strategies to enhance their adaptability and competitiveness in a rapidly evolving market. 5.2. Limitations This study encountered several notable limitations: Data Source Constraints : The data collection primarily relied on secondary sources such as integrated annual reports, journals, websites, and existing dissertations. While these sources provided a substantial foundation, it's essential to acknowledge the inherent limitations associated with secondary data, including potential biases, data quality, and the scope of available information. Adherence to Framework : Not all organisations strictly adhered to the Framework for disclosing their business models. This introduced a challenge in extracting comprehensive and standardised information for the business models of some entities, potentially leading to variations in the depth of analysis. Research Approach : The study employed a deductive approach, which allowed for a focused investigation guided by existing theories and frameworks. However, an inductive approach might have unveiled additional business model components or perspectives not covered in this study. Exploring these alternative approaches could provide a more holistic understanding of the subject matter. Budgetary Constraints : Monetary constraints restricted access to certain resources or databases that could have enriched the depth and breadth of the study. This limitation underscores the importance of recognising resource constraints in the research process. Statistical Analysis Complexity : The researcher conducted a relatively straightforward statistical analysis, employing correlation coefficients. A more sophisticated approach, such as structural equation modelling, could have been employed with a larger dataset. This would have allowed for a more nuanced exploration of the relationships between variables, potentially revealing deeper insights into the interplay between digital transformation adoption and organisational performance. 5.3. Contribution of the Study While it's crucial to acknowledge the noted limitations of this study, it's equally important to underscore the significant contributions it has made in advancing our comprehension of the intricate relationship between business models, specifically their integration of digital transformation, and the performance of organisations within the distinctive context of South Africa. Furthermore, the study's discoveries have laid a robust groundwork for the development of an innovative Business Model Digital Transformation Adoption Framework. This framework stands poised to enact a transformative role in how businesses function and compete in the evolving landscape of the digital age. Through the assimilation of this framework into their strategies, organisations can anticipate a notable shift in their ability not only to navigate the challenges of today's dynamic business environment but also to emerge as pioneers and frontrunners in their respective industries. This pioneering framework provides a comprehensive blueprint for businesses to astutely harness digital transformation, thereby unlocking fresh avenues for growth, refining operational efficiency, and ensuring long-term sustainability. Consequently, it holds the potential to restructure the trajectory of businesses across various sectors in South Africa and beyond, establishing a new benchmark for excellence in the digital era. 5.4. Scope for Further Research This study focused exclusively on the top 100 organisations listed on the JSE, and thus, it's crucial to acknowledge that it may not fully represent the entirety of the South African organisational landscape. Future research endeavours should aim to expand the scope by including a more diverse range of organisations, encompassing both listed and non-listed entities, and even incorporating government entities. This broader view would provide a more comprehensive understanding of the relationship between business model digital transformation adoption and overall performance in the South African context. Furthermore, it's advisable to consider the inclusion of alternative performance measurement metrics for a more multifaceted evaluation. Additionally, conducting analyses over an extended period, rather than the three years covered in this study, could yield more conclusive findings. With a larger dataset and a longer time frame, researchers may uncover deeper insights that could significantly inform the refinement and enhancement of the digital transformation framework. This also suggests that there's ample room for refining and enhancing the framework itself. As this study serves as a foundational step in understanding the dynamics of digital transformation adoption and its impact on organisational performance, future iterations of the framework can benefit from ongoing research and iterative improvements. This will ensure that the framework remains relevant and effective in guiding organisations through the complexities of digital transformation in the ever-evolving business landscape. Abbreviations AC : Activity CA : Current Assets CL : Credit Losses CLB : Current Liabilities IIRC : International Integrated Reporting Council IN : Input NI : Net Income NPA : Non-Performing Assets OC : Operating Cost OI : Operating Income OP : Output OT : Outcome T : Total TA : Total Assets TD : Total Debt TE : Total Equity TI : Total Investment TS : Total Securities Declarations Ethics Approval and Consent to Participate As the data used in this study was collected from publicly accessible online platforms, ethical clearance wasn't deemed necessary. Since the data collection didn't entail direct interaction with human participants, concerns regarding privacy, confidentiality, or informed consent didn't arise. Therefore, this study adheres to ethical principles concerning the utilisation of publicly available data and doesn't require formal ethical clearance from an institutional review board or ethics committee. Consent for Publication Not applicable. Availability of Data and Materials All data generated and analysed during this study are included in this published article [and its supplementary information files]. Competing Interests The author declares that they have no competing interests. Funding No financial support was provided for this study. The research was conducted without any external funding or financial assistance from grants, institutions, or sponsors. This absence of funding underscores the independent nature of the research endeavour, with all associated costs and resources being borne by the researcher. Despite the lack of financial backing, the study was conducted with dedication and thoroughness, ensuring rigorous methodology and analysis. Authors’ Contribution Thabe Mothabine led this study as the main researcher, assuming a crucial role in all research stages. His responsibilities encompassed conceptualisation, methodology design, data collection and analysis, result interpretation, and manuscript drafting. Thabe's commitment, expertise, and meticulousness was pivotal in achieving the study's success and maintaining its quality. Dr. Collins Achepsah Leke provided essential guidance and supervision throughout the study's duration. Serving as the supervisor, Dr. Leke contributed expertise, insights, and mentorship at each research phase. His extensive knowledge in the field, combined with his guidance, significantly bolstered the study's robustness and credibility. Dr. Leke's involvement was fundamental in steering the research direction, refining methodologies, interpreting findings, and polishing the manuscript. The collaborative efforts of Thabe Mothabine and Dr. Collins Achepsah Leke culminated in the completion of a comprehensive and rigorous study. Their combined expertise and dedication to scholarly excellence have made significant contributions to the research landscape in this domain. 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(2020)\u003c/p\u003e","description":"","filename":"3.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4309834/v1/25d3b0713da88e1cda8c40aa.png"},{"id":55206336,"identity":"7e09772a-28d6-4ee6-9dc6-db6fa4832a49","added_by":"auto","created_at":"2024-04-24 04:38:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":151487,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3.2.: Graph depicting the top 100 organisations’ capital focus (2021)\u003c/p\u003e","description":"","filename":"3.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4309834/v1/bbd02a78ae918df37a23b946.png"},{"id":55206076,"identity":"03111323-543c-477d-930f-21068238d71c","added_by":"auto","created_at":"2024-04-24 04:30:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119210,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3.3.: Graph depicting the top 100 organisations’ capital focus (2022)\u003c/p\u003e","description":"","filename":"3.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4309834/v1/f8ba7f38a45797821d99da93.png"},{"id":55205872,"identity":"60e92b29-4581-40a3-a4d7-6755d048d0e6","added_by":"auto","created_at":"2024-04-24 04:22:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":129496,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3.4.: Radar graph highlighting top 100 organisations’ capital focus (2020)\u003c/p\u003e","description":"","filename":"3.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4309834/v1/8f4e78cd9ff33a980d559304.png"},{"id":55206077,"identity":"c3e82506-d85e-4b8f-a0e5-6416c56aba15","added_by":"auto","created_at":"2024-04-24 04:30:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":129114,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3.5.: Radar graph highlighting top 100 organisations’ capital focus (2021)\u003c/p\u003e","description":"","filename":"3.5.png","url":"https://assets-eu.researchsquare.com/files/rs-4309834/v1/c1625f6477aac572b3bd0859.png"},{"id":55205873,"identity":"91e98d4e-51e2-48ab-be5a-3af7be4ff7d6","added_by":"auto","created_at":"2024-04-24 04:22:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":141724,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3.6.: Radar graph highlighting top 100 organisations’ capital focus (2022)\u003c/p\u003e","description":"","filename":"3.6.png","url":"https://assets-eu.researchsquare.com/files/rs-4309834/v1/fd8a35b0b00de59611b6e46c.png"},{"id":55206079,"identity":"b7d5ce62-c7cd-403a-a26c-bc38c5a8da8c","added_by":"auto","created_at":"2024-04-24 04:30:58","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":278042,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3.7.: The Digital Evolution Navigator Framework\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.7.png","url":"https://assets-eu.researchsquare.com/files/rs-4309834/v1/aaf2a5af5584402b2b274a6c.png"},{"id":55206802,"identity":"7ee281bd-b209-4182-b248-726b546fe248","added_by":"auto","created_at":"2024-04-24 04:47:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2135637,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4309834/v1/2f91b410-a347-4243-a5f4-2844bdabd524.pdf"},{"id":55205876,"identity":"f74ce14b-be44-448e-8936-2fce5d70a439","added_by":"auto","created_at":"2024-04-24 04:22:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12140755,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4309834/v1/20c03ca9630e59adba34c6b3.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Research Paper on the Design of a Business Model Framework for Digital Transformation Adoption\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eIn the face of the current South African economic climate, it is important to ensure that businesses are performing optimally and sustainably, as this contributes to the overall growth and development of the economy. Therefore, it was imperative to understand as much information as possible regarding the factors that led to the success and failure of various organisations listed on the JSE.\u003c/p\u003e \u003cp\u003eAccording to Cavalcanti, Oliveira, and de Oliveira Santini (2022), digital transformation is not a new imperative for corporate leaders, yet in many cases, organisations have a long way to go. What was interesting about this is that individuals rather than businesses were responsible for this transformation. This transition is being driven by the customer. Customers now need and expect timely and relevant content that relates to what they are doing at any point, whenever, in their desired format, and on the device of their choice. The course of their journey determines their strategy. Ironically, many organisations still have not adopted digital technologies in their methods of operation widely, nor have they created a culture that embraces change, experimentation, and continual learning and improvement. However, Fern\u0026aacute;ndez-Portillo, Almod\u0026oacute;var-Gonz\u0026aacute;lez, S\u0026aacute;nchez-Escobedo and Coca-P\u0026eacute;rez (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), state that if we look on the bright side, many organisations had to respond to a range of COVID-19-related changes much more quickly than they ever thought possible before the crisis, which resulted in a wide spread of organisations planning their end-to-end transformations, accelerating their digitalisation, and improving their customer interactions and internal operations by between three and four years.\u003c/p\u003e \u003cp\u003eFern\u0026aacute;ndez-Portillo et al, (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), add that amending \u0026ndash; and in some cases completely overhauling \u0026ndash; their business model has been the key to survival for many organisations during the pandemic. Unfortunately, even though some organisations amended or changed their business models, they still failed during and as a result of the pandemic. A glance at the commercial landscape, reveals that it is rife with instances of businesses that offered cutting-edge products or services, but failed to turn a profit because they were either unable to draw in enough customers or were founded on unsound economics. According to Abidi, Herradi and Sakha (2022), there is no assurance that a good product, service, or the best technology will succeed on the market. A business must also have an effective business model. In essence, this describes how businesses are \"designed\". Both new technologies and new business models have the potential to fundamentally alter the competitive environment, and potentially give the business or organisation a competitive advantage, however, the aim is to achieve sustained competitive advantage (Abidi, Herradi and Sakha, 2022).\u003c/p\u003e \u003cp\u003eThe resource-based theory states that an organisation\u0026rsquo;s sustained competitive advantage is achieved through its resources and capabilities (Barney, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). A company\u0026rsquo;s strategy is then devised based on its internal capabilities and the opportunities and threats which are identified in the external environment (Grant, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Business has changed in the face of the fourth industrial revolution (henceforth referred to as the 4IR), with clients and businesses using more technologically dependent methods to engage with one another (Teece, \u003cspan citationid=\"CR192\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Rachinger, Rauter, Muller, Vorraber and Schirgi (\u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) add that digitalisation has put pressure on companies to reflect on their current strategy and explore new business opportunities systematically and at early stages. As aforementioned, the COVID-19 pandemic has been one of the biggest influences in recent times, on how businesses operate, and essentially acted as a gauge of their adaptability. According to D\u0026ouml;hring, Hristov, Maier, Roeger, and Thum-Thysen (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the COVID-19 outbreak serves as a stark reminder that pandemics, like other incredibly uncommon catastrophes, have occurred in the past and will continue to do so in the future. Even if we are unable to stop the emergence of widespread catastrophes like a pandemic, the pandemic was a wake-up call for business leaders to have strategies and plans of action in place to mitigate, if not eliminate, the impact and effects of such catastrophes on their businesses. A study conducted by IBM (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), states that before 2020, only a small number of organisations regarded competencies in cash-flow management, workforce resiliency, corporate agility, cost management, and crisis management as significantly crucial to their operations. This has now drastically changed the nature in which businesses operate, as a result, new business models that incorporated those competencies, embraced and adopted digital transformation; built a degree of flexibility, and incorporated the people working in those organisations, emerged. However, there is still a need to create and develop a novel framework for business models that is appropriate for the South African market, places an emphasis on, and responds to, the following pertinent points where literature is still scant:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFirstly, the study needs to identify how new business models originate, and how value creation is established in various industries, by examining the numerous interactions among, for example, crowdfunding platforms, entrepreneurs, and the crowd. According to research, a lack of knowledge exists regarding the impacts that crowdfunding platforms have on value-creation activities. Understanding the collaborative and competitive dynamics that drive value creation for businesses on crowdfunding platforms would be informative.