Assessing the Benefits and Challenges in Transportation Logistics: Testing the Effectiveness and Efficiency of Existing Risk Evaluation Procedures in Supply Chain 

Assessing the Benefits and Challenges in Transportation Logistics: Testing the Effectiveness and Efficiency of Existing Risk Evaluation Procedures in Supply Chain | 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 Assessing the Benefits and Challenges in Transportation Logistics: Testing the Effectiveness and Efficiency of Existing Risk Evaluation Procedures in Supply Chain Lord Emmanuel Yamoah, Ronald Osei Mensah, Daniel Opoku-Akyea This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6691764/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 Supply chain risk management is critical to the effectiveness and resilience in transportation logistics operations. The purpose of this study is to examine the effectiveness and proficiency of current risk evaluation processes in transportation logistics supply chains. Through quantitative research approach, the data was sourced through surveys of supply chain professionals, logistics directors, and corporate executives. The study shows that Scenario Analysis, Quantitative Risk Assessment, Qualitative Risk Assessment, Supply Chain Mapping and Root Cause Analysis as well as Supply Chain Diversification significantly impacts transport logistics performance. However, Real-time Monitoring and Tracking are not strongly linked to changes in transport logistics performance. The development of best practises and standardization for efficient risk assessment and management in supply chains can be influenced by these findings. The study also perceives the challenge of integrating standardized risk evaluation procedures with existing systems as highly significant and practically impactful. Several anticipated benefits arise from incorporating new technology in improving supply chain risk evaluation processes. However, the cost of implementation and maintenance, along with addressing organizational culture and change resistance, as highly significant incorporating emerging technologies into risk assessment procedure. It emerged that when integrating new technologies, the costs of installation and upkeep, data security and privacy concerns as well as dealing with organisational culture and change resistance, are quite important. The study underscores the value of robust risk assessment practices in enhancing supply chain. Risk evaluation transportation logistics standardization technology integration supply chain Industrial sociology 1. Introduction Supply chains are intricate networks that encompass the movement of goods, services, information, and funds across various stages, from procurement to production to distribution (Christopher, 2016 ). A significant issue has been the risk that reduces the supply chain's effectiveness. By easing the flow of commodities from suppliers to manufacturers, distributors, retailers, and finally to end customers, transportation logistics, which entail the actual movement of goods, resources, and products, play a crucial role in supply chains (Christopher, 2016 ). The rising unpredictability and complexity of global supply networks are to blame for the striking rise in supply chain risks and disturbances that researchers have seen in recent years (Ivanov, 2020 ; Katsaliaki et al., 2021 ; Pournader et al., 2020 ; Sharma et al., 2021 ). According to Choi et al. ( 2016 ), the business environment has changed dramatically since the past and is predicted to change even more quickly in the future. Natural catastrophes, political unrest, local instability, and epidemics are only a few of the possible dangers that come under the category of catastrophic events (Wagner & Bode, 2008 ). The issue of risk has been a major concern due to their linkage to other supply chain problem. Even though these "black swan" events are unlikely, they can have a disastrous effect. However, it is essential to consider risks of all sizes and varieties as part of a thorough supply chain risk management (SCRM) strategy. Any risk, whether it be a queue halt or the delivery of the incorrect item, can cause significant disruptions and have an influence on other risks. This highlights the need of proper risk analysis. The "Global Risk Report 2020" (World Economic Forum, 2020 ), which listed 30 dangers that have the potential to have a substantial impact on countries and emphasised their interconnectivity, also recognised the interconnectedness of threats. Hendricks et al. ( 2009 ) contend that prompt restoration of the supply chain to its regular condition is made possible by good management of these risks, which lessens disruptions and their detrimental effects on performance. For instance, the failure of supply chain management (SCM) at Boeing, Cisco, and Pfizer resulted in losses of $ 2 billion, $ 2.25 billion, and $ 2.8 billion over the course of the last ten years, respectively (Hult et al., 2010 ). Examples of how supply chain failures can lead to financial losses are given by Kern et al. ( 2012 ) and Sodhi et al. ( 2012 ), respectively. Risk management in supply networks, particularly transportation logistics, must be done properly if supply chain operations are to be resilient and sustainable. The heart of risk evaluation techniques, which are a crucial component of supply chain risk management, is the systematic detection, assessment, and mitigation of dangers within supply chain operations. (Tang, 2006 ). Supply chain managers used several approaches to proactively evaluate the possible risk, come out with measures to reduce them and take decisions to improve the smooth operations of companies’ supply chain The risk evaluation processes in supply chains and its consequences for transport logistics have come into sharper focus in the literature as organisations recognise more and more how crucially important effective risk management is to maintaining the continuity of supply chain operations and enhancing competitiveness. To comprehend the efficiency and effectiveness of the risk assessment approaches on transportation logistics operations, most researchers and experts have been developing new approaches to evaluate risk. For instance, Purohit and Kumar (2013) evaluated the intensity, degree, speed, forewarning, and duration of the technological hazards encountered by logistics supply chain organisations in Dubai. Bao Jiang, Jian Li, and Siyi Shen (2018) concentrated on supply chain risk valuation and control in Chinese port firms and proposed strategies to improve supply chain risk management using the Analytic Hierarchy Process (AHP). Elock Sons (2018) indicated that to reduce risks and enhance supply chain performance comes with several techniques. The significance of more study on risk avoidance tactics and risk management models is brought home by this. In a case study on Australian clothing stores, Mamun (2023) outlined significant risks, the need for enhanced assessment methods, and the benefits of proactive risk management strategies. The need of specific organisational structures for supply chain risk management was stressed by Ganiyu, Yu, Xu, and Providence (2020) in their investigation of supply chain risks and their implications on performance for Ghanaian enterprises. There are several possible gaps in the literature that can be found. Significant quantitative research that comprehensively examines the efficacy, productivity, and wider implications of risk evaluation practises in transportation logistics supply chains is lacking. While there is a tonne of qualitative and conceptual literature that explores the topic, empirical studies that provide specific, quantitative data are conspicuously scarce. This lack of quantitative study has had substantial influence on transportation logistics supply chain making it difficult for decision-makers to manage their task. They frequently lack the empirical foundation required to make well-informed judgements on the deployment and optimisation of risk evaluation techniques since there aren't any solid quantitative data. More empirical research is strongly advocated in order to promote efficient risk management, spur innovation, improve communication, and increase the resilience of transportation logistics supply chains in a constantly shifting environment. Also, lack of standardisation in supply chain risk evaluation practises, particularly in the complex setting of transportation logistics, poses a variety of problems. Developing common standards, guidelines and benchmarks will reduce difficulties facing industrial players. Standardisation helps to increase the comparability of research and decision-making on a worldwide level as well as the resilience and effectiveness of supply chains for transportation logistics. In the area of transportation logistics in particular, more research is required on the uniformity of risk evaluation techniques as a lack of standardisation may make it challenging to compare and generalise research findings across studies. Furthermore, the existing studies focus on particular sectors or areas in analyzing the risk assessment methodologies has a considerable influence on supply chain and transportation logistics. This bias slows down the advancement in industrial specific approaches to risk management, reducing the potential for cross-industry knowledge, and prevents the transferability of facts between industries. Making formulation