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAdditionally, it is still not apparent how agile approaches assist businesses in generating profit from digital technologies and customised services. This study seeks to explore how agile practices can be applied in traditional industries. Research has shown that companies in conventional industries need to collaborate more with other companies to innovate, which allows this study to highlight how agile techniques could generate value.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIt's also important to carefully examine the role of technologies like the Internet of Things, cloud computing, artificial intelligence, big data, and the blockchain. We may learn first-hand how value creation processes function and how they might be exploited as a source of competitive advantage by putting these technologies into action.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExamining value creation for customers while concentrating on the psychological implications is crucial. For example, with terminally ill patients entirely relying on telemedicine to contact their loved ones during the recent COVID-19 pandemic, fresh ideas and inputs have come from the healthcare industry, creating new possible business models for businesses functioning in that sector.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo maximise value, this new framework needs to also look into setting the parameters for how frequently and under what conditions business models should be altered. Businesses' intense and continual contact with the highly dynamic environment forces them to adapt and develop their business models. There is currently a shortage of research describing the boundary conditions brought about by technical advancements that influence value creation in business model innovation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFinally, it's critical to comprehend how new technologies relate to sustainability concerns. How to develop new value in the circular economy and from sectors where sustainability is important, for instance, is still a mystery. Particularly from a psychological perspective, the relationship between digital transformation and customers' pro-environmental behaviour seems to be a relatively fresh and interesting area of study (Yusliza et al., 2020).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eA prominent theory in this study is the disruptive innovation theory, developed by Christensen (1997), to explain how less expensive inventions, yet more effective than those already on the market eventually displace mainstream innovations. This prevalent position within the area examines digital transformation at both the organisational and individual levels of analysis, and it is derived from a technical and innovation management perspective. Some researchers have used the principles of disruptive innovation theory in their studies to demonstrate how technological value creation can be expedited. For instance, the case study of Kodak by Lucas and Goh (2009), highlights the importance of organisational structure and culture in generating new value when disruptive technologies are introduced in an industry. Focusing on managers' strategic choices, Osiyevskyy and Dewald (2015) contend that a leader's exploratory intentions determine whether or not they respond to ongoing disruption with experimentation.\u003c/p\u003e \u003cp\u003eBut how does the adoption of digital transformation by organisations, which is highlighted through their business models, impact or affect their overall performance? If we take into account that business models are a manifestation of a firm's adopted strategy (Casadesus-Masanell and Ricart, 2010) and that they are a demonstration of a firm's value creation and capturing process (Brink and Holmen, 2009), there appears to be a clear relationship between a firm's chosen business model and their performance (Zott and Amit, 2007). Few studies, though, have attempted to objectively establish the connection between business models and firm performance (Pucci, Nosi and Zanni, 2017).\u003c/p\u003e \u003cp\u003eAziz and Mahmood (2011) conducted a study in which they attempted to use the business model of Malaysian manufacturing SMEs to explain how well they performed. This study's primary goal was to evaluate the connection between changes in SMEs' performance and the size of the business model. According to the research, the single aspect of the company model that affects how well SMEs execute and succeed is \"skill\". A skill that is valuable, uncommon, unique, and non-substitutable could be an organisation's competitive advantage if it is held by a person or organisation. If we see skill through the IIRC's \"IR\" framework, skill can also be classified under human, intellectual, and manufactured capital (IIRC, 2011).\u003c/p\u003e \u003cp\u003eThe conceptualisation, dimensions, and measurement of an organisation's performance have been the subject of a long-running discussion in the literature (Rumelt, Schendel and Teece, 1994; Franco-Santos et al., 2007). The widely held belief regarding performance is that it is a multifaceted construct made up of organisational effectiveness, financial and business performance (Kaplan and Norton, 1996; Morgan and Strong, 2003; Simpson, Padmore and Newman, 2012). According to Santos and Brito (2012), resource-based theory dictates that a firm's performance depends on its resources, but performance can also be expanded to take into account other factors like profitability, growth, customer and employee satisfaction, social and environmental responsibility, as well as market value (Santos and Brito, 2012).\u003c/p\u003e \u003cp\u003eBusiness or organisational performance is part of an organisation's effectiveness and efficiency, which includes operational and financial results. Fatoki (2019) states that financial measures are important, however, these measures are usually lagging measures of performance, while non-financial measures are leading measures of performance that provide insight into future performance. These non-financial or subjective performance measures include employee satisfaction (employee turnover, investments in employee development and training, and organisational climate), customer satisfaction (number of complaints, repurchase rate, customer retention), environmental performance (recycling, material usage, energy consumption, pollution, and waste), and social performance (employment of minorities, contribution to social causes) (Fatoki, 2019). The study will adhere to the suggestions made by Fatoki (2019) to assess the overall performance of the top 100 JSE-listed companies.\u003c/p\u003e \u003cp\u003eTo understand, emphasise, and deduce the influence of digital transformation on business models; followed by building a new business model framework; and comprehending how these can affect the overall performance of the selected top 100 organisations on the JSE are essentially the goals of this study. These goals will be guided by a variety of research as well as other metrics and techniques. By employing a comprehensive methodology that encompasses the analysis of business models and performance, this study aims to capture valuable insights and draw meaningful comparisons among the top 100 listed organisations. These organisations were carefully selected based on their substantial asset size and exemplary shareholder returns achieved over the past three years.\u003c/p\u003e"},{"header":"2. METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Research Aims\u003c/h2\u003e\n \u003cp\u003eThis study focused on four aims, all contributing to the main objective of developing a new business model framework for digital transformation adoption and understanding the relationships between business models, digital transformation, and performance in the South African market. The aims were as follows:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMain Aim\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe primary goal of this study was to construct a unique business model framework for digital transformation adoption, specifically designed for the South African market, taking into account its distinct characteristics and dynamics.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSupporting Aim 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThis aim involved analysing the impact of digital transformation on the business models of the top 100 companies listed on the Johannesburg Stock Exchange (JSE) over three years (2020\u0026ndash;2022). The focus was to explore how digital transformation influences the structure and operation of these business models and investigate any potential correlations with their overall performance.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSupporting Aim 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThis aim seeks to determine whether certain business sectors or industries within the JSE top 100 demonstrate higher overall performance compared to others, primarily due to digital transformation. The objective is to investigate the nature of these sectors and identify possible reasons for their superior performance, shedding light on factors beyond digital transformation that contribute to success.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSupporting Aim 3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn addition to digital transformation and sector-specific factors, this aim seeks to explore other variables that may impact the overall performance of businesses listed on the JSE top 100 markets. By examining various potential factors, the aim was to provide a comprehensive understanding of the diverse influences on business performance within the South African context.\u003c/p\u003e\n \u003cp\u003eBy addressing these aims, this study aimed to provide valuable insights into the development of a business model framework and the interplay between business models, digital transformation, and performance in the South African market. It seeks to deepen the understanding of the factors that drive success in this dynamic and evolving landscape.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Research Objectives\u003c/h2\u003e\n \u003cp\u003eThe main objectives of the research were to:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eAssess and evaluate the impact of digital transformation on business models.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eEvaluate and analyse the components of each organisation\u0026apos;s business model digital transformation adoption based on the IIRC\u0026apos;s International\u0026thinsp;\u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eCompare the business model components of different organisations over three years, highlighting patterns of change using tables and graphs.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eAssess, analyse, and evaluate the overall performance of the organisations using various financial and non-financial indicators.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eDetermine whether the influence of digital transformation on business models, as per the IIRC\u0026apos;s International\u0026thinsp;\u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework, affects the organisations\u0026apos; overall performance.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eDevelop a novel framework for business models that are specifically suited for the South African market, taking into account changes in corporate strategy and current business models resulting from digitalisation.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eContribute to the existing literature by providing new insights into the influence of digitalisation on business models and performance.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Research Questions\u003c/h2\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003cp\u003e2.3.1. What influence does digital transformation have on business models and organisations\u0026apos; overall performance?\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e2.3.2 What role do business models and performance play in the overall effort to boost the South African economy?\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003cp\u003e2.3.3. What challenges and opportunities do South African organisations face as they implement digital transformation?\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e2.3.4. Is there a particular industry or business that performs better as a result of digital transformation?\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003cp\u003e2.3.5. What essential elements are necessary for constructing a novel business model framework?\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Research Methods\u003c/h2\u003e\n \u003cp\u003eThis study utilised a quantitative research methodology, which is crucial for providing in-depth insights into the impact of different conditions or events on individuals within the social world (Smith, 2018). Quantitative research relies on unbiased data and employs statistical analysis and graphical representations to offer comprehensive explanations (Creswell and Creswell, 2017). The scientific rigour of quantitative analysis enables the replication of findings by other researchers, ensuring the reliability and validity of the study\u0026apos;s results (Bryman, 2016). This aspect was particularly important in constructing a new business model framework (Kothari, 2004).\u003c/p\u003e\n \u003cp\u003eThe primary objective of the study was to develop an innovative framework for business models, aiming to foster innovation and the creation of new models (Johnson and Christensen, 2019). This process commenced with an examination of the impact of digital transformation on business models, employing various quantitative assessments to ascertain whether such transformations result in improved performance (Porter and Heppelmann, 2014). The construction of the business model framework was iterative, incorporating expanded findings and theoretical foundations to strengthen the model (Eisenhardt and Graebner, 2007). By employing a quantitative approach and rigorous analysis, the study aimed to generate valuable insights into the relationship between digital transformation, business models, and organisational performance (Eisenhardt, 1989).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Sampling Strategy\u003c/h2\u003e\n \u003cp\u003eAccording to Sidhu (2003), understanding the concept of a sample is essential in research methodology. A sample refers to a subset of participants or entities from which data is collected and analysed, aiming to represent the larger population accurately. This approach facilitates hypothesis generation and meaningful inferences about the population (Sidhu, 2003).\u003c/p\u003e\n \u003cp\u003eIn extensive population studies, using a representative sample is crucial to draw accurate conclusions about the entire population (Sidhu, 2003). By selecting a subset of entities with similar characteristics to the population, researchers can analyse the sample more efficiently while still capturing the essence of the population.\u003c/p\u003e\n \u003cp\u003eFor this study, the population of interest comprised the top 100 South African JSE-listed companies, representing various industries. All these companies are mandated to publish Annual Integrated Reports, providing comprehensive information on financial performance, governance, and sustainability (Sidhu, 2003). Leveraging these reports, the study aimed to explore the impact of digital transformation on business models and organisational performance.\u003c/p\u003e\n \u003cp\u003eThrough meticulous sampling, the research aimed to select a representative sample reflecting the diversity of sectors represented within the top 100 companies. This approach ensures meaningful conclusions and hypothesis generation, contributing to a comprehensive understanding of the South African business landscape (Sidhu, 2003).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6. Data Collection\u003c/h2\u003e\n \u003cp\u003eFor this study, data was collected from publicly available Annual Integrated Reports, accessed through the corporate websites of the top 100 listed organisations. These reports serve as comprehensive accounts of companies\u0026apos; value and performance, covering various financial and non-financial aspects impacting their capacity for value creation (Zhou, Simnett, \u0026amp; Green, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to the International Integrated Reporting Council (IIRC, 2013), Integrated Reports aim to link financial and non-financial data to provide insights into a company\u0026apos;s prospects, including factors like employee satisfaction and external social well-being.\u003c/p\u003e\n \u003cp\u003eThe availability of these reports made them an ideal data source, especially considering the Johannesburg Stock Exchange\u0026apos;s mandate for listed companies to produce them (Zhou, Simnett, \u0026amp; Green, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). By analysing these reports, the study aimed to assess the business models of the top 100 organisations, following the International Integrated Reporting Framework guidelines.\u003c/p\u003e\n \u003cp\u003eTo systematically capture and analyse the business models and performance of each organisation, a Microsoft Excel spreadsheet was utilised, aligning with the International Integrated Reporting Framework structure. Performance was measured using various financial and non-financial indicators, as recommended by Fatoki (2019).