of decisions more difficult by ignoring several factors. The problems in new businesses and underrepresented sectors go unsolved, and regional differences in risk factors are disregarded. This disparity hinders cross-industry and cross-national collaboration, innovation, and successful risk management initiatives. It undermines regulatory frameworks and distorts public perceptions of risk. The incorporation of cutting-edge technology into risk assessment processes, such as big data analytics, IoT, AI, and blockchain, will have a substantial influence on supply chains and transportation logistics. Hence, more study is still required on this issue. The optimisation of operations, decision-making, and resilience are all hampered by this gap, which prevents the realisation of possible technical improvements in risk management. Due to a lack of knowledge about the advantages and difficulties of integrating new technologies, businesses have failed to fully realise their transformational potential. Their capacity to adjust to changing risk circumstances is hampered by this restriction. Supply networks struggle to deal with new risks effectively as a result of being more prone to mistakes and inefficiencies. If one wishes to maintain their competitiveness, make educated judgements, and enable proactive risk mitigation measures in transportation logistics and beyond, they must explore this unexplored region. The absence of conclusive empirical evidence correlating the application of risk evaluation approaches in supply chains to enhanced efficiency in transportation logistics has profound implications. It is challenging to establish a clear connection between the effects of risk assessment methodology on operational effectiveness, resilience, and supply chain performance as a whole because of this mismatch. Decision-makers may find it difficult to justify expenditures in risk evaluation programmes and comprehend their concrete advantages in the absence of such evidence. Certain details about the application of risk-based techniques in the formulation of transportation logistics strategy and the overall effectiveness of the supply chain will undoubtedly require more study. This robust backing will not only help with decision-making but also advance our knowledge of how crucial risk assessment is to producing the best outcomes for business operations. We might be able to comprehend risk management strategies better by filling in the gaps in the literature on supply chain risk evaluation techniques and their effects on transportation logistics. Organisations will be able to do this through extending standard approaches, overcoming industry and regional variances, using new technologies, and connecting risk management procedures to improvements in transportation logistics and supply chain efficiency. These developments will provide enterprises the tools they need to manage risks in advance, improve production resilience, and gain an advantage over rivals in the quickly expanding supply chain management sector. The study's goals include a thorough evaluation of the efficacy of the current risk evaluation techniques and the challenges or limitations encountered in using risk evaluation procedures in Transport Logistics supply chain. 2. Methodology 2.1 Research Philosophy and Design Approach The study is aligned to the positivist research philosophy along side descriptive research design. Both approaches allow for the collection and analysis of quantitative data in an objective way. This approach can be useful when studying phenomena that can be measured and observed in a quantifiable way. The study adopted a quantitative approach. The justification for utilising a quantitative approach in this research is based on its strong connection with the research objectives, which centre on the assessment of risk evaluation techniques and their influence on transport logistics. Quantitative approaches provide a systematic and complete approach to investigating these aims using numerical data and statistical studies. Furthermore, this technique aligns with well-established approaches in the field of transportation logistics and supply chain risk evaluation, as established by prior studies conducted by Lai et al. (2017), Bode et al. (2011), and Soni & Kodali (2019). Through the utilisation of quantitative measures and the application of statistical rigour, the selected approach guarantees the study's outcomes to be robust, reliable, and directly relevant to decision-making within the field of transport logistics. 2.2 Research Design The study adopted cross-sectional study research design. This design allows researchers to effectively capture a simultaneous representation of prevailing practises and outcomes, which is an essential requirement for efficiently addressing the study surveys. Moreover, the choice to utilise a cross-sectional design is substantiated by methodological evidence found in previous studies conducted in the fields of transportation logistics (Finck et al., 2022) and supply chain risk (Manuj & Mentzer, 2008; Mamun, 2023), which emphasizes its alignment with established conventions in the discipline. Through the effective gathering of data that is pertinent to the specific subject of the study, this design strengthens the significance, timeliness, and ability of the research to provide insights into crucial matters pertaining to the evaluation of supply chain risks within the field of transportation logistics. 2.3 Population, Sampling and Sample Size The target population was made up of supply chain specialists, logistics directors, and business executives with knowledge and experience in risk assessment techniques and logistics of transportation. According to Pournader et al. (2000) and Katsaliaki et al. (2000), this type of population was chosen based on the applicability of their ideas and their participation in the implementation and management of supply chain and logistics operations. The researchers selected a sample size of 273 for this investigation, considering the available resources and the necessary level of precision. To assure the representativeness of the findings across all industries and company sizes, a stratified random sample approach was utilised. The utilisation of this method not only serves to mitigate bias but also amplifies the practicality of the research findings (Hair et al., 2019 ; Bryman, 2016 ). The determination of the sample size in this study demonstrates a careful consideration of both attaining a satisfactory level of accuracy and efficiently allocating the available resources. According to the industry and size of the company, a population is divided into subgroups using the stratified random sampling technique. According to industry type (manufacturing, retail, healthcare) and firm size (small, medium, large), the researchers divided the population into subgroups. Within each stratum (industry and firm size category), the researchers allocated a proportionate number of respondents to ensure that each subgroup is represented adequately in the final sample. This step minimized the potential for bias and enhanced the external validity of the study. Within each stratum, respondents were selected using random sampling techniques. This ensured that each member of the target population within a specific subgroup had an equal chance of being included in the study. After the researchers identified potential respondents within each stratum, the research team employed data collection tool such as questionnaire to gather information relevant to the study. Consequently, this method represents a methodologically robust selection for the research endeavour. 2.4 Data Collection Through surveys, primary data was gathered. Questionnaire in the form of likert scale was used to source data from the respondents about the efficiency of risk evaluation processes and their effect on the performance of transport logistics (Creswell & Creswell, 2018 ; Saunders et al., 2018). In addition, the secondary data from academic and industry reports, and relevant databases. This information was added to the backdrop, theoretical background, and market trends around risk assessment techniques and transportation logistics (Eisenhardt, 1989; Yin, 2018). To the predetermined sample of supply chain experts and logistics managers, the question was administered through face-to-face interview with the respondents. In order to increase response rates, a mix of personalised email invites and reminders will be sent (Dillman et al., 2014; Vannieuwenhuyze et al., 2016). 2.5 Data Analysis Following the completion of data collection, a thorough data analysis approach was conducted in order to provide meaningful findings and fulfil the objectives of the study. The data was analysed using the Statistical Package for the Social Sciences (SPSS) in order to assess the benefits involved from implementing risk evaluation procedures and the challenges in transportation logistics supply chain. Copyright license for the SPSS software was obtained through the License Authorisation Wizard. Descriptive statistics, including measures of central tendency such as means, as well as frequencies and percentages, will be computed. In order to examine the connections between variables and assess the importance of these interactions, inferential statistical methods such as regression analysis and correlation analysis will be employed (Hair et al., 2019 ; Bryman, 2016 ). 2.6 Ethical Considerations The study conformed to all the relevant ethical considerations pertinent to research involving human. Before any data was collected, all participants' informed consent was sought. All during the study, participants' confidentiality, privacy, and anonymity was maintained. During analysis and reporting, all personally identifiable information was taken out of the data. To protect the ethical integrity of the study, ethical permission was requested from the appropriate institutional review board (Creswell & Creswell, 2018 ; Saunders et al., 2018). 3. Results 3.1 Demographic Characteristics Table 1 Demographic Characteristics Items Counts % of the Total Gender Male 177 64.8% Female 96 35.2% Marital status Single 129 47.3% Married 129 47.3% Divorced 12 4.4% Widowed 3 1.1% Educational level HND/Diploma 3 1.1% First degree 246 90.1% Post-graduate 24 8.8% Age range 20–30 years 12 4.4% 31–40 years 90 33.2% 41–50 years 102 37.6% 51–60 years 67 24.7% Source: Field data, 2023 Table 1 shows a gender imbalance in the population of the survey is shown by the examination of demographic data, which indicates that 64.8% of respondents are men. With 47.3% of people in each marital status, the distribution of marital status is fairly equal. The age distribution is diversified, with substantial percentages in the 41–50-year (37.6%) and 31–40-year (33.2%) age groups, indicating a highly educated population, and the preponderance of first-degree holders (90.1%). Age-related studies are made possible by this age diversity. It is advised to use a questionnaire that combines multiple-choice, open-ended, and Likert scale questions to collect thorough quantitative and qualitative data in order to assess the effect of risk evaluation processes on transport logistics performance. 3.2 The Effectiveness and Efficiency of Existing Risk Evaluation Procedures in Supply Chains and their Impact on Transport Logistics Performance Table 2 Responses on Frequency of Risk Evaluation Procedures Conducted Levels Counts % of Total Cumulative % Daily 3 1.1% 1.1% Weekly 12 4.4% 5.5% Quarterly 22 8.1% 13.6% Annually 12 4.4% 17.9% Dont know 224 82% 100% Source: Field data, 2023 How frequently risk evaluation processes are carried out is shown in Table 2 . According to the distribution, 1.1% of respondents do procedures daily, 4.4% perform them weekly, 8.1% perform them quarterly, and 4.4% perform them yearly. When asked how frequently these operations are performed, 82% of respondents said they were unsure. This distribution illustrates the respondents' diverse familiarity and participation with the frequency of risk evaluation procedures. Table 3 Risk evaluation techniques are currently employed Qualitative risk assessment Quantitative risk assessment Scenarios analysis Others N 273 273 273 273 Mean 3.29 3.21 4.99 4.63 Median 4 3 5 5 Standard deviation 1.39 1.38 0.121 0.899 Variance 1.93 1.91 0.0147 0.808 Range 4 5 2 5 From Table 3 , the respondents exhibit a spectrum of preferences, with Scenarios Analysis (mean = 4.99) and Others (mean = 4.63) emerging as the most favored techniques. Notably, Scenarios Analysis stands out with the highest mean score, underscoring its pivotal role in conducting thorough risk assessments. Similarly, the category Others (supply chain mapping, real-time Monitoring and tracking, contingency planning, supply chain diversification) encompasses alternative risk assessment methods that hold significant relevance. Qualitative Risk Assessment and Quantitative Risk Assessment are also in use, garnering mean scores of 3.29 and 3.21 respectively, indicating their considerable but relatively slightly lower adoption compared to scenarios analysis and other techniques. The alignment between median values and mean values reinforces the consistency of the respondents' preferences. This sheds light on the variety of risk assessment techniques used, highlighting a preference for thorough methodology like Scenario research and highlighting the applicability of the Others categorization, which may include unique or specialised methods. Table 4 Frequencies of the current risk evaluation procedures communicated Levels Counts % of Total Written reports 33 12.1% Dashboards and visualizations 63 23.2% Meetings and presentations 141 51.7% Other 36 13.2% Source: Field data, 2023 The Table 4 shows the various methods for communicating procedures within the organizations. Meetings and Presentations emerge as the most prevalent mode, accounting for 51.7% of the responses. This shows that discussions of the results of risk evaluations are greatly influenced by in-person interactions. Dashboards and Visualizations also play a substantial role, with 23.2% of respondents indicating their use. This is probably due to the growing emphasis on effectively communicating complicated facts through visualisation. Written Reports are the chosen mode for 12.1% of respondents, indicating a traditional approach to documentation. Additionally, Other is chosen by 13.2% of respondents, suggesting that there might be diverse and context-specific communication methods beyond the provided options. These findings highlight the complexity of risk communication as well as the need of participatory settings like meetings and visualisation tools. Table 5 Gender and Effectiveness of the current risk evaluation procedures Gender Less effective Fairly effective Neutral Very effective effective Total χ² Tests P-value Male 27 12 21 84 33 177 11.8 0.019 Female 27 12 12 30 15 96 Total 54 24 33 114 48 273 Source: Field data, 2023 A thorough breakdown of answers across all efficacy levels and gender is shown in Table 5 . Among male participants, a significant number of 84 respondents indicated that the procedures were categorized as Very Effective, while 27 respondents found them Less Effective. Notably, 12 males responded positively to the Fairly Effective category, and 21 expressed a Neutral viewpoint. Additionally, 33 males deemed the procedures Effective. In contrast, the pattern diverged among female participants. Specifically, 30 respondents rated the procedures as Very Effective, whereas 27 found them Less Effective. The Fairly Effective category garnered 12 positive responses from females. Further, 12 females indicated a Neutral stance, while 15 were categorized as Effective. The χ² test, yielding a calculated value of 11.8 with 4 degrees of freedom, yields a p-value of 0.019. The p-value shows a strong correlation between gender and the efficacy of risk assessment techniques. Individuals' perceptions of these procedures are greatly influenced by gender, highlighting the significance of taking gender-specific aspects into account when evaluating risk. This demonstrates the necessity of include gender-related viewpoints in risk assessment methodologies. Table 6 Educational Level and the effectiveness of the current risk evaluation procedures educational level Less effective Fairly effective Neutral Very effective effective Total χ² Value P-value HND/Diploma 0 0 0 3 0 3 23.9 0.002 First degree 54 24 33 96 39 246 Post-graduate 0 0 0 15 9 24 Total 54 24 33 114 48 273 Source: Field data, 2023 Based on the respondents' educational backgrounds, this table provides a thorough breakdown of the responses spread across different efficacy levels (Table 6 ). Notably, within the HND/Diploma group, all respondents reported considering the procedures Very Effective. Among respondents with a First Degree, 54 participants viewed the procedures as Less Effective, 24 as Fairly Effective, 33 as Neutral, 96 as Very Effective, and 39 as Effective. In the Post-graduate category, 15 respondents regarded the procedures as Very Effective, and 9 as Effective. The computed χ² test statistic demonstrated a substantial outcome, reaching a value of 23.9, and with 8 degrees of freedom, it yielded a p-value of 0.002. The p-value demonstrates a strong relationship between respondents' educational backgrounds and how well they believe risk assessment processes are working. This emphasises how vital it is to take into account participants' credentials while assessing these procedures, as their level of education has a big influence on how they perceive things. Table 7 The main benefits observed from implementing risk evaluation procedures in Transportation Logistics supply chain Statistic df p Effect Size Cost reduction Student's t 33.2 272 < .001 Cohen's d 2.01 Wilcoxon W 37401 < .001 Rank biserial correlation 1.00 Improved supply chain resilience Student's t 40.6 272 < .001 Cohen's d 2.46 Wilcoxon W 37401 < .001 Rank biserial correlation 1.00 Better decision-making Student's t 62.4 272 < .001 Cohen's d 3.78 Wilcoxon W 37401 < .001 Rank biserial correlation 1.00 Enhanced customer satisfaction Student's t 54.1 272 < .001 Cohen's d 3.27 Wilcoxon W 37401 < .001 Rank biserial correlation 1.00 Increased operational efficiency Student's t 210.5 272 < .001 Cohen's d 12.74 Wilcoxon W 37401 < .001 Rank biserial correlation 1.00 Source: Field data, 2023 The One Sample T-Test results provide important information on the key benefits of applying supply chain risk evaluation techniques. For the benefit of Cost reduction, the Student's t-statistic was 33.2 with 272 degrees of freedom, yielding an extremely low p-value of < .001. The effect size, measured by Cohen's d, was substantial at 2.01. Similarly, for Improved supply chain resilience, the student’s t-statistic was 40.6 with a p-value < .001, and Cohen's d was 