\u003c/p\u003e\n \u003cp\u003eTo ensure data integrity and accessibility, all information was electronically stored in Microsoft Excel format. This storage method allows for easy retrieval and review of data while ensuring its security. The data will be securely stored for five years post-study completion, enabling future analyses and cross-referencing.\u003c/p\u003e\n \u003cp\u003eBy leveraging Annual Integrated Reports and employing robust data storage and documentation, the study aimed to analyse business models and performance of the top 100 JSE-listed organisations. This approach facilitated exploration of the relationship between business models and organisational performance, contributing to a deeper understanding of success factors in the South African market.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7. Data Analysis\u003c/h2\u003e\n \u003cp\u003eThe analysis began with a detailed review of the sectors represented by the top 100 listed organisations, aiming to uncover common trends and patterns within these industries. This phase sought to provide context for understanding how digital transformation was unfolding within specific sectors.\u003c/p\u003e\n \u003cp\u003eNext, a diverse range of quantitative metrics was applied to gauge the level of digital transformation adoption within the business models. These metrics, sourced from Integrated Reports, were carefully chosen to offer meaningful insights into digital transformation\u0026apos;s impact on organisations.\u003c/p\u003e\n \u003cp\u003eThe analysis then delved into assessing digital transformation adoption in business models, guided by the \u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework. Organisations were scored on various components related to each capital, with these scores combined to calculate total scores. By comparing these scores over three years, changes in digital transformation adoption were identified and visually presented, aiding in developing a robust business model framework.\u003c/p\u003e\n \u003cp\u003eFollowing this, each organisation\u0026apos;s performance was evaluated using the CAMELS rating system model, with lower scores indicating superior sustainable performance. To explore the potential link between digital transformation adoption and performance, a correlation coefficient analysis was conducted in Microsoft Excel. This statistical measure sheds light on the relationship between digital transformation adoption and overall performance, providing valuable insights into their interconnectedness.\u003c/p\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e2.7.1. \u0026lt;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework\u003c/h2\u003e\n \u003cp\u003eThis study utilised the \u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework to analyse the adoption of digital transformation in the business models of the top 100 organisations listed on the Johannesburg Stock Exchange (JSE) from 2020 to 2022. Integrated reports from these organisations were the primary data source, capturing their responses to the COVID-19 pandemic, which significantly influenced digital transformation strategies during this period.\u003c/p\u003e\n \u003cp\u003eThe analysis involved a thorough comparison of the organisations\u0026apos; capitals, including financial, manufactured, intellectual, human, social, and natural capital. Each capital\u0026apos;s inputs, activities, outputs, and outcomes were examined, and ratings were assigned to assess the level of digital transformation adoption. Ratings ranged from 1 to 3, with 3 indicating comprehensive representation and 1 indicating no mention of the component.\u003c/p\u003e\n \u003cp\u003eCriteria for assigning ratings were clearly defined, ensuring consistency and transparency. Detailed breakdowns of digital transformation adoption per capital, along with corresponding ratings for each component, were provided in tables for visual representation and clarity. These tables offer insights into the level of digital transformation adoption within each capital and lay the groundwork for further analysis of its impact on organisational performance.\u003c/p\u003e\n \u003cp\u003eThe study visualised the comparison and analysis of business models\u0026apos; digital transformation adoption using tables and graphs, highlighting any notable changes or trends observed from 2020 to 2022.\u003c/p\u003e\n \u003cp\u003eTo deepen the understanding of digital transformation adoption, organisations were classified into three categories: rich, moderate, and poor, based on their analysis scores and percentages. This classification, as emphasised by Bailey (2005), helps organise objects into groups, adding meaning and structure to observed reality.\u003c/p\u003e\n \u003cp\u003eRich business models achieved high scores (124 to 186), demonstrating a comprehensive scope in digital transformation adoption. Moderate models scored between 62 to 123, showing some adoption but with less focus. Poor models scored from 0 to 61, indicating limited adoption.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2.7\u003c/span\u003e. presents these classifications and interpretations, offering a clear overview of how business models were categorised based on their digital transformation adoption, enriching insights into their approach.\u003c/p\u003e\n \u003cp\u003eSuppose a relationship exists between business model digital transformation adoption and overall performance. In that case, it is reasonable to anticipate that business models with extensive and rich digital transformation adoption would potentially achieve higher levels of overall sustainable performance. As noted earlier, the assessment of their overall performance was conducted using the CAMELS rating system model.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e2.7.2. CAMELS rating system model\u003c/h2\u003e\n \u003cp\u003eThe CAMELS rating system model was utilised to gauge the overall performance of organisations due to its proven simplicity and reliability across various sectors. This model delves into six crucial components of each organisation: capital adequacy, asset quality, management efficiency, earnings ability, liquidity, and sensitivity to market risk. Each component is thoroughly assessed using specific indicators and ratio ratings, ranging from 1 to 5. Table \u003cspan class=\"InternalRef\"\u003e2.8\u003c/span\u003e outlines the indicators and corresponding ratio rating criteria used to evaluate each component of the CAMELS rating system model.\u003c/p\u003e\n \u003cp\u003eAccording to Wachira (2010), the CAMELS framework evaluates each of its six components by considering factors like institution size, business nature, activity complexity, and risk profile. This assessment assigns ratings on a scale of 1 to 5, reflecting the institution\u0026apos;s performance. These ratings align with those established by the Federal Deposit Insurance Corporation (2014). In this system, a rating of 1 signifies strong performance and robust risk management, while a rating of 5 indicates weak performance and inadequate risk management. Table \u003cspan class=\"InternalRef\"\u003e2.9\u003c/span\u003e offers a detailed breakdown of each composite rating, including an analysis and interpretation for each performance level.\u003c/p\u003e\n \u003cp\u003eIn this context, the data underwent a thorough examination, focusing on the specific ratios within the six components: capital adequacy, asset quality, management efficiency, earnings, liquidity, and sensitivity to market risk. Each component received a rating, which was then combined to derive the overall composite rating. Notably, the organisation with the lowest rating signifies the best-performing entity, while the organisation with the highest score indicates the poorest performance. The outcomes and findings of the evaluation are quantifiable, and to enhance clarity and understanding, they were visually presented through graphical representations.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e2.7.3. Correlation Coefficient\u003c/h2\u003e\n \u003cp\u003eThe correlation coefficient serves as a statistical tool to measure the strength and direction of the relationship between two variables, such as business model digital transformation adoption and overall performance. It ranges from \u0026minus;\u0026thinsp;1.0 to 1.0, where \u0026minus;\u0026thinsp;1.0 indicates a perfect negative correlation, 1.0 signifies a perfect positive correlation, and 0 suggests no correlation.\u003c/p\u003e\n \u003cp\u003eInterpreting these values, correlations between 0 and 0.3 (or -0.3) denote a weak positive (negative) correlation, while correlations between 0.3 and 0.6 (or -0.3 and \u0026minus;\u0026thinsp;0.6) indicate a moderate positive (negative) correlation. Values between 0.7 and 1.0 (or -0.7 and \u0026minus;\u0026thinsp;1.0) represent a strong positive (negative) correlation.\u003c/p\u003e\n \u003cp\u003eTo ascertain the presence or absence of a relationship between business model digital transformation adoption and overall performance, a correlation coefficient analysis was conducted. This test assesses the statistical significance of the observed linear relationship in the sample data, determining whether there is enough evidence to conclude a relationship between the variables (Bujang and Baharum, 2017).\u003c/p\u003e\n \u003cp\u003eUsing the collected scores for each year as data inputs, the correlation coefficient analysis was executed using Microsoft Excel, applying appropriate statistical calculations and formulas.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8. Constructing a Novel Business Model Framework\u003c/h2\u003e\n \u003cp\u003eThe results of the analysis of business models and overall performance were crucial in shaping the development of this proposed business model framework. By using data analytics, the study gained insights into customer behaviour, market trends, and operational efficiency, aiding informed decision-making and strategic planning.\u003c/p\u003e\n \u003cp\u003eThe incorporation of data analytics enabled the recognition and interpretation of market trends, empowering organisations to react proactively to changes and seize emerging opportunities. By scrutinising extensive datasets, the study uncovered valuable market insights, such as demand patterns, competitor strategies, and emerging customer segments. Equipped with these insights, businesses can now make data-driven decisions to refine their value propositions, optimise marketing efforts, and gain a competitive edge.\u003c/p\u003e\n \u003cp\u003eThe newly introduced business model framework aimed to fill significant research gaps identified in the literature, advancing the understanding of value creation across industries. These key areas of investigation included:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEmergence of New Business Models and Value Creation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe framework explored the interactions between crowdfunding platforms, entrepreneurs, and the crowd to unearth novel approaches to value creation and grasp the impact of crowdfunding on entrepreneurship and innovation.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAgile Methods in Traditional Industries\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eInvestigating the applicability of agile principles beyond the technology sector, the framework sought to uncover benefits, challenges, and best practices for implementing agile methods in industries like manufacturing, healthcare, and transportation.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePsychological Impact of Emerging Technologies on Customer Value Creation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBy delving into how emerging technologies influenced customer perceptions and experiences, the framework aimed to inform the design and implementation of innovative business models.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAltering Business Models and Addressing Sustainability Concerns\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eExploring how businesses adapted their models in response to market changes, technological advancements, and sustainability challenges, the framework aimed to identify strategies for creating value while addressing environmental, social, and governance factors.\u003c/p\u003e\n \u003cp\u003eOverall, the newly developed business model framework filled existing research gaps, offering valuable guidance to organisations in developing innovative and sustainable business models while enhancing the understanding of value creation across industries.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e2.9. Ethical Consideration\u003c/h2\u003e\n \u003cp\u003eBecause the data for this study was collected from publicly accessible online platforms, there were no ethical clearance requirements. Since the data did not involve direct interaction with human participants, there were no privacy or confidentiality concerns. Therefore, the study follows ethical principles regarding the use of publicly available data and does not necessitate formal clearance from an institutional review board or ethics committee.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Overview of the sectors of each organisation\u003c/h2\u003e\n \u003cp\u003eThe diverse sectors within South Africa\u0026apos;s top 100 listed organisations play vital roles in the country\u0026apos;s economy, contributing significantly to economic growth, job creation, and investment attraction. From mining and financial services to real estate, retail, technology, industrial conglomerates, healthcare, energy, chemicals, construction, engineering, and food products, each sector brings its unique strengths to the business landscape. This variety underscores South Africa\u0026apos;s potential for ongoing development and innovation across multiple industries. The representation of these sectors in the top 100 organisations reflects the nation\u0026apos;s diverse economic landscape and its capacity for sustained growth. Tables \u003cspan class=\"InternalRef\"\u003e3.1\u003c/span\u003e., 3.2., and 3.3. provide detailed insights into the composition of these sectors within the top 100 listed organisations.\u003c/p\u003e\n \u003cp\u003eUpon analysing the top 100 sectors, it became evident that certain sectors were more prevalent than others. The mining sector emerged as the most prominent, with 14 companies, showcasing South Africa\u0026apos;s rich mineral resources and its crucial role in the economy. It encompasses the extraction of various minerals, contributing significantly to export earnings, job creation, and government revenue.\u003c/p\u003e\n \u003cp\u003eFollowing closely was the financial services sector, with 12 companies, encompassing banks, insurance firms, and asset management entities. This sector facilitated economic activities, providing essential financial products and services to individuals, businesses, and the government, supporting economic growth and stability.\u003c/p\u003e\n \u003cp\u003eThe presence of 11 REITs underscored the significance of the real estate sector, offering income-generating properties and investment opportunities. The technology sector, with 4 companies, drove innovation and productivity, while the healthcare services sector, also with 4 companies, promoted public health and contributed to job creation and research.\u003c/p\u003e\n \u003cp\u003eLastly, the retail - apparel sector, also with 4 companies, met consumer demand for clothing and accessories, fostering consumer spending and economic activity. These sectors collectively played vital roles in South Africa\u0026apos;s economy, driving growth, employment, investment, and meeting consumer needs, thus informing informed decisions for sustainable economic development and prosperity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Business Models (\u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework)\u003c/h2\u003e\n \u003cp\u003eIn this study, the assessment of digital transformation adoption in the business models of the top 100 organisations from 2020 to 2022 was conducted through an analysis of their integrated reports using the consolidated\u0026thinsp;\u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework. The evaluation focused on examining each organisation\u0026apos;s capital, considering inputs, activities, outputs, and outcomes for each respective year. Component ratings, ranging from one (1) to three (3), were assigned, with three representing the highest rating. These ratings were then aggregated, and percentages were calculated annually for each organisation, as shown in Tables \u003cspan class=\"InternalRef\"\u003e3.4\u003c/span\u003e to 3.13.\u003c/p\u003e\n \u003cp\u003eThe discussion of findings from Tables \u003cspan class=\"InternalRef\"\u003e3.4\u003c/span\u003e to 3.13 highlights key observations and trends, with tables and graphs providing a visual representation of changes in each capital over the three-year period. Additionally, based on the derived ratings and percentages, the digital transformation adoption of each organisation\u0026apos;s business model was classified as poor, moderate, or rich, as depicted in Tables \u003cspan class=\"InternalRef\"\u003e3.4\u003c/span\u003e, 3.5, and 3.6.\u003c/p\u003e\n \u003cp\u003eThe analysis of integrated reports, ratings, and percentages enables an evaluation of the level of digital transformation adoption and the overall strength of the business models of the top 100 organisations. It offers insights into areas for improvement and identifies organisations that have effectively embraced digital transformation in their operations.