2.46. Better decision-making also showed a strong effect, with a Student's t-statistic of 62.4 and a p-value < .001, accompanied by a large Cohen's d of 3.78. Enhanced customer satisfaction had a student’s t-statistic of 54.1 with a p-value < .001, and Cohen's d was 3.27. Lastly, increased operational efficiency demonstrated an immensely significant result, with a student’s t-statistic of 210.5 and a p-value < .001, and a considerable Cohen's d of 12.74. The Wilcoxon W tests further supported the statistical significance and effect sizes, with all tests showing p-values < .001 and a Rank biserial correlation of 1.00 for each benefit. These findings underscore the substantial positive impact of implementing risk evaluation procedures in the supply chain across various dimensions. Table 8 The key challenges or limitations encountered in using risk evaluation procedures in Transport Logistics supply chain Statistic ±% df p Effect Size Lack of data availability Student's t 120.0 272 < .001 Cohen's d 7.26 Bayes factor₁₀ 1.63e + 233 7.33e-238 Wilcoxon W 37401 < .001 Rank biserial correlation 1.00 Difficulty in risk identification Student's t 52.1 272 < .001 Cohen's d 3.16 Bayes factor₁₀ 4.52e + 139 6.46e-148 Wilcoxon W 37401 < .001 Rank biserial correlation 1.00 Insufficient resources for risk evaluation Student's t 67.4 272 < .001 Cohen's d 4.08 Bayes factor₁₀ 4.63e + 167 2.50e-172 Wilcoxon W 37401 < .001 Rank biserial correlation 1.00 Limited employee engagement and commitment Student's t 71.3 272 < .001 Cohen's d 4.31 Bayes factor₁₀ 6.94e + 173 3.97e-181 Wilcoxon W 37401 < .001 Rank biserial correlation 1.00 Inadequate integration of risk evaluation into decision-making Student's t 61.4 272 < .001 Cohen's d 3.72 Bayes factor₁₀ 2.31e + 157 1.09e-162 Wilcoxon W 37401 < .001 Rank biserial correlation 1.00 Source: Field data, 2023 The results of the One Sample T-Test provided a description of the major difficulties or restrictions associated with supply chain risk evaluation techniques. For the challenge of Lack of data availability, the student’s t-statistic was notably high at 120.0, with 272 degrees of freedom, resulting in an extremely low p-value of < .001. The effect size, as measured by Cohen's d, was substantial, registering at 7.26. The Bayes factor₁₀ also indicated overwhelming evidence in support of the findings. Similarly, for Difficulty in risk identification, the student’s t-statistic was 52.1, with a p-value < .001, and Cohen's d was 3.16, indicating a significant effect size. The Bayes factor₁₀ further supported the statistical significance. Insufficient resources for risk evaluation had a student’s t-statistic of 67.4, a p-value < .001, and Cohen's d of 4.08, indicating a substantial effect size. Limited employee engagement and commitment showed a student’s t-statistic of 71.3, a p-value < .001, and Cohen's d of 4.31, denoting a substantial effect size. The challenge of Inadequate integration of risk evaluation into decision-making demonstrated a student’s t-statistic of 61.4, a p-value < .001, and Cohen's d of 3.72, signifying a significant effect size. The Wilcoxon W tests further supported the statistical significance and effect sizes for all challenges, with p-values < .001 and a Rank biserial correlation of 1.00 for each challenge. These findings emphasise the important challenges faced when applying risk evaluation techniques in the supply chain and place emphasis on the areas that need attention and development. 4. Discussion of the Findings 4.1 The Effectiveness and Efficiency of Existing Risk Evaluation Procedures in Supply Chains The outcomes of this study resonate with the literature on supply chain risk management. Garvey & Carnovale ( 2020 ) highlight the nuanced impact of risk factors on supply chain structures, suggesting that the distribution of response frequencies in this study may stem from the diverse nature of risks and their variable effects on different supply chain aspects. Fan et al. ( 2017 ) and Kauppi et al. ( 2016 ) provide insights into the preferred risk assessment methods and holistic risk management approaches, which align with the study's emphasis on the prominence of Scenario Analysis. The communication modes identified in this study align with the findings of previous research. Meetings and presentations as dominant modes of communication resonate with the interpersonal and interactive nature of risk discussions, as suggested by Simoa et al. (2016) and Wiengarten (2016). The increasing role of dashboards and visualizations is consistent with the trend towards using visual aids for conveying complex risk data, in line with Fugate et al. ( 2010 ). The significance of gender and educational levels on perceived effectiveness of risk evaluation procedures corresponds with existing research on the influence of individual characteristics on risk perceptions. Similar to the outcomes of Zhao et al. ( 2013 ) and Kwak et al. ( 2018 ), this study acknowledges the impact of gender and education on risk assessment perceptions, emphasizing the need to consider these factors in risk management strategies. In terms of the implications, the positive impact of risk evaluation procedures on cost reduction aligns with the financial benefits suggested by Fan et al. ( 2017 ) and the notion that proactive risk management can lead to cost savings. The link between risk evaluation and supply chain resilience supports the emphasis on resilience strategies in Simoa et al. (2016) and the need for continuity in uncertain environments highlighted by Wiengarten (2016). The alignment between risk evaluation and decision-making effectiveness resonates with the strategic value of data-driven insights emphasized in Fugate et al. ( 2010 ) and Kauppi et al. ( 2016 ). The connection between risk evaluation and enhanced customer satisfaction is consistent with the customer-centric focus discussed by Zhao et al. ( 2013 ) and the importance of customer experiences in Simoa et al. (2016). Finally, the impact of risk evaluation on operational efficiency is in line with the notion of streamlining processes and optimizing operations discussed by Wang et al. ( 2018 ) and Hsieh et al. ( 2018 ). Together, the study's findings and their alignment with existing literature reinforce the multidimensional advantages of robust risk evaluation procedures for supply chain management and organizational performance. 4.2 Key Challenges or Limitations Encountered in Using Risk Evaluation Procedures The challenges associated with risk evaluation procedures are well-documented in the literature and addressing them is indeed crucial for realizing the benefits outlined above. Garvey & Carnovale ( 2020 ) emphasize the significance of data availability in risk assessment, highlighting the need for robust data collection to accurately evaluate risks. Wang et al. ( 2018 ) discuss the difficulty of risk identification, suggesting that collaboration across departments, as mentioned by Fan et al. ( 2017 ) and Kauppi et al. ( 2016 ), is essential to comprehensively recognize risks. The challenge of resource constraints is acknowledged in the literature. Smith & Morrato ( 2014 ) emphasize the importance of efficient resource allocation in risk-minimization initiatives. By strategically allocating resources to new ways, Wang et al. ( 2020 ) highlight the significance of logistics innovation in reducing supply chain risks. Smith & Morrato ( 2014 ) emphasise the significance of employee involvement in risk management in the context of pharmaceutical risk-minimization activities. Wiengarten (2016) emphasizes the significance of organizational culture in integrating supplier integration practices with risk management, highlighting the role of communication and engagement. The challenge of integrating risk evaluation into decision-making aligns with findings from multiple studies. Hsieh et al. ( 2018 ) highlight the practical implementation of risk evaluation models, emphasizing their integration into decision-making. Kwak et al. ( 2018 ) stress the importance of holistic risk analysis, which requires integrating risk elements and interactions. This provides valuable insights into the challenges of risk evaluation procedures and offers strategies that organizations can adopt to address these challenges effectively. By learning from these studies, organizations can develop more resilient and effective risk management practices that contribute to improved supply chain performance. 4.3 Implications for Best Practices and Guidelines The analysis on the creation of best practices and recommendations for risk assessment and management in supply networks is in good agreement with the literature already available in the area of risk management and supply chain operations. The emphasis on effective methodologies, comprehensive risk identification and mitigation, and alignment with broader supply chain strategies resonates with the findings of Fan et al. ( 2017 ) regarding the positive impact of risk-sharing mechanisms on operational performance. This suggests that a holistic approach to risk management, involving collaboration and alignment with broader strategies, is preferred for achieving optimal outcomes. The incorporation of developing technologies like AI and IoT into risk assessment processes highlights the dynamic character of risk management, which is reflected in the recognition of emerging technologies and cooperation with regulatory agencies. This is consistent with Liu et al.'s ( 2017 ) observations when they proposed a technique combining suppliers' risk assessment based on material flow analysis, stressing the significance of creative approaches. The implications of standardizing risk evaluation procedures in transportation logistics supply chains are also well-supported by the literature. The idea that standardization ensures a unified approach to risk assessment and promotes transparency resonates with the findings of Kwak et al. ( 2018 ), who developed a comprehensive risk analysis model that considers risk elements and their interactions, enhancing transparency and consistency in risk evaluation. Similarly, the concept that standardization facilitates benchmarking and collective response to challenges is in line with the observations of Smith & Morrato ( 2014 ), who recommended utilizing models and frameworks to guide successful outcomes. The acknowledgement of challenges in achieving standardization and the importance of collaborative efforts and open communication align with the findings of Wiengarten (2016) in the context of supplier integration practices. This suggests that addressing challenges in standardization involves fostering collaboration and effective communication among stakeholders. The analysis provides insights that are consistent with the existing literature, emphasizing the importance of best practices, holistic approaches, technology integration, and standardization in risk assessment and management in transportation logistics supply chains. 4.4 The impact of risk evaluation procedures on transport logistics performance The examination of the impact of risk evaluation procedures on transport logistics performance, as evidenced by this analysis, closely mirrors the established body of literature, illuminating the intricate relationship between distinct risk assessment methodologies and the holistic effectiveness of transport logistics operations. The substantial and positive influence of Scenario Analysis on transport logistics performance is harmonious with the discoveries by Garvey & Carnovale ( 2020 ), who underscore the necessity of encompassing diverse scenarios in risk appraisal. This concurrence aligns with the prevailing notion that delving into potential outcomes and devising strategies for multiple eventualities substantially enhances an organization's adeptness in confronting disruptions, as echoed by Kwak et al. ( 2018 ). The affirmative impact of Quantitative Risk Assessment on transport logistics performance gains further affirmation from the practical evidence offered by Wang et al. ( 2018 ), which establishes a link between logistics competence, the uncertainty of supply chains, and risk. Organizations that strategically leverage quantitative data and rigorous calculations for risk evaluation stand better poised to arrive at judicious decisions, supported by a more accurate grasp of potential risks, as demonstrated in the work of Fan et al. ( 2017 ). The robustly significant positive correlation between Qualitative Risk Assessment and transport logistics performance resonates harmoniously with the observations of Simoa et al. (2016), who dissect the consequences of logistics and packing postponement strategies. This alignment underscores the significance of harnessing the expertise and qualitative insights of seasoned professionals for discerning and addressing crucial risks that might elude quantification yet hold pivotal relevance for logistics operations. The apparent absence of statistically significant impact of Supply Chain Mapping on transport logistics performance adheres to the perspective that visualization techniques may not invariably translate into direct performance enhancements. This outcome underscores the nuanced and intricate interplay between visualization tools and logistical performance, as articulated by Zhao et al. ( 2013 ) in their study on the interrelation between supply chain integration and company performance. The positive consequence of Root Cause Analysis on transport logistics performance harmonizes with the findings of Wang et al. ( 2020 ), who dissect the role of innovation in logistics capability in mitigating supply chain risks. The significance of identifying the root causes of issues and implementing targeted resolutions aligns well with the observations of Kauppi et al. ( 2016 ), emphasizing the critical nature of addressing challenges at their core for sustainable improvements in performance. The lack of apparent statistically significant effect of Real-time Monitoring and Tracking on transport logistics performance converges with the multifaceted nature of integrating technology, as underscored in the discourse on emerging technologies. This result acknowledges the intricate dynamics at play in the implementation and impact of real-time monitoring, a fact that Holzmeister & Stefan ( 2019 ) highlight in their study on risk preferences. The affirmative impact of Supply Chain Diversification on transport logistics performance echoes the concept of bolstering supply chain resilience, as expounded by Wiengarten (2016). This finding aligns well with the idea that diversified supply chains fortify an organization's capacity to navigate disruptions, as also emphasized by Page et al. ( 2018 ) in their study concerning risk assessment related to reporting biases.To conclude, this analysis augments our understanding by affirming the correlation between varied risk assessment techniques and transport logistics performance, in harmony with the existing literature. These findings, characterized by their alignment with prior research, provide invaluable insights for organizations endeavoring to optimize risk assessment strategies and elevate the efficiency of logistics operations. 5. Conclusions A deep understanding of Supply chain dynamics requires the adoption of a comprehensive approach to risk assessment that include cutting-edge technology, standardised practices, best practices and stakeholder participation come with the challenging nature of systematic risk assessment on productivity, adaptability, and competitiveness. The strides to successful risk management in the transportation logistics supply chain management necessitates resourcefulness, collaboration and commitment to excellence. The results of this study could render supply chains more effective and adequately prepared for transportation logistics in a connected and unpredictably dynamic world, which would be highly beneficial to supply chains for transportation logistics. 6. Recommendations Organisations should proactively engage in developing technologies like Big Data analytics, IoT, AI, and Blockchain given the favourable image of these systems. These kinds of emerging technologies would greatly increase the accuracy of risk assessment, decision-making, and real-time monitoring. In managing the risks of a dynamic supply chain, strategically integrating them might give an advantage. Organisation should combine qualitative, quantitative methods. Scenario analysis for a comprehensive risk assessment, they should adopt to enhance their potential benefits in risk management. Also, the improvement of real-time monitoring and tracking systems in transportation logistics is very vital. While the study found that real-time monitoring was not strongly linked to changes in logistics performance, enhancing these systems may provide more timely insights into emerging risks. 7. Limitations Despite the study findings and conclusion drawn it come limitations to be mindful of which include the limited sample size of the study and potential self-reporting bias, both of which may affect the generalization of the results. Also, it's feasible that the analysis's static strategy and industry-specific concentration could accurately dazzling the dynamic changes in risk management in the transport logistics. The study encourages the requisite the embracing of best practices, new know-hows, and diverse risk assessment approaches. It also backs for these wide-ranging approaches. Future Research on the longitudinal studies on the advancing environment of risk management practices on sustainable and environmental factors within the transport logistic. Declarations Author contributions LEY wrote the main manuscript and ROM and DOA assisted in the preparation of the tables and figures. All authors reviewed the manuscript. Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request Ethics approval and consent to participate Ethical review and approval were waived by Takoradi Technical University (TTU) Research Ethics Committee for the study. This was due to anonymity of interviewees and also no sensitive data were collected. All procedures for data collection were treated with confidentiality according to TTU’s Declaration on ethics. Informed consent was sought from all individual participants (respondents) included in the study. They were informed that the survey was anonymous and that participation was voluntary. Funding No funding was received to assist with the preparation of the manuscript Competing interests The authors declare no competing interests. Clinical Trial Number : not applicable References Braun, V., & Clarke, V. (2019). Thematic analysis. In H. Cooper, P. M. Camic, D. L. Long, A. T. 