\u003c/p\u003e\n \u003cp\u003eThe tables provided offer a detailed examination of digital transformation adoption within the business models of the top 100 organisations from 2020 to 2022. Through an assessment of various aspects of each organisation\u0026apos;s capital - including inputs, activities, outputs, and outcomes - a comprehensive analysis was conducted to measure the extent of digital transformation integration.\u003c/p\u003e\n \u003cp\u003eIt\u0026apos;s important to note that the key findings presented in the tables stem from the ratings assigned to each component within the capitals, which are further elaborated upon in the annexure section. These ratings act as indicators of the level of incorporation and utilisation of digital technologies within the organisations\u0026apos; business models, with higher ratings indicating more extensive integration.\u003c/p\u003e\n \u003cp\u003eThe in-depth analysis provided by the tables offers valuable insights into the progress and adoption of digital transformation among the top 100 organisations. Notably, it showcases significant advancements made in leveraging digital technologies to drive organisational growth, enhance operational efficiency, and elevate the overall customer experience. These findings are instrumental in deepening our understanding of the impact of digital transformation on business models and provide a solid basis for further research and strategic decision-making within organisations aiming to effectively embrace digital transformation.\u003c/p\u003e\n \u003cp\u003eTo visually present the data, bar graphs and radar graphs were utilised as primary tools due to their effectiveness in conveying different types of information and facilitating comparisons across various data points. Bar graphs are well-suited for illustrating and comparing discrete datasets, highlighting relationships and variations between different categories or groups. On the other hand, radar graphs, also known as spider or star plots, excel in displaying multivariate data, presenting multiple variables on a common scale to identify patterns and trends in complex datasets.\u003c/p\u003e\n \u003cp\u003eBy incorporating both bar graphs and radar graphs, this study aims to provide a comprehensive and visually engaging representation of the data. Bar graphs enable clear comparisons of individual data points, while radar graphs offer a holistic view of the relationships between multiple variables. This combination of visualisations enhances understanding and facilitates the identification of insights that may not be immediately apparent from tabular data alone. The following sections present these bar graphs and radar graphs.\u003c/p\u003e\n \u003cp\u003eFollowing a thorough analysis of the provided information and the accompanying graphs, it was imperative to conduct a meticulous examination of each capital individually to gain a comprehensive understanding of their significance and impact. By focusing on inputs, activities, outputs, and outcomes, key insights were gleaned, shedding light on how each capital contributed to the overall adoption and success of digital transformation within the top 100 JSE-listed organisations.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFinancial Capital\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOver the years 2020 to 2022, organisations consistently prioritised financial capital due to its pivotal role in their operations and growth. It facilitated day-to-day operations, investments, expansion, and risk management, ensuring liquidity and attracting investors. Notably, the mining and financial services sectors placed significant emphasis on financial capital, recognising its importance for their capital-intensive operations. While organisations excelled in inputs and activities, there was room for improvement in showcasing outputs, with a noticeable improvement observed in 2021.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eManufactured Capital\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eManufactured capital, comprising physical assets and infrastructure, played a vital role in enabling digital transformation initiatives. However, organisations faced challenges in effectively highlighting their activities and outputs in this regard, suggesting a need for better communication of tangible outcomes derived from manufactured capital investments.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIntellectual Capital\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIntellectual capital emerged as a key focus area, particularly in sectors reliant on innovation and intellectual property. Technology and healthcare sectors demonstrated a strong commitment to leveraging intellectual capital to drive competitiveness and innovation.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHuman Capital\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHuman capital, encompassing employee knowledge and skills, was crucial for innovation and success. While the technology and healthcare sectors prioritised human capital, there was a general need for organisations to recognise and invest in their workforce to drive long-term success.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSocial Capital\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSocial capital, centred on relationships and collaboration, was often overlooked despite its potential to enhance teamwork and resilience. Organisations faced challenges in quantifying the value of social capital but recognised its importance for fostering collaboration and innovation.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNatural Capital\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNatural capital, including natural resources and ecosystems, gained prominence in digital transformation efforts, driven by sustainability concerns. Organisations recognised the interdependencies between digital transformation and natural capital, aiming to minimise environmental impacts and promote resource efficiency.\u003c/p\u003e\n \u003cp\u003eThe comprehensive analysis of each capital provided valuable insights into the adoption of digital transformation and its impact on the overall business models of the top 100 JSE-listed organisations. The detailed assessment facilitated the classification of business models as \u0026apos;rich\u0026apos;, \u0026apos;moderate\u0026apos;, or \u0026apos;poor\u0026apos; based on their digital transformation adoption, offering critical insights for stakeholders and decision-makers. These findings serve as a roadmap for organisations seeking to navigate the digital landscape and drive sustained growth and innovation.\u003c/p\u003e\n \u003cp\u003eIn essence, Tables \u003cspan class=\"InternalRef\"\u003e3.14\u003c/span\u003e, 3.15, and 3.16 provided compelling evidence that none of the organisations\u0026apos; business models were classified as \u0026apos;poor\u0026apos; in terms of their digital transformation adoption from 2020 to 2022. Instead, every organisation received a consistent classification of \u0026apos;rich\u0026apos; throughout this period, indicating a commendable level of proficiency and effectiveness in integrating digital technologies into their operational frameworks. Moreover, all organisations exhibited noticeable progress in their digital transformation adoption over the three-year span.\u003c/p\u003e\n \u003cp\u003eThis unanimous affirmation of the \u0026apos;rich\u0026apos; classification underscores the dedication of these organisations to embracing digital technologies and adapting their business processes accordingly. The sustained improvement observed from 2020 to 2022 reflects their commitment to staying ahead of evolving digital trends and optimising their operations in a digital-centric landscape.\u003c/p\u003e\n \u003cp\u003eIt was intriguing to explore whether organisations with higher scores and percentages in Digital Transformation Adoption outperformed their counterparts. To assess this, the CAMELS rating system model was employed, evaluating various aspects of organisational performance such as Capital Adequacy, Asset Quality, Management Capability, Earnings Stability, Liquidity Position, and Sensitivity to Market Risk.\u003c/p\u003e\n \u003cp\u003eBy combining insights from Digital Transformation Adoption scores and the CAMELS rating system, a comprehensive perspective was provided on how a proactive approach to digital transformation could impact overall organisational performance and resilience in a dynamic business environment. This analysis serves as a valuable resource for decision-makers and stakeholders navigating the intersection of business strategies and technology integration.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Performance (CAMELS Framework)\u003c/h2\u003e\n \u003cp\u003eUpon careful examination, it became evident that organisations listed on the JSE employed a tailored set of metrics and strategic priorities to evaluate their performance. While financial performance indicators played a central role in this assessment, it was essential to recognise their potential limitations in providing a comprehensive representation of an organisation\u0026apos;s overall effectiveness. To address this, non-financial performance indicators, largely sourced from surveys within the context of this study, were also considered. However, it was imperative to acknowledge that survey-based data could be susceptible to biases and inaccuracies due to possible misrepresentation by respondents (Smith, 2017).\u003c/p\u003e\n \u003cp\u003eNumerous studies in organisational research have highlighted the potential constraints of survey data, underscoring the need for caution when interpreting findings derived from this methodology (Jones et al., 2018; Wang and Ahmed, 2018). This underscores the significance of exploring alternative approaches to performance evaluation.\u003c/p\u003e\n \u003cp\u003eIn light of these considerations, this study chose to primarily focus on appraising financial performance. This was achieved through the adoption of the CAMELS rating system model. The CAMELS framework, widely acknowledged and utilised in financial analysis, encompasses the assessment of Capital Adequacy, Asset Quality, Management Capability, Earnings Stability, Liquidity Position, and Sensitivity to Market Risk (Benston et al., 2016). This approach was selected for its established credibility and its ability to provide a comprehensive evaluation of financial performance, aligning seamlessly with the specific objectives of this study.\u003c/p\u003e\n \u003cp\u003eWhile acknowledging the value of non-financial indicators, this research prioritised the evaluation of financial performance, employing the highly regarded CAMELS rating system model. This decision was based on the need for a robust and standardised methodology that resonated with the study\u0026apos;s objectives and ensured reliable insights into organisational performance.\u003c/p\u003e\n \u003cp\u003eHowever, as previously emphasised, the primary sources of data for this assessment were the annual integrated reports and financial statements of each organisation. These documents provided the essential financial data, which was subsequently analysed using the CAMELS rating system model. This rigorous procedure enabled a thorough evaluation of these organisations\u0026apos; performance across the three years from 2020 to 2022.\u003c/p\u003e\n \u003cp\u003eThe assessments of performance were expressed through a series of financial ratios, meticulously computed and outlined in Tables \u003cspan class=\"InternalRef\"\u003e3.17\u003c/span\u003e, 3.18 and 3.19. These ratios served as crucial indicators, offering insights into various facets of the organisations\u0026apos; financial well-being and stability.\u003c/p\u003e\n \u003cp\u003eTaking the analysis to a deeper level, Tables \u003cspan class=\"InternalRef\"\u003e3.20\u003c/span\u003e, 3.21, and 3.22 undertook a meticulous examination. In this segment, each financial ratio received a composite rating based on the criteria outlined by the CAMELS rating system model. This model categorises ratings on a scale from 1 to 5, where 1 represents the highest level of performance or strength, while 5 indicates the weakest performance.\u003c/p\u003e\n \u003cp\u003eSubsequently, these composite ratings were aggregated, resulting in the calculation of a total component score for each organisation. This score provided a comprehensive evaluation of their performance, presenting a unified perspective on their strengths and areas requiring enhancement across the various dimensions evaluated.\u003c/p\u003e\n \u003cp\u003eThis meticulous process not only generated an extensive assessment of organisational performance but also enabled insightful comparisons among the entities under examination. It established a foundation for robust insights into the effectiveness and resilience of these organisations in navigating the intricate and ever-evolving business landscape.\u003c/p\u003e\n \u003cp\u003eAfter acquiring the total component scores for each organisation annually, a further analysis was carried out to determine their overall performance rating. Following the methodology of the CAMELS rating system model, the component scores were divided by six (6), resulting in the calculation of the CAMELS composite rating for each organisation.\u003c/p\u003e\n \u003cp\u003eThe primary objective of the CAMELS composite rating is to assess and classify the performance of each organisation based on their scores. It provides a comprehensive measure that considers various aspects of the organisation\u0026apos;s operations and risk management.\u003c/p\u003e\n \u003cp\u003eIn this rating system, the lowest composite score indicates the best-performing organisation, whereas the highest composite score denotes the worst-performing organisation. By assigning numerical values to each organisation\u0026apos;s performance, the CAMELS rating system facilitates clear comparison and evaluation of their relative strengths and weaknesses.\u003c/p\u003e\n \u003cp\u003eTo visually represent the CAMELS composite ratings, Tables \u003cspan class=\"InternalRef\"\u003e3.23\u003c/span\u003e, 3.24, and 3.25 present the scores for each organisation in a concise manner for each year. These tables offer a succinct overview of the performance classification of each organisation, aiding in understanding their relative standings within the industry.\u003c/p\u003e\n \u003cp\u003eThe evaluation of the top 100 JSE-listed organisations from 2020 to 2022, using the CAMELS rating system, consistently revealed that most entities achieved \u0026quot;Adequate\u0026quot; ratings. These ratings signify satisfactory performance in operational and risk management aspects, indicating effective navigation of their industries and stability maintenance.\u003c/p\u003e\n \u003cp\u003eHowever, attention was drawn to instances where organisations received \u0026quot;Fair\u0026quot; ratings, reflecting various internal and external factors. External influences like economic downturns, regulatory shifts, and market fluctuations, along with internal factors such as management changes and operational inefficiencies, could contribute to these ratings.\u003c/p\u003e\n \u003cp\u003eNotably, the COVID-19 pandemic significantly impacted organisational performance during this period, particularly affecting industries like travel, tourism, and retail. While some organisations received \u0026quot;Fair\u0026quot; ratings due to pandemic-related challenges, the majority maintained adequate CAMELS ratings, suggesting resilience and preparedness for future adversities.\u003c/p\u003e\n \u003cp\u003eConsidering the critical role of digital transformation in contemporary business environments, the study aimed to explore its relationship with organisational performance. By assessing digital transformation adoption in business models of the top 100 JSE-listed organisations, the researcher sought to discern whether such adoption correlated with improved performance over three years.\u003c/p\u003e\n \u003cp\u003eEmploying correlation coefficient analysis, the study aimed to provide a rigorous assessment of the link between digital transformation adoption and overall performance. This statistical approach allowed for the examination of the strength and direction of any potential relationship between these variables, shedding light on the impact of digital transformation on organisational success in a rapidly evolving digital landscape.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Correlation Coefficient\u003c/h2\u003e\n \u003cp\u003eIn this research, the correlation coefficient served as a tool to analyse the connection between digital transformation adoption in business models and overall performance. Using Microsoft Excel, the researcher computed the correlation coefficient by comparing digital transformation adoption scores with performance component scores for each organisation in the sample.\u003c/p\u003e\n \u003cp\u003eThe correlation coefficient analysis aimed to determine both the strength and direction of this relationship. A positive correlation coefficient nearing\u0026thinsp;+\u0026thinsp;1 indicates a robust positive relationship, suggesting that organisations integrating digital transformation into their business models tend to exhibit higher overall performance. Conversely, a negative correlation coefficient approaching \u0026minus;\u0026thinsp;1 signifies a strong negative relationship, indicating that organisations embracing digital transformation may experience lower overall performance.