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K., Srivastava, P. R., Kumar, A., Jindal, A., & Gupta, S. (2021). Supply chain vulnerability assessment for manufacturing industry. Annals of Operations Research. https://doi.org/10.1007/s10479- 021-04155-4 Sheffi, Y. (2005). The resilient enterprise: Overcoming vulnerability for competitive advantage. MIT Press. Simão, L., Gonçalves, M., & Rodriguez, C. (2016). An approach to assess logistics and ecological supply chain performance using postponement strategies. Ecological Indicators, 63, 398-408. https://doi.org/10.1016/J.ECOLIND.2015.10.048. Smith, M., & Morrato, E. (2014). Advancing the Field of Pharmaceutical Risk Minimization Through Application of Implementation Science Best Practices. Drug Safety, 37, 569 - 580. https://doi.org/10.1007/s40264-014-0197-0. Sodhi, M. S., Son, B., & Tang, C. (2012). Researchers’ perspectives on supply chain risk management. Production and Operations Management, 21(1), 1-13. http://dx.doi. org/10.1111/j.1937-5956.2011.01251.x. Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451-488. Wagner, S. M., & Bode, C. (2008). An empirical examination of supply chain performance along several dimensions of risk. Journal of Business Logistics, 29(1), 307–325. Wang, M., Asian, S., Wood, L., & Wang, B. (2020). Logistics innovation capability and its impacts on the supply chain risks in the Industry 4.0 era. , 2, 83-98. https://doi.org/10.1108/mscra-07-2019-0015. Wang, M., Jie, F., & Abareshi, A. (2018). Logistics Capability, Supply Chain Uncertainty and Risk, and Logistics Performance: An Empirical Analysis of Australian Courier Industry. , 11, 45-54. https://doi.org/10.31387/OSCM0300200. Wiengarten, F., Humphreys, P., Gimenez, C., & McIvor, R. (2016). Risk, risk management practices, and the success of supply chain integration. International Journal of Production Economics, 171, 361-370. https://doi.org/10.1016/J.IJPE.2015.03.020. World Economic Forum. (2020). The Global Risk Report. http://www3.weforum.org/docs/WEF_Global_ Risk_Report_2020.pdf Zhao, L., Huo, B., Sun, L., & Zhao, X. (2013). The impact of supply chain risk on supply chain integration and company performance: a global investigation. Supply Chain Management, 18, 115-131. https://doi.org/10.1108/13598541311318773. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6691764","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":462596852,"identity":"471b14fd-5b17-4e90-adda-b6fb7618d476","order_by":0,"name":"Lord Emmanuel Yamoah","email":"","orcid":"","institution":"Takoradi Technical University","correspondingAuthor":false,"prefix":"","firstName":"Lord","middleName":"Emmanuel","lastName":"Yamoah","suffix":""},{"id":462596853,"identity":"bcd87745-9e43-458c-860f-e3686aaec9ca","order_by":1,"name":"Ronald Osei Mensah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYLCCB2wQmpmhAsKQAOIEvFoS4FrOGEiQqIWxjQgt5u29Bz8klDFE80u3P/xcOO9PncEB5oO3eRhq83BpkTlzLlki4RxD7sw5Z4ylZ24zkDA4wJZszcNwvBiXFgmJHAOJxDaG3A03chikecFaeMykeRiOJTbg1mL8A6Rl/430x79554C08H8jpMUMYotEgpk0bwPYFjaglhrcWnjOmFkknJPInXEjx8ya55ix5MzDbMaWcwwO4NbC3mN840OZTW7/jPTHt3lq5Pj5jjc/vPGmog6nFphOJDYziDA4jF8DNlBHupZRMApGwSgYrgAApDBRuL8NWDIAAAAASUVORK5CYII=","orcid":"","institution":"Takoradi Technical University","correspondingAuthor":true,"prefix":"","firstName":"Ronald","middleName":"Osei","lastName":"Mensah","suffix":""},{"id":462596855,"identity":"283df579-747b-4807-8b1a-9685ec4db287","order_by":2,"name":"Daniel Opoku-Akyea","email":"","orcid":"","institution":"Ghana Technology University College","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Opoku-Akyea","suffix":""}],"badges":[],"createdAt":"2025-05-18 12:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6691764/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6691764/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87734215,"identity":"4f583e7f-d240-4258-b359-8a1008ef3ec8","added_by":"auto","created_at":"2025-07-28 12:09:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1284178,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6691764/v1/c305d486-98bc-42ca-91d5-7f44e7028d34.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the Benefits and Challenges in Transportation Logistics: Testing the Effectiveness and Efficiency of Existing Risk Evaluation Procedures in Supply Chain ","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSupply chains are intricate networks that encompass the movement of goods, services, information, and funds across various stages, from procurement to production to distribution (Christopher, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A significant issue has been the risk that reduces the supply chain's effectiveness. By easing the flow of commodities from suppliers to manufacturers, distributors, retailers, and finally to end customers, transportation logistics, which entail the actual movement of goods, resources, and products, play a crucial role in supply chains (Christopher, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe rising unpredictability and complexity of global supply networks are to blame for the striking rise in supply chain risks and disturbances that researchers have seen in recent years (Ivanov, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Katsaliaki et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pournader et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to Choi et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the business environment has changed dramatically since the past and is predicted to change even more quickly in the future. Natural catastrophes, political unrest, local instability, and epidemics are only a few of the possible dangers that come under the category of catastrophic events (Wagner \u0026amp; Bode, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The issue of risk has been a major concern due to their linkage to other supply chain problem. Even though these \"black swan\" events are unlikely, they can have a disastrous effect. However, it is essential to consider risks of all sizes and varieties as part of a thorough supply chain risk management (SCRM) strategy. Any risk, whether it be a queue halt or the delivery of the incorrect item, can cause significant disruptions and have an influence on other risks. This highlights the need of proper risk analysis. The \"Global Risk Report 2020\" (World Economic Forum, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which listed 30 dangers that have the potential to have a substantial impact on countries and emphasised their interconnectivity, also recognised the interconnectedness of threats.\u003c/p\u003e \u003cp\u003eHendricks et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) contend that prompt restoration of the supply chain to its regular condition is made possible by good management of these risks, which lessens disruptions and their detrimental effects on performance. For instance, the failure of supply chain management (SCM) at Boeing, Cisco, and Pfizer resulted in losses of \u003cspan\u003e$\u003c/span\u003e2\u0026nbsp;billion, \u003cspan\u003e$\u003c/span\u003e2.25\u0026nbsp;billion, and \u003cspan\u003e$\u003c/span\u003e2.8\u0026nbsp;billion over the course of the last ten years, respectively (Hult et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Examples of how supply chain failures can lead to financial losses are given by Kern et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Sodhi et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), respectively. Risk management in supply networks, particularly transportation logistics, must be done properly if supply chain operations are to be resilient and sustainable. The heart of risk evaluation techniques, which are a crucial component of supply chain risk management, is the systematic detection, assessment, and mitigation of dangers within supply chain operations. (Tang, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Supply chain managers used several approaches to proactively evaluate the possible risk, come out with measures to reduce them and take decisions to improve the smooth operations of companies\u0026rsquo; supply chain\u003c/p\u003e \u003cp\u003eThe risk evaluation processes in supply chains and its consequences for transport logistics have come into sharper focus in the literature as organisations recognise more and more how crucially important effective risk management is to maintaining the continuity of supply chain operations and enhancing competitiveness. To comprehend the efficiency and effectiveness of the risk assessment approaches on transportation logistics operations, most researchers and experts have been developing new approaches to evaluate risk. For instance, Purohit and Kumar (2013) evaluated the intensity, degree, speed, forewarning, and duration of the technological hazards encountered by logistics supply chain organisations in Dubai. Bao Jiang, Jian Li, and Siyi Shen (2018) concentrated on supply chain risk valuation and control in Chinese port firms and proposed strategies to improve supply chain risk management using the Analytic Hierarchy Process (AHP). Elock Sons (2018) indicated that to reduce risks and enhance supply chain performance comes with several techniques. The significance of more study on risk avoidance tactics and risk management models is brought home by this. In a case study on Australian clothing stores, Mamun (2023) outlined significant risks, the need for enhanced assessment methods, and the benefits of proactive risk management strategies. The need of specific organisational structures for supply chain risk management was stressed by Ganiyu, Yu, Xu, and Providence (2020) in their investigation of supply chain risks and their implications on performance for Ghanaian enterprises. There are several possible gaps in the literature that can be found.\u003c/p\u003e \u003cp\u003eSignificant quantitative research that comprehensively examines the efficacy, productivity, and wider implications of risk evaluation practises in transportation logistics supply chains is lacking. While there is a tonne of qualitative and conceptual literature that explores the topic, empirical studies that provide specific, quantitative data are conspicuously scarce. This lack of quantitative study has had substantial influence on transportation logistics supply chain making it difficult for decision-makers to manage their task. They frequently lack the empirical foundation required to make well-informed judgements on the deployment and optimisation of risk evaluation techniques since there aren't any solid quantitative data. More empirical research is strongly advocated in order to promote efficient risk management, spur innovation, improve communication, and increase the resilience of transportation logistics supply chains in a constantly shifting environment.\u003c/p\u003e \u003cp\u003eAlso, lack of standardisation in supply chain risk evaluation practises, particularly in the complex setting of transportation logistics, poses a variety of problems. Developing common standards, guidelines and benchmarks will reduce difficulties facing industrial players. Standardisation helps to increase the comparability of research and decision-making on a worldwide level as well as the resilience and effectiveness of supply chains for transportation logistics. In the area of transportation logistics in particular, more research is required on the uniformity of risk evaluation techniques as a lack of standardisation may make it challenging to compare and generalise research findings across studies. Furthermore, the existing studies focus on particular sectors or areas in analyzing the risk assessment methodologies has a considerable influence on supply chain and transportation logistics. This bias slows down the advancement in industrial specific approaches to risk management, reducing the potential for cross-industry knowledge, and prevents the transferability of facts between industries. Making formulation of decisions more difficult by ignoring several factors. The problems in new businesses and underrepresented sectors go unsolved, and regional differences in risk factors are disregarded. This disparity hinders cross-industry and cross-national collaboration, innovation, and successful risk management initiatives. It undermines regulatory frameworks and distorts public perceptions of risk.\u003c/p\u003e \u003cp\u003eThe incorporation of cutting-edge technology into risk assessment processes, such as big data analytics, IoT, AI, and blockchain, will have a substantial influence on supply chains and transportation logistics. Hence, more study is still required on this issue. The optimisation of operations, decision-making, and resilience are all hampered by this gap, which prevents the realisation of possible technical improvements in risk management. Due to a lack of knowledge about the advantages and difficulties of integrating new technologies, businesses have failed to fully realise their transformational potential. Their capacity to adjust to changing risk circumstances is hampered by this restriction. Supply networks struggle to deal with new risks effectively as a result of being more prone to mistakes and inefficiencies. If one wishes to maintain their competitiveness, make educated judgements, and enable proactive risk mitigation measures in transportation logistics and beyond, they must explore this unexplored region.\u003c/p\u003e \u003cp\u003eThe absence of conclusive empirical evidence correlating the application of risk evaluation approaches in supply chains to enhanced efficiency in transportation logistics has profound implications. It is challenging to establish a clear connection between the effects of risk assessment methodology on operational effectiveness, resilience, and supply chain performance as a whole because of this mismatch. Decision-makers may find it difficult to justify expenditures in risk evaluation programmes and comprehend their concrete advantages in the absence of such evidence. Certain details about the application of risk-based techniques in the formulation of transportation logistics strategy and the overall effectiveness of the supply chain will undoubtedly require more study. This robust backing will not only help with decision-making but also advance our knowledge of how crucial risk assessment is to producing the best outcomes for business operations.\u003c/p\u003e \u003cp\u003eWe might be able to comprehend risk management strategies better by filling in the gaps in the literature on supply chain risk evaluation techniques and their effects on transportation logistics. Organisations will be able to do this through extending standard approaches, overcoming industry and regional variances, using new technologies, and connecting risk management procedures to improvements in transportation logistics and supply chain efficiency. These developments will provide enterprises the tools they need to manage risks in advance, improve production resilience, and gain an advantage over rivals in the quickly expanding supply chain management sector. The study's goals include a thorough evaluation of the efficacy of the current risk evaluation techniques and the challenges or limitations encountered in using risk evaluation procedures in Transport Logistics supply chain.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research Philosophy and Design Approach\u003c/h2\u003e \u003cp\u003eThe study is aligned to the positivist research philosophy along side descriptive research design. Both approaches allow for the collection and analysis of quantitative data in an objective way. This approach can be useful when studying phenomena that can be measured and observed in a quantifiable way. The study adopted a quantitative approach. The justification for utilising a quantitative approach in this research is based on its strong connection with the research objectives, which centre on the assessment of risk evaluation techniques and their influence on transport logistics. Quantitative approaches provide a systematic and complete approach to investigating these aims using numerical data and statistical studies. Furthermore, this technique aligns with well-established approaches in the field of transportation logistics and supply chain risk evaluation, as established by prior studies conducted by Lai et al. (2017), Bode et al. (2011), and Soni \u0026amp; Kodali (2019). Through the utilisation of quantitative measures and the application of statistical rigour, the selected approach guarantees the study's outcomes to be robust, reliable, and directly relevant to decision-making within the field of transport logistics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research Design\u003c/h2\u003e \u003cp\u003eThe study adopted cross-sectional study research design. This design allows researchers to effectively capture a simultaneous representation of prevailing practises and outcomes, which is an essential requirement for efficiently addressing the study surveys. Moreover, the choice to utilise a cross-sectional design is substantiated by methodological evidence found in previous studies conducted in the fields of transportation logistics (Finck et al., 2022) and supply chain risk (Manuj \u0026amp; Mentzer, 2008; Mamun, 2023), which emphasizes its alignment with established conventions in the discipline. Through the effective gathering of data that is pertinent to the specific subject of the study, this design strengthens the significance, timeliness, and ability of the research to provide insights into crucial matters pertaining to the evaluation of supply chain risks within the field of transportation logistics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Population, Sampling and Sample Size\u003c/h2\u003e \u003cp\u003eThe target population was made up of supply chain specialists, logistics directors, and business executives with knowledge and experience in risk assessment techniques and logistics of transportation. According to Pournader et al. (2000) and Katsaliaki et al. (2000), this type of population was chosen based on the applicability of their ideas and their participation in the implementation and management of supply chain and logistics operations.