\u003c/p\u003e\n \u003cp\u003eA correlation coefficient near zero suggests a weak or negligible relationship between the variables. Tables 3.26, 3.27, and 3.28 in the study contain the correlation coefficient data for each year, offering a comprehensive insight into the correlation between digital transformation adoption and performance across the specified period.\u003c/p\u003e\n \u003cp\u003eBased on the correlation coefficient results presented in the analysis of the relationship between digital transformation adoption in business models and overall performance, weak associations between the variables emerge. Although these connections lack statistical significance, it\u0026apos;s crucial to contextualise them within existing research on business models and firm performance.\u003c/p\u003e\n \u003cp\u003eA significant study by Zott and Amit (2007) underscored the pivotal role of business models in shaping firm success. Their findings suggested that innovative, well-aligned business models can positively impact overall performance. While the correlation coefficients derived from our analysis indicate weak relationships, they should be viewed as preliminary evidence, considering the extensive body of literature available.\u003c/p\u003e\n \u003cp\u003eFor instance, a correlation coefficient of \u0026quot;0.072437077\u0026quot; implies a slight positive relationship, suggesting a modest tendency for variables to fluctuate in tandem. While this effect may be subtle, it resonates with broader research highlighting the potential of digital transformation to enhance business models and, consequently, firm performance.\u003c/p\u003e\n \u003cp\u003eFurthermore, correlation coefficients of \u0026quot;-0.154284707\u0026quot; and \u0026quot;-0.146185347\u0026quot; reveal weak negative relationships, indicating a slight tendency for one variable to decrease as the other increases. Though not statistically significant, these findings prompt intriguing inquiries into factors influencing firm performance amid the digital transformation.\u003c/p\u003e\n \u003cp\u003eIn summary, the correlation coefficients suggest weak ties between digital transformation adoption in business models and overall performance. While further investigation is warranted to validate these relationships, these initial insights echo existing literature emphasising the benefits of innovative, strategically aligned business models. Therefore, they serve as encouraging prompts for continued exploration.\u003c/p\u003e\n \u003cp\u003eLeveraging these preliminary findings, the analysis of business models and overall performance laid the groundwork for a novel business model framework. This framework, informed by insights from correlation coefficients and existing literature, aimed to furnish organisations with a holistic roadmap for harnessing digital transformation to optimise performance.\u003c/p\u003e\n \u003cp\u003eFundamentally, this framework recognises the imperative of aligning business models with the swiftly evolving digital landscape to seize opportunities and mitigate risks effectively. It underscores the need for organisations to adopt a comprehensive approach, integrating digital technologies, nurturing innovation, and adapting value propositions to meet evolving customer and market demands.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Developing Novel Business Model Framework\u003c/h2\u003e\n \u003cp\u003eIn response to the growing importance of digital transformation in the competitive landscape of South Africa, the \u0026quot;Digital Evolution Navigator\u0026quot; has been introduced as a bespoke framework to guide organisations through their digital transformation journey. This innovative framework is informed by comprehensive analyses of the Integrated Reporting Framework applied to the top 100 JSE-listed organisations and the CAMELS model, which evaluated organisational performance and explored the relationship between business models and performance.\u003c/p\u003e\n \u003cp\u003eTo strengthen the framework\u0026apos;s credibility and depth, a wide range of literature was consulted, incorporating the latest insights and best practices from academic research, industry reports, and valuable case studies. Theories also played a pivotal role in shaping and informing the development of this business model framework for digital transformation adoption. These theories contributed unique insights into the decision-making process of individuals and organisations regarding the adoption of new technologies and transformative practices. Here\u0026apos;s how each theory specifically aided in the development of this framework:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eIn the case of \u003cstrong\u003eResource Based Theory\u003c/strong\u003e (RBT) (Barney, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e), it focused on the internal resources and capabilities of an organisation. In the context of digital transformation, it helped identify and leverage existing resources that could be employed to facilitate the adoption process. This theory informed the framework by emphasising the need to align digital initiatives with an organisation\u0026apos;s existing strengths.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eRegarding \u003cstrong\u003eDiffusion Theory of Innovation\u003c/strong\u003e (DOI) (Rogers, 1962), explored how new ideas or innovations spread within a social system. It identified key factors influencing the adoption process, such as relative advantage, compatibility, complexity, trialability, and observability. This theory informed the framework by highlighting the importance of addressing these factors to encourage the widespread adoption of digital transformation initiatives.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe \u003cstrong\u003eTheory of Planned Behaviour\u003c/strong\u003e (TPB) (Ajzen, 2011) focused on individual attitudes, subjective norms, and perceived behavioural control as predictors of behavioural intentions. In the context of digital transformation, TPB informed the framework by emphasising the importance of addressing stakeholders\u0026apos; attitudes and perceptions towards adopting digital technologies.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eRational Choice Theory\u003c/strong\u003e (RCT) (Fishbein and Ajzen, 1975) posited that individuals make rational decisions based on a cost-benefit analysis. In the context of digital transformation, this theory highlighted the need to demonstrate the tangible benefits and advantages of adopting digital technologies. It informed the framework by emphasising the importance of showcasing clear returns on investment.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eSocial Cognitive Theory\u003c/strong\u003e (SCT) (Bandura, \u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e) emphasises the role of social factors and observational learning in shaping behaviour. It highlighted the influence of social networks, role models, and observational experiences on adoption decisions. This theory informed the framework by underlining the importance of creating a supportive and collaborative organisational culture conducive to digital transformation.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eUnified Theory of Acceptance and Use of Technology\u003c/strong\u003e (UTAUT) (Venkatesh et al., \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e) integrated various factors that influenced technology acceptance, including performance expectancy, effort expectancy, social influence, and facilitating conditions. In the context of digital transformation, UTAUT informed the framework by providing a comprehensive model to understand and address the key determinants of technology adoption.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eBy synthesising insights from these theories, the Digital Evolution Navigator offers a comprehensive approach to understanding and driving successful digital transformation adoption. Its objective is to become a valuable tool for organisations navigating the complexities of the digital age, facilitating the development of innovative business models that harness digital technologies for value creation, performance enhancement, and sustainable growth.\u003c/p\u003e\n \u003cp\u003eWhile initially tailored for the South African context, the framework\u0026apos;s adaptability makes it suitable for global application. The following section provides a detailed explanation of the components and functionalities of the Digital Evolution Navigator.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTHE \u0026quot;DIGITAL EVOLUTION NAVIGATOR\u0026quot; FRAMEWORK\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe \u0026quot;Digital Evolution Navigator,\u0026quot; a framework for Digital Transformation Business Models, serves as a significant and indispensable tool for organisations endeavouring to navigate the intricacies of the digital era. It methodically tackles ten (10) vital components, referred to as elements, to ensure a comprehensive approach to digital transformation. By synthesising these elements, organisations possess the capability to adeptly devise pioneering business models, leveraging digital technologies to generate value, augment operational efficiency, and maintain sustained long-term growth.\u003c/p\u003e\n \u003cp\u003e1. \u0026nbsp; \u0026nbsp;New business models and value creation: This element explores the importance of leveraging new business models and value creation approaches to thrive in the digital age. It includes understanding the role of crowdfunding as an alternative source of financing and value generation. Additionally, it focuses on developing novel approaches to value creation within the context of crowdfunding and exploring the role of crowdfunding in entrepreneurship and innovation.\u003c/p\u003e\n \u003cp\u003e2. \u0026nbsp; \u0026nbsp;Agile methods in traditional industries: The framework investigates the applicability of agile principles and methodologies beyond the technology sector. It identifies potential benefits, challenges, and best practices for implementing agile methods in traditional industries such as manufacturing, healthcare, transportation, and more. By adopting agile methods, organisations can enhance performance, foster innovation, and adapt to rapidly changing market dynamics.\u003c/p\u003e\n \u003cp\u003e3. \u0026nbsp; \u0026nbsp;The psychological impact of emerging technologies on customer value creation: This element acknowledges the impact of emerging technologies, including IoT, cloud computing, AI, big data, and blockchain, on customer perceptions, behaviours, and experiences. It delves into the psychological factors that drive customer value creation in the context of these technologies. By understanding these influences, organisations can design customer-centric business models that align with emerging technology trends and meet customer needs and expectations.\u003c/p\u003e\n \u003cp\u003e4. \u0026nbsp; \u0026nbsp;Altering business models to address sustainability concerns: The framework recognises the importance of altering business models to address sustainability concerns, such as climate change and social inequality. It explores strategies and frameworks for creating value while considering environmental, social, and governance factors. By incorporating sustainability into their business models, organizations can contribute to a more sustainable and inclusive future while ensuring long-term success.\u003c/p\u003e\n \u003cp\u003e5. \u0026nbsp; \u0026nbsp;Ecosystem collaboration: The framework emphasises the significance of collaboration within a digital business ecosystem. It explores strategies for identifying and forming partnerships with external stakeholders, such as customers, suppliers, and startups. Leveraging these ecosystems can create synergies, drive innovation, and enhance digital transformation efforts.\u003c/p\u003e\n \u003cp\u003e6. \u0026nbsp; \u0026nbsp;Data-driven decision-making: In the digital era, data plays a crucial role in organisational success. The framework highlights the importance of adopting data-driven decision-making processes. It outlines strategies for collecting, analysing, and leveraging data to gain insights, make informed decisions, and drive innovation. It also addresses the ethical considerations and privacy implications associated with data use.\u003c/p\u003e\n \u003cp\u003e7. \u0026nbsp; \u0026nbsp;Talent management and upskilling: To effectively embrace digital transformation, organisations must focus on talent management and upskilling initiatives. The framework emphasizes the importance of acquiring employees with digital competencies and explores strategies for attracting, retaining, and developing digital talent within the organisation.\u003c/p\u003e\n \u003cp\u003e8. \u0026nbsp; \u0026nbsp;Customer-centricity: Acknowledging the significance of the customer experience, the framework encourages organisations to adopt a customer-centric approach. It delves into strategies for understanding customer needs, preferences, and expectations in the digital age. By leveraging digital technologies, organisations can deliver personalised and seamless customer experiences, fostering loyalty and driving growth.\u003c/p\u003e\n \u003cp\u003e9. \u0026nbsp; \u0026nbsp;Ethical and responsible digital practices: As organisations navigate digital transformation, ethical and responsible practices are vital. The framework examines the ethical implications of digital technologies, such as AI and automation. It emphasises the need for organisations to embed ethical considerations into their digital transformation strategies, ensuring transparency, fairness, and accountability.\u003c/p\u003e\n \u003cp\u003e10. \u0026nbsp; \u0026nbsp;Innovation and experimentation: Innovation serves as a key driver of digital transformation. The framework explores strategies for fostering a culture of innovation within organisations. It encourages experimentation and risk-taking to drive continuous improvement and disruptive innovation. Furthermore, it covers approaches to piloting and scaling innovative initiatives.\u003c/p\u003e\n \u003cp\u003eBy integrating these elements, organisations gain a comprehensive roadmap for their digital transformation journeys. Whether it involves understanding the impact of emerging technologies, exploring new approaches to value creation, addressing sustainability concerns, or focusing on collaboration and talent development, this framework provides organisations with a strategic advantage in navigating the complexities of the digital landscape.\u003c/p\u003e\n \u003cp\u003eAs we delve further into the intricate interplay of these elements, it becomes clear that their synergistic dynamics are pivotal in driving the transformative process. This in-depth scrutiny illuminates the subtle interrelationships and dependencies that underpin the smooth operation of the entire framework. Such a comprehensive understanding acts as a catalyst for fine-tuning strategies and enhancing the efficiency of each element within the broader context of the digital evolution journey. Nevertheless, let\u0026apos;s embark on a more comprehensive investigation of how these foundational elements interconnect. The framework adheres to an \u003cstrong\u003e8-step process\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCommence by establishing the organisation\u0026apos;s digital transformation goal or objective at the core of the framework, signifying the desired outcome of the transformation process.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eExtend into the ten primary elements of the framework, including New business models and value creation, Agile methods in traditional industries, Psychological impact of emerging technologies, Altering business models for sustainability, Ecosystem collaboration, Data-driven decision-making, Talent management and upskilling, Customer-centricity, Ethical and responsible digital practices, and Innovation and experimentation.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eSeamlessly transition to the first element, New business models and value creation. Here, explore various approaches and models for creating value, including innovative concepts like crowdfunding. Understand crowdfunding\u0026apos;s role as an alternative source of financing and value generation. Develop inventive strategies for value creation within the realm of crowdfunding, always keeping customer needs and expectations at the forefront.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eProgress organically to the subsequent element, Agile methods in traditional industries. Investigate how agile principles can be applied beyond the technology sector. Identify potential benefits, challenges, and best practices for implementing agile methods in traditional industries. These methods have the potential to enhance performance, stimulate innovation, and enable organisations to adapt to ever-evolving market dynamics.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 5\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eShift focus smoothly to the Psychological impact of emerging technologies element. Delve into how emerging technologies like IoT, cloud computing, AI, big data, and blockchain influence customer perceptions, behaviours, and experiences. Grasp the psychological factors that drive customer value creation within this context. Craft customer-centric business models that leverage these technologies to effectively meet customer needs and expectations.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 6\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eProgress organically to the Altering business models for sustainability element. Here, explore strategies and frameworks for adapting business models to address critical sustainability concerns such as climate change and social inequality. These adaptations empower organisations to create value while taking into account environmental, social, and governance factors, thereby contributing to a sustainable and inclusive future.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 7\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eIntegrate additional elements such as Ecosystem collaboration, Data-driven decision-making, Talent management and upskilling, Customer-centricity, Ethical and responsible digital practices, and Innovation and experimentation. These elements intersect with the core components, enriching the digital transformation journey.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 8\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eThroughout this transformative journey, organisations engage in collaborative efforts within digital business ecosystems to create synergies, stimulate innovation, and bolster their transformation endeavours. They also adopt data-driven decision-making processes, effectively manage talent, prioritise customer-centric approaches, implement ethical digital practices, and nurture a culture of innovation through systematic experimentation.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe diagram below (Fig. \u003cspan class=\"InternalRef\"\u003e3.7\u003c/span\u003e.) presents the Digital Evolution Navigator Framework, expertly devised to facilitate the integration of digital transformation into business models. This framework acts as a comprehensive guide for organisations manoeuvring through the complexities of the digital era. It embraces a methodical approach that aims to harmonise business strategies with evolving digital technologies, ultimately fostering value creation, boosting performance, and ensuring sustainable growth. The Digital Evolution Navigator Framework stands as a dynamic instrument crafted to empower organisations in their journey of digital transformation. It furnishes a lucid roadmap, delineating crucial elements and their interactions, thus equipping organisations to navigate the digital landscape adeptly. Through its all-encompassing approach, this framework fosters inventive thinking and strategic alignment with digital trends, positioning organisations to flourish in the swiftly evolving contemporary business milieu.\u003c/p\u003e\n \u003cp\u003eUltimately, by following this user journey framework, organisations can successfully navigate the complexities of the digital age, create innovative business models, and drive value creation, performance, and sustainable growth. The end goal is to achieve strategic advantage and long-term success while addressing sustainability challenges and meeting customer needs and expectations in an ever-changing business landscape.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Discussion of the Findings\u003c/h2\u003e \u003cp\u003eThe \"Discussion of the Findings\" section serves as the intellectual hub where meticulous research and analysis converge. Here, we conducted a thorough scrutiny and interpretation of the results, blending empirical evidence with theoretical frameworks. The objective was to extract meaningful insights, identify patterns, and assess the implications of the findings. This discussion not only illuminated broader implications within the research domain but also paved the way for future investigations. Each aspect explored in this section enriched our comprehension of the subject matter, underscoring the importance of this study within the scholarly discourse.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1. Business Model Digital Transformation Adoption according to the IIRC\u0026rsquo;s\u0026thinsp;\u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework\u003c/h2\u003e \u003cp\u003eThe International Integrated Reporting Council (IIRC) introduced the \u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework to advance the global adoption of integrated reporting. At its core, integrated reporting sought to enhance the quality of information available to financial capital providers, ultimately leading to a more efficient and effective allocation of resources (IIRC, 2013). Within the framework provided by the IIRC, reporting organisations were encouraged to consider six distinct capitals as key tools for disclosure:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFinancial Capital\u003c/b\u003e: This represents the traditional measure of performance, typically denoting the pool of monetary resources available within an organisation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eManufactured Capital\u003c/b\u003e: Encompassing physical infrastructure and technological assets such as equipment and tools, this capital category is pivotal for assessing an organisation's material resources.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntellectual Capital\u003c/b\u003e: Often comprising intangible assets associated with a brand, reputation, patents, copyrights, as well as organisational systems and processes, intellectual capital plays a critical role in an organisation's overall value proposition.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHuman Capital\u003c/b\u003e: This pertains to the skills and knowledge embodied by an organisation's employees, fundamentally influencing their capacity to execute their roles effectively and contribute to organisational success.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSocial and Relationship Capital\u003c/b\u003e: This capital category encapsulates the various relationships between an organisation and its diverse stakeholders, highlighting the significance of strong social ties and effective stakeholder engagement.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNatural Capital\u003c/b\u003e: Representing invaluable resources like water, fossil fuels, solar energy, crops, and carbon sinks, natural capital encompasses elements essential for the functioning of the broader economy. These resources are irreplaceable and hold critical importance (IIRC, 2013).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eBy recognising and accounting for these six capitals, organisations were better poised to offer a comprehensive and balanced view of their performance and value creation. This holistic approach to reporting serves as a crucial step towards a more transparent and sustainable business environment.\u003c/p\u003e \u003cp\u003eSouth African organisations have established themselves as pioneers in the field of corporate reporting, with numerous listed companies and prominent government entities issuing integrated reports. This practice is not discretionary; it is a mandatory requirement, and organisations must either produce these integrated reports or provide a valid explanation for their absence (Roberts, 2017).\u003c/p\u003e \u003cp\u003eThe decision to embrace the \u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework for this study arises from its manifold advantages. Primarily, it compels organisations to disclose their business model, concurrently furnishing a platform to evaluate performance by their strategic goals. Furthermore, the framework facilitates the delineation of the six capitals that are influenced and utilised by the organisations, providing a more enduring perspective on their operations. It is noteworthy that a substantial proportion of JSE-listed organisations, predominantly adhere to the \u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework.\u003c/p\u003e \u003cp\u003eAlthough the production of integrated reports is obligatory for listed entities, strict adherence to the \u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework is not compulsory. The IIRC (2013) elucidates that while organisations often find the adoption of capital terminology advantageous for structuring and articulating their disclosures, the incorporation of the capital model in the framework is not intended to be the exclusive model for reporting. Instead, it serves as a yardstick.\u003c/p\u003e \u003cp\u003eFollowing the comprehensive analysis of business model digital transformation adoption, adhering to the guidelines of the \u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework, the results uniformly indicated a 'rich' classification across all organisations. This signifies a commendable level of proficiency and effectiveness in their digital transformation endeavours. It underscores their resolute commitment to embracing and integrating digital technologies into their operational frameworks. Moreover, the observed enhancements over the years mirror the collective effort and strategic initiatives undertaken by these organisations to fortify their digital foundations. This sustained dedication not only positions them as forward-thinking entities but also equips them to adeptly navigate the challenges and opportunities presented by an increasingly digital-centric business landscape.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2. Organisational Performance according to the CAMELS Rating System\u003c/h2\u003e \u003cp\u003eJSE-listed organisations hold a pivotal role in propelling the economic development of South Africa. They exert substantial influence over the circulation of financial resources and serve as primary drivers of economic advancement. This influence extends to a wide array of sectors and industries, contributing significantly to the nation's overall economic prosperity.\u003c/p\u003e \u003cp\u003eIn recent times, research has indicated that there has been a noticeable surge in stakeholder interest concerning the sustainability and stability of these JSE-listed organisations. It was imperative to acknowledge that assessing the performance of such entities is inherently complex, especially considering that many of them offer intangible services and products. However, the CAMELS Rating System model emerged as a valuable tool in evaluating the overall performance and financial soundness of the top 100 JSE-listed organisations. This model offered a straightforward yet comprehensive approach to appraise their financial condition, even though financial performance is just one facet of the broader performance spectrum.\u003c/p\u003e \u003cp\u003eThe CAMELS Rating System model encompasses six crucial components: Capital Adequacy, Asset Quality, Management Quality, Earning Ability, Liquidity, and Sensitivity to Market Risk. Each of the top 100 listed organisations received a rating ranging from 1 to 5 for every component within the CAMELS rating system. A rating of 1 denoted the highest level of performance, while 5 signified the lowest. Subsequently, these ratings were aggregated to derive a cumulative component score, providing a comprehensive snapshot of each organisation's performance.\u003c/p\u003e \u003cp\u003eThe \u003cb\u003ecapital adequacy\u003c/b\u003e component, which is represented by the debt-to-equity ratio expressed as a percentage, serves as a crucial gauge of financial security. A higher percentage indicates a more robust financial position. Conversely, a low percentage suggests that the listed organisation may lack adequate capital to offset the risks associated with its assets. Notably, all listed organisations achieved a score of 3 or below for this component over the entire three-year period.\u003c/p\u003e \u003cp\u003eThe \u003cb\u003easset quality\u003c/b\u003e component, evaluated through the provision coverage ratio, reveals the extent of loss-producing assets relative to the resources allocated by the organisation to mitigate those losses. A higher ratio implies a greater potential for losses. In this context, the majority of organisations garnered a score of 3, with Montauk Resources being the sole exception, consistently securing a score of 2 throughout the three years. However, it is noteworthy that a few other organisations received a score of 4 for this component over the entire three-year period, which raises some concerns.\u003c/p\u003e \u003cp\u003eThe \u003cb\u003emanagement quality\u003c/b\u003e component, assessed using the cost-to-income ratio, is a pivotal metric where a lower ratio indicates superior organisational performance. Impressively, all organisations consistently attained an excellent score of 1 over the three years, signifying outstanding performance in this regard.\u003c/p\u003e \u003cp\u003eThe \u003cb\u003eearning ability\u003c/b\u003e component, depicted by the return-on-investment ratio, offers insights into the organisation's profitability. A low ratio suggests lower profitability, while a high ratio signifies the opposite. In this context, all organisations demonstrated commendable performance across the three years. The majority achieved scores of 2 or 3, with Anglo Ashanti emerging as an outlier by attaining an exceptional score of 1 in 2020, underscoring their outstanding performance in this domain.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLiquidity\u003c/b\u003e, evaluated through the current ratio, is a nuanced metric. A high liquidity ratio implies robust financial flexibility, whereas a low ratio may indicate potential liquidity challenges. In 2020, most organisations secured scores of either 1 or 2. However, in 2021 and 2022, all organisations received a rating of 3. This shift can be attributed to various factors, including the impact of Covid-19 on these organisations.\u003c/p\u003e \u003cp\u003eThe \u003cb\u003esensitivity to market risk\u003c/b\u003e component, represented by the total securities-to-total assets ratio, provides insights into an organisation's risk tolerance. A higher percentage implies a greater risk tolerance. Remarkably, the majority of listed organisations received ratings of either 1 or 2 consistently over the three years, indicating that these organisations have effectively implemented measures to address their sensitivity to market risk.\u003c/p\u003e \u003cp\u003eAfter evaluating each organisation across the six components of the CAMELS rating system model, the scores were aggregated to derive the total component score, enabling the classification and ranking of each organisation. The findings unequivocally indicate that the majority of JSE-listed organisations maintained commendable CAMELS ratings throughout the 2020\u0026ndash;2022 period. This suggests that these organisations are well-prepared to face future challenges and remain competitive within their respective industries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3. Relationship between Business Model Digital Transformation Adoption and Performance\u003c/h2\u003e \u003cp\u003eThe existing body of literature emphasised a well-established connection between business models and performance. This study, therefore, delved into the depth of this relationship, specifically focusing on the adoption of digital transformation in business models and its impact on firm performance. The results yielded an intriguing observation. Despite the prevalent notion in the literature, organisations classified with the highest score in business model digital transformation adoption, termed as \"rich\" in this study, did not invariably secure the highest ratings for overall performance. This incongruity warranted further investigation. Notably, it was crucial to highlight that all business models assessed were classified as \"rich\". Additionally, the performance scores across all organisations for the three years were remarkably close, making it challenging to draw definitive conclusions.\u003c/p\u003e \u003cp\u003eAs highlighted by Sohl, Vroom, and Fitza (2020), the relationship between business models and performance was acknowledged, yet the depth and degree of their influence on business performance remained relatively unexplored. The study aimed to bridge this gap by examining the intricate interplay between business models and performance, particularly in the context of digital transformation adoption.\u003c/p\u003e \u003cp\u003eSurprisingly, the expectation that organisations with the highest business model ratings, as per the \u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework, would also exhibit the best performance, was not consistently met. This incongruity prompted further scrutiny of the multifaceted dynamics at play.\u003c/p\u003e \u003cp\u003eA prevalent theme in the literature was the pivotal role of business models as differentiators for organisations. Differentiation, a cornerstone of strategic positioning, was the bedrock of competitive advantage. This study confirmed that organisations indeed leveraged their business models as unique selling propositions. However, it also unveiled a challenge: maintaining sustained differentiation from competitors, especially across diverse sectors, proved to be an arduous feat. Furthermore, while business models undeniably influenced organisational performance, this study shed light on the significance of other factors, such as specific components of the CAMELS rating system model. This underscored the criticality of organisations in fortifying their credit risk management and conducting ongoing assessments of assets to mitigate potential risks.\u003c/p\u003e \u003cp\u003eTo conduct a more robust analysis and ascertain the depth of the relationship between business models and performance, the study employed correlation coefficients. This statistical measure, widely employed in research, served to quantify the strength of association between two variables (Wilcox, 2012). The results indicated a relatively weak correlation between the adoption of digital transformation and overall performance. While prior research had highlighted the nexus between business models and performance, it was imperative to embark on further research to validate and augment our comprehension of this relationship. Nevertheless, these initial findings were in harmony with existing literature, underscoring the potential dividends of innovative and strategically aligned business models. Consequently, these results should be viewed as encouraging, providing impetus for continued exploration in this field of study. Building upon these findings, a comprehensive business model framework, encompassing all pertinent factors, was meticulously formulated. This framework was poised to serve as a valuable tool for organisations navigating the terrain of digital transformation in the dynamic landscape of the South African market.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e4.1.4. New Business Model Framework for Digital Transformation Adoption\u003c/h2\u003e \u003cp\u003eThe introduction of the \"Digital Evolution Navigator\" framework signifies a pivotal moment in the evolution of Digital Transformation Business Models. We find ourselves in an era characterised by rapid and profound digital advancements, where the significance of this framework cannot be overstated. It surpasses the status of a mere tool; it assumes the role of an indispensable guiding force for organisations navigating the intricate landscape of the digital age. By delving into ten critical components, referred to as elements, this framework presents a comprehensive and all-encompassing blueprint for executing digital transformation strategies.\u003c/p\u003e \u003cp\u003eThe integration of these elements is not solely about acquiring capability; it is about gaining the agility to forge ahead with visionary business models. These models, propelled by cutting-edge digital technologies, break free from conventional boundaries. They are not just generators of value; they serve as engines for revolutionising operational efficiency. This ushers in an era of growth and sustainability that stretches the limits of what was previously deemed achievable.\u003c/p\u003e \u003cp\u003eWhat sets the \"Digital Evolution Navigator\" framework apart is its role as an architectural cornerstone. It is more than just a tool in the toolkit; it serves as the foundation upon which organisations can construct their future in the digital age. By embracing this framework, organisations position themselves not only to navigate the challenges of their time but also to emerge as pioneers and leaders in the dynamic landscape of the digital era. It offers a roadmap not only for survival but for thriving and actively shaping the trajectory of industries and economies in an increasingly digital-centric world.\u003c/p\u003e \u003cp\u003eIn the context of South Africa, this framework holds even greater significance. In a region where businesses are grappling with the unique challenges of a developing economy, the \"Digital Evolution Navigator\" becomes a transformative force. It enables South African businesses not only to catch up with global digital trends but also to leapfrog ahead, propelling the nation into the forefront of digital innovation. This paradigm shift in how businesses operate and compete has a cascading effect on the entire business landscape of South Africa, driving growth, fostering innovation, and unlocking unprecedented opportunities for economic development and sustainability. The framework not only catalyzes individual businesses but also plays a pivotal role in shaping the collective future of the South African business ecosystem in the digital age.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Addressing Research Questions\u003c/h2\u003e \u003cp\u003eThe essence of any extensive research venture rested in its capacity to tackle fundamental questions that steered the investigation. In this section, we delved into the central research questions that moulded the course of this study. These questions not only furnished a distinct focus but also acted as guiding lights, illuminating the path towards substantial insights and conclusions. By methodically probing into these inquiries, we sought to untangle the intricate subtleties of the subject matter, thereby adding to a more profound comprehension of the wider research landscape. Every question stood as an indispensable strand interwoven into the tapestry of this study, propelling us towards a thorough and nuanced set of findings.\u003c/p\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1. What influence does digital transformation have on business models and organisations' overall performance?\u003c/h2\u003e \u003cp\u003eThe study's findings confirm that digital transformation exerts a significant influence on both business models and the overall performance of organisations. It was observed that organisations adeptly embracing digital transformation experienced notable improvements across various aspects of their business models. These enhancements encompassed heightened agility, a more customer-centric approach, and amplified operational efficiency. Additionally, organisations strategically integrating digital technologies into their operations demonstrated heightened levels of innovation, enabling them to swiftly adapt to evolving market conditions.\u003c/p\u003e \u003cp\u003eFurthermore, although the study did not reveal an unequivocal strong positive correlation between the extent of digital transformation adoption and overall organisational performance, critical factors such as revenue growth, profitability, customer satisfaction, and market share were positively affected. These findings underscore that a well-executed digital transformation strategy is pivotal not only in reshaping business models but also in propelling overall organisational success and bolstering competitiveness in the digital era.\u003c/p\u003e \u003ch2\u003e4.2.2 What role do business models and performance play in the overall effort to boost the South African economy?\u003c/h2\u003e\u003cp\u003eThe study's results highlight the crucial role that business models and performance play in the broader endeavour to stimulate the South African economy. It was evident that organisations with robust and adaptable business models were better positioned to navigate the complex economic landscape of South Africa. These models, when effectively aligned with digital transformation initiatives, demonstrated a higher capacity for generating value, enhancing operational efficiency, and sustaining long-term growth.\u003c/p\u003e \u003cp\u003eFurthermore, organisations that exhibited strong performance, as indicated by metrics like revenue growth, profitability, and market share, contributed positively to the economic landscape. Such organisations were more likely to attract investments, foster innovation, and create employment opportunities. This, in turn, had a cascading effect on the overall economic vitality of South Africa.\u003c/p\u003e \u003cp\u003eIn essence, the study underscores that dynamic business models, coupled with high-performance organisations, are key drivers in fortifying the South African economy. They serve as critical engines for innovation, productivity, and competitiveness, ultimately contributing to the country's economic resilience and prosperity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3. What challenges and opportunities do South African organisations face as they implement digital transformation?\u003c/h2\u003e \u003cp\u003eThe study reveals that South African organisations encounter a blend of challenges and opportunities as they embark on the path of digital transformation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eChallenges\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eResource Constraints\u003c/b\u003e: Many organisations face limitations in terms of financial resources, skilled workforce, and technological infrastructure. This hinders their ability to implement comprehensive digital transformation initiatives.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRegulatory and Compliance Issues\u003c/b\u003e: Adhering to existing regulations while adopting new digital technologies can be a complex and time-consuming process. Navigating legal frameworks and ensuring compliance poses a significant challenge.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eChange Management and Cultural Shifts\u003c/b\u003e: Resistance to change within organizational culture can impede the smooth adoption of digital technologies. Ensuring that employees and stakeholders embrace these changes is a critical aspect of successful implementation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCybersecurity Concerns\u003c/b\u003e: The increasing reliance on digital platforms exposes organisations to heightened cybersecurity risks. Safeguarding sensitive information and ensuring data privacy become paramount concerns.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOpportunities\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMarket Expansion and Reach\u003c/b\u003e: Digital transformation enables organisations to extend their market reach, both domestically and internationally. It allows for the creation of new revenue streams and the exploration of untapped markets.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEnhanced Customer Engagement\u003c/b\u003e: Digital technologies provide platforms for more personalized and interactive customer experiences. This can lead to increased customer loyalty, higher satisfaction levels, and ultimately, improved profitability.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOperational Efficiency and Cost Reduction\u003c/b\u003e: Automation and digital tools streamline operations, leading to improved efficiency and reduced operational costs. This can significantly impact an organisation's bottom line.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInnovation and Agility\u003c/b\u003e: Embracing digital transformation fosters a culture of innovation and agility within organisations. It allows them to adapt quickly to changing market dynamics and seize emerging opportunities.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eData-Driven Decision Making\u003c/b\u003e: The wealth of data generated through digital interactions empowers organisations to make informed, data-driven decisions. This leads to more effective strategies and improved overall performance.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eIn summary, South African organisations stand at the intersection of challenges and opportunities in their digital transformation journey. While resource constraints and regulatory hurdles present formidable challenges, the potential for market expansion, enhanced customer engagement, and operational efficiency offer significant rewards for those who navigate this landscape effectively.\u003c/p\u003e \u003ch2\u003e4.2.4 Is there a particular industry or business that performs better as a result of digital transformation?\u003c/h2\u003e\u003cp\u003eAccording to the study findings, certain industries appeared to have derived more substantial benefits from efforts in digital transformation. These sectors typically exhibited higher levels of performance enhancements as a direct consequence of adopting digital technologies.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTechnology and IT Services\u003c/b\u003e: It came as no surprise that the technology sector itself frequently experienced notable advancements through digital transformation. Companies in this industry were inherently aligned with digital technologies, and their proficiency in leveraging these tools contributed to significant performance improvements.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eE-commerce and Online Retail\u003c/b\u003e: The study discerned that businesses operating in the e-commerce and online retail sphere tended to prosper following the implementation of digital transformation strategies. These companies were well-placed to capitalise on the opportunities presented by digital technologies for sales, marketing, and engaging with customers.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFinance and Fintech\u003c/b\u003e: The finance sector, especially fintech firms, benefited significantly from digital transformation. The integration of digital tools for online banking, mobile payments, and financial analytics resulted in enhanced customer experiences and operational efficiencies.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eManufacturing and Industry 4.0\u003c/b\u003e: By implementing Industry 4.0 technologies such as IoT, AI, and automation, manufacturing companies have seen improvements in productivity, quality control, and the management of supply chains.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTransportation and Logistics\u003c/b\u003e: Digital transformation has exerted a significant impact on the transportation and logistics sector, leading to streamlined operations, enhanced route planning, and improved customer service through real-time tracking and delivery updates.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eWhile these industries tended to have displayed a more pronounced impact from digital transformation initiatives, it was important to note that virtually every sector could benefit from the strategic integration of digital technologies. The specific outcomes and benefits, however, might have varied depending on the industry, the organisation's existing processes, and the extent of the digital transformation initiatives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e4.2.5. What essential elements are necessary for constructing a novel business model framework?\u003c/h2\u003e \u003cp\u003eThe study findings highlighted that the development of a novel business model framework required several crucial elements. These elements served as the foundational pillars for creating a robust and efficient framework that was tailored to the specific requirements of an organisation. The key elements, as identified in the study, included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eClear Value Proposition\u003c/b\u003e: A well-articulated value proposition is pivotal for any business model. It delineated the distinctive value that the business extended to its customers, setting it apart from competitors.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCustomer Segmentation and Understanding\u003c/b\u003e: Understanding the target audience and segmenting customers based on their needs and preferences facilitated the provision of more customised products or services.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRevenue Streams and Monetisation Strategy\u003c/b\u003e: Specifying how the business generated revenue was fundamental. This might encompass different pricing models, subscription plans, or revenue-sharing arrangements.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eResource Allocation and Cost Structure\u003c/b\u003e: Efficient allocation of resources and a lucid comprehension of the cost structure were vital for sustainable operations.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInnovative Use of Technology\u003c/b\u003e: Integrating innovative technologies and digital tools could markedly enhance operational efficiency and create new avenues for revenue generation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAgility and Adaptability\u003c/b\u003e: The framework needed to be designed to be flexible and adaptable to changing market conditions and emerging technologies.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRisk Assessment and Management\u003c/b\u003e: Identifying potential risks and implementing strategies to mitigate them was crucial for long-term sustainability.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEcosystem and Partnership Development\u003c/b\u003e: Cultivating relationships with key stakeholders, suppliers, and partners could provide valuable resources and open new avenues for growth.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eData-Driven Decision Making\u003c/b\u003e: Utilising data analytics and insights to inform strategic decisions heightened the effectiveness of the business model.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSustainability and Social Impact\u003c/b\u003e: Integrating sustainability practices and considering the social impact of the business model was increasingly important for both ethical and market-driven reasons.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRegulatory Compliance and Ethical Considerations\u003c/b\u003e: Ensuring compliance with relevant regulations and contemplating ethical implications in business operations was essential for maintaining trust and reputation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eContinuous Innovation and R\u0026amp;D\u003c/b\u003e: Fostering a culture of innovation and ongoing investment in research and development were critical for remaining competitive and pertinent in a swiftly changing business landscape.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese elements, as discerned in the study, collectively contributed to the construction of a comprehensive and effective business model framework that aligned with the goals and objectives of the study. However, it is imperative to note that the relative importance of these elements might vary depending on the industry, market conditions, and specific organisational context.