\u003c/p\u003e \u003cp\u003eThe researchers selected a sample size of 273 for this investigation, considering the available resources and the necessary level of precision. To assure the representativeness of the findings across all industries and company sizes, a stratified random sample approach was utilised. The utilisation of this method not only serves to mitigate bias but also amplifies the practicality of the research findings (Hair et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bryman, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The determination of the sample size in this study demonstrates a careful consideration of both attaining a satisfactory level of accuracy and efficiently allocating the available resources. According to the industry and size of the company, a population is divided into subgroups using the stratified random sampling technique. According to industry type (manufacturing, retail, healthcare) and firm size (small, medium, large), the researchers divided the population into subgroups. Within each stratum (industry and firm size category), the researchers allocated a proportionate number of respondents to ensure that each subgroup is represented adequately in the final sample. This step minimized the potential for bias and enhanced the external validity of the study. Within each stratum, respondents were selected using random sampling techniques. This ensured that each member of the target population within a specific subgroup had an equal chance of being included in the study. After the researchers identified potential respondents within each stratum, the research team employed data collection tool such as questionnaire to gather information relevant to the study. Consequently, this method represents a methodologically robust selection for the research endeavour.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Collection\u003c/h2\u003e \u003cp\u003eThrough surveys, primary data was gathered. Questionnaire in the form of likert scale was used to source data from the respondents about the efficiency of risk evaluation processes and their effect on the performance of transport logistics (Creswell \u0026amp; Creswell, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Saunders et al., 2018). In addition, the secondary data from academic and industry reports, and relevant databases. This information was added to the backdrop, theoretical background, and market trends around risk assessment techniques and transportation logistics (Eisenhardt, 1989; Yin, 2018). To the predetermined sample of supply chain experts and logistics managers, the question was administered through face-to-face interview with the respondents. In order to increase response rates, a mix of personalised email invites and reminders will be sent (Dillman et al., 2014; Vannieuwenhuyze et al., 2016).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data Analysis\u003c/h2\u003e \u003cp\u003eFollowing the completion of data collection, a thorough data analysis approach was conducted in order to provide meaningful findings and fulfil the objectives of the study. The data was analysed using the Statistical Package for the Social Sciences (SPSS) in order to assess the benefits involved from implementing risk evaluation procedures and the challenges in transportation logistics supply chain. Copyright license for the SPSS software was obtained through the License Authorisation Wizard. Descriptive statistics, including measures of central tendency such as means, as well as frequencies and percentages, will be computed. In order to examine the connections between variables and assess the importance of these interactions, inferential statistical methods such as regression analysis and correlation analysis will be employed (Hair et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bryman, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Ethical Considerations\u003c/h2\u003e \u003cp\u003eThe study conformed to all the relevant ethical considerations pertinent to research involving human. Before any data was collected, all participants' informed consent was sought. All during the study, participants' confidentiality, privacy, and anonymity was maintained. During analysis and reporting, all personally identifiable information was taken out of the data. To protect the ethical integrity of the study, ethical permission was requested from the appropriate institutional review board (Creswell \u0026amp; Creswell, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Saunders et al., 2018).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.1 Demographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCounts\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% of the Total\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHND/Diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost-graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u0026ndash;40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u0026ndash;50 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u0026ndash;60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eSource: Field data, 2023\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows a gender imbalance in the population of the survey is shown by the examination of demographic data, which indicates that 64.8% of respondents are men. With 47.3% of people in each marital status, the distribution of marital status is fairly equal. The age distribution is diversified, with substantial percentages in the 41\u0026ndash;50-year (37.6%) and 31\u0026ndash;40-year (33.2%) age groups, indicating a highly educated population, and the preponderance of first-degree holders (90.1%). Age-related studies are made possible by this age diversity. It is advised to use a questionnaire that combines multiple-choice, open-ended, and Likert scale questions to collect thorough quantitative and qualitative data in order to assess the effect of risk evaluation processes on transport logistics performance.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.2 The Effectiveness and Efficiency of Existing Risk Evaluation Procedures in Supply Chains and their Impact on Transport Logistics Performance\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResponses on Frequency of Risk Evaluation Procedures Conducted\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLevels\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCounts\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e% of Total\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCumulative %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeekly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuarterly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnually\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDont know\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eSource: Field data, 2023\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eHow frequently risk evaluation processes are carried out is shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. According to the distribution, 1.1% of respondents do procedures daily, 4.4% perform them weekly, 8.1% perform them quarterly, and 4.4% perform them yearly. When asked how frequently these operations are performed, 82% of respondents said they were unsure. This distribution illustrates the respondents\u0026apos; diverse familiarity and participation with the frequency of risk evaluation procedures.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRisk evaluation techniques are currently employed\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQualitative risk assessment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQuantitative risk assessment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eScenarios analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFrom Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the respondents exhibit a spectrum of preferences, with Scenarios Analysis (mean\u0026thinsp;=\u0026thinsp;4.99) and Others (mean\u0026thinsp;=\u0026thinsp;4.63) emerging as the most favored techniques. Notably, Scenarios Analysis stands out with the highest mean score, underscoring its pivotal role in conducting thorough risk assessments. Similarly, the category Others (supply chain mapping, real-time Monitoring and tracking, contingency planning, supply chain diversification) encompasses alternative risk assessment methods that hold significant relevance. Qualitative Risk Assessment and Quantitative Risk Assessment are also in use, garnering mean scores of 3.29 and 3.21 respectively, indicating their considerable but relatively slightly lower adoption compared to scenarios analysis and other techniques. The alignment between median values and mean values reinforces the consistency of the respondents\u0026apos; preferences. This sheds light on the variety of risk assessment techniques used, highlighting a preference for thorough methodology like Scenario research and highlighting the applicability of the Others categorization, which may include unique or specialised methods.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFrequencies of the current risk evaluation procedures communicated\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLevels\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCounts\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e% of Total\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWritten reports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDashboards and visualizations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeetings and presentations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eSource: Field data, 2023\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the various methods for communicating procedures within the organizations. Meetings and Presentations emerge as the most prevalent mode, accounting for 51.7% of the responses. This shows that discussions of the results of risk evaluations are greatly influenced by in-person interactions. Dashboards and Visualizations also play a substantial role, with 23.2% of respondents indicating their use. This is probably due to the growing emphasis on effectively communicating complicated facts through visualisation. Written Reports are the chosen mode for 12.1