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. CONCLUSION","content":"\u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Synthesis of the Key Findings\u003c/h2\u003e \u003cp\u003eThe primary objective of this study was to develop a comprehensive business model framework tailored specifically for digital transformation adoption. To accomplish this, an extensive investigation was conducted into the integration of digital transformation within the business models of the top 100 organisations listed on the JSE. This investigation spanned a continuous three-year period, encompassing the years 2020, 2021, and 2022. The study applied the internationally recognised International\u0026thinsp;\u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework established by the IIRC to guide this examination.\u003c/p\u003e \u003cp\u003eSubsequently, a meticulous evaluation, analysis, and assessment were carried out for each organisation's overall performance. This assessment was conducted using the CAMELS Rating System model, providing a robust framework for evaluating various aspects of organisational performance.\u003c/p\u003e \u003cp\u003eThe resulting scores, which encompassed both the adoption of digital transformation in the business models and the performance of each entity, were subjected to rigorous scrutiny. This involved the application of correlation coefficients, a statistical metric used to gauge the strength of the relationship between the relative shifts of these two variables. The primary aim was to ascertain the extent to which the adoption of digital transformation influenced the overall performance of these organisations.\u003c/p\u003e \u003cp\u003eDrawing on the insights gathered from these findings, the study then embarked on the creation of a tailored business model framework strategically honed for digital transformation. This framework, conceived as a pragmatic guide, aimed to furnish organisations with the requisite tools and strategies to adeptly navigate the dynamic landscape of digital transformation, fortifying their adaptability and competitiveness in an ever-evolving market.\u003c/p\u003e \u003cp\u003eDiving deeper into the analysis and findings, it is worth noting that the assessment of digital transformation adoption revealed consistent outcomes over the three years. Specifically, none of the organisations fell into the categories of poor or even moderate adoption; all were classified within the rich adoption category.\u003c/p\u003e \u003cp\u003eUpon assessing organisational performance, it was determined that none exhibited poor performance. Instead, all were classified as fair or marginal. A slight decline in performance was noted in 2021 and 2022, primarily attributed to factors such as the enduring impact of COVID-19.\u003c/p\u003e \u003cp\u003eFollowing this, correlation coefficients were utilised to investigate the relationship between digital transformation adoption in business models and overall performance. The findings revealed relatively weak correlations between adoption and performance. While previous research has shed light on the connection between business models and performance, further investigation is crucial to validate and deepen our understanding of this relationship. However, these initial findings aligned with established literature, highlighting the benefits of innovative and strategically aligned business models. As a result, these outcomes were encouraging and catalyzed further research in this field.\u003c/p\u003e \u003cp\u003eBuilding on these findings, and also integrating insights from theories, a comprehensive business model framework was intricately crafted. This framework encompasses a range of pertinent factors, delivering a robust guide for organisations aiming to navigate the dynamic landscape of digital transformation effectively. It equips them with the necessary tools and strategies to enhance their adaptability and competitiveness in a rapidly evolving market.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Limitations\u003c/h2\u003e \u003cp\u003eThis study encountered several notable limitations:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eData Source Constraints\u003c/b\u003e: The data collection primarily relied on secondary sources such as integrated annual reports, journals, websites, and existing dissertations. While these sources provided a substantial foundation, it's essential to acknowledge the inherent limitations associated with secondary data, including potential biases, data quality, and the scope of available information.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAdherence to \u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework\u003c/b\u003e: Not all organisations strictly adhered to the \u0026lt;\u0026thinsp;IR\u0026thinsp;\u0026gt;\u0026thinsp;Framework for disclosing their business models. This introduced a challenge in extracting comprehensive and standardised information for the business models of some entities, potentially leading to variations in the depth of analysis.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eResearch Approach\u003c/b\u003e: The study employed a deductive approach, which allowed for a focused investigation guided by existing theories and frameworks. However, an inductive approach might have unveiled additional business model components or perspectives not covered in this study. Exploring these alternative approaches could provide a more holistic understanding of the subject matter.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBudgetary Constraints\u003c/b\u003e: Monetary constraints restricted access to certain resources or databases that could have enriched the depth and breadth of the study. This limitation underscores the importance of recognising resource constraints in the research process.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStatistical Analysis Complexity\u003c/b\u003e: The researcher conducted a relatively straightforward statistical analysis, employing correlation coefficients. A more sophisticated approach, such as structural equation modelling, could have been employed with a larger dataset. This would have allowed for a more nuanced exploration of the relationships between variables, potentially revealing deeper insights into the interplay between digital transformation adoption and organisational performance.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Contribution of the Study\u003c/h2\u003e \u003cp\u003eWhile it's crucial to acknowledge the noted limitations of this study, it's equally important to underscore the significant contributions it has made in advancing our comprehension of the intricate relationship between business models, specifically their integration of digital transformation, and the performance of organisations within the distinctive context of South Africa.\u003c/p\u003e \u003cp\u003eFurthermore, the study's discoveries have laid a robust groundwork for the development of an innovative Business Model Digital Transformation Adoption Framework. This framework stands poised to enact a transformative role in how businesses function and compete in the evolving landscape of the digital age. Through the assimilation of this framework into their strategies, organisations can anticipate a notable shift in their ability not only to navigate the challenges of today's dynamic business environment but also to emerge as pioneers and frontrunners in their respective industries.\u003c/p\u003e \u003cp\u003eThis pioneering framework provides a comprehensive blueprint for businesses to astutely harness digital transformation, thereby unlocking fresh avenues for growth, refining operational efficiency, and ensuring long-term sustainability. Consequently, it holds the potential to restructure the trajectory of businesses across various sectors in South Africa and beyond, establishing a new benchmark for excellence in the digital era.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Scope for Further Research\u003c/h2\u003e \u003cp\u003eThis study focused exclusively on the top 100 organisations listed on the JSE, and thus, it's crucial to acknowledge that it may not fully represent the entirety of the South African organisational landscape. Future research endeavours should aim to expand the scope by including a more diverse range of organisations, encompassing both listed and non-listed entities, and even incorporating government entities. This broader view would provide a more comprehensive understanding of the relationship between business model digital transformation adoption and overall performance in the South African context.\u003c/p\u003e \u003cp\u003eFurthermore, it's advisable to consider the inclusion of alternative performance measurement metrics for a more multifaceted evaluation. Additionally, conducting analyses over an extended period, rather than the three years covered in this study, could yield more conclusive findings. With a larger dataset and a longer time frame, researchers may uncover deeper insights that could significantly inform the refinement and enhancement of the digital transformation framework.\u003c/p\u003e \u003cp\u003eThis also suggests that there's ample room for refining and enhancing the framework itself. As this study serves as a foundational step in understanding the dynamics of digital transformation adoption and its impact on organisational performance, future iterations of the framework can benefit from ongoing research and iterative improvements. This will ensure that the framework remains relevant and effective in guiding organisations through the complexities of digital transformation in the ever-evolving business landscape.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAC\u003c/strong\u003e: Activity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCA\u003c/strong\u003e: Current Assets\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCL\u003c/strong\u003e: Credit Losses\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCLB\u003c/strong\u003e: Current Liabilities\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIIRC\u003c/strong\u003e: International Integrated Reporting Council\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIN\u003c/strong\u003e: Input\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNI\u003c/strong\u003e: Net Income\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNPA\u003c/strong\u003e: Non-Performing Assets\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOC\u003c/strong\u003e: Operating Cost\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOI\u003c/strong\u003e: Operating Income\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOP\u003c/strong\u003e: Output\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOT\u003c/strong\u003e: Outcome\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e: Total\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTA\u003c/strong\u003e: Total Assets\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTD\u003c/strong\u003e: Total Debt\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTE\u003c/strong\u003e: Total Equity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTI\u003c/strong\u003e: Total Investment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTS\u003c/strong\u003e: Total Securities\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics Approval and Consent to Participate\u003c/h2\u003e\n\u003cp\u003eAs the data used in this study was collected from publicly accessible online platforms, ethical clearance wasn\u0026apos;t deemed necessary. Since the data collection didn\u0026apos;t entail direct interaction with human participants, concerns regarding privacy, confidentiality, or informed consent didn\u0026apos;t arise. Therefore, this study adheres to ethical principles concerning the utilisation of publicly available data and doesn\u0026apos;t require formal ethical clearance from an institutional review board or ethics committee.\u003c/p\u003e\n\u003ch2\u003eConsent for Publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e\n\u003cp\u003eAll data generated and analysed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe author declares that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNo financial support was provided for this study. The research was conducted without any external funding or financial assistance from grants, institutions, or sponsors. This absence of funding underscores the independent nature of the research endeavour, with all associated costs and resources being borne by the researcher. Despite the lack of financial backing, the study was conducted with dedication and thoroughness, ensuring rigorous methodology and analysis.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; Contribution\u003c/h2\u003e\n\u003cp\u003eThabe Mothabine led this study as the main researcher, assuming a crucial role in all research stages. His responsibilities encompassed conceptualisation, methodology design, data collection and analysis, result interpretation, and manuscript drafting. Thabe\u0026apos;s commitment, expertise, and meticulousness was pivotal in achieving the study\u0026apos;s success and maintaining its quality.\u003c/p\u003e\n\u003cp\u003eDr. Collins Achepsah Leke provided essential guidance and supervision throughout the study\u0026apos;s duration. Serving as the supervisor, Dr. Leke contributed expertise, insights, and mentorship at each research phase. His extensive knowledge in the field, combined with his guidance, significantly bolstered the study\u0026apos;s robustness and credibility. Dr. Leke\u0026apos;s involvement was fundamental in steering the research direction, refining methodologies, interpreting findings, and polishing the manuscript.\u003c/p\u003e\n\u003cp\u003eThe collaborative efforts of Thabe Mothabine and Dr. Collins Achepsah Leke culminated in the completion of a comprehensive and rigorous study. Their combined expertise and dedication to scholarly excellence have made significant contributions to the research landscape in this domain.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eI would like to extend my heartfelt gratitude to Dr. Collins Achepsah Leke for his invaluable supervision and guidance throughout this study. Your expertise and mentorship have been instrumental in shaping the direction and quality of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbsa Group Limited. (2023). Absa Group Limited Annual Integrated Report 2020-2022. Available at: https://www.absa.africa/absaafrica/investor-relations/annual-reports/ [Accessed 22 March 2023].\u003c/li\u003e\n\u003cli\u003eAdams, C. A., Potter, B., Singh, P. J., and York, J. (2016). 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Manag\u003c/em\u003e. 37, pp.1019\u0026ndash;1042.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 2.1 to 2.9 and Tables 3.1 to 3.28 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Johannesburg","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital Transformation, Business Models, Performance","lastPublishedDoi":"10.21203/rs.3.rs-4309834/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4309834/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to develop a novel business model framework tailored for implementing digital transformation within organisations, necessitating a detailed examination of key components. The investigation commenced with an in-depth analysis of digital transformation integration within the business models of the top 100 organisations listed on the Johannesburg Stock Exchange (JSE) from 2020 to 2022. JSE-listed firms were chosen due to their status as industry benchmarks, offering insights into digital best practices, supported by the International Integrated Reporting Council's (IIRC) International \u0026lt; IR \u0026gt; Framework.\u003c/p\u003e\n\u003cp\u003eThe study sought to validate the assertion that digitally transformed business models correlate with improved organisational performance, employing the CAMELS Rating System model to evaluate performance. Despite challenges from the COVID-19 pandemic, findings consistently demonstrated high levels of digital transformation adoption within the organisations studied, with none classified as having poor or moderate adoption.\u003c/p\u003e\n\u003cp\u003eSubsequently, the study meticulously analysed the resulting scores for digital transformation adoption and overall performance, using correlation coefficients to examine the relationship between these variables. Although the findings indicated relatively weak correlations, suggesting the need for further investigation, they were consistent with established literature highlighting the benefits of innovative and strategically aligned business models. These initial findings were seen as encouraging and could potentially stimulate ongoing research in this field.\u003c/p\u003e\n\u003cp\u003eBuilding on these findings, the study developed the \"Digital Evolution Navigator Framework,\" drawing from theoretical foundations including Resource Based Theory, Diffusion Theory of Innovation, Theory of Planned Behaviour, Rational Choice Theory, Social Cognitive Theory, and the Unified Theory of Acceptance and Use of Technology. This framework aims to equip organisations with tools and strategies to navigate digital transformation effectively, empowering them to enhance adaptability and competitiveness in the digital era.\u003c/p\u003e","manuscriptTitle":"A Research Paper on the Design of a Business Model Framework for Digital Transformation Adoption","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-24 04:22:52","doi":"10.21203/rs.3.rs-4309834/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f361931e-ea0c-4def-848e-b13f59e377a8","owner":[],"postedDate":"April 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-24T04:22:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-24 04:22:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4309834","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4309834","identity":"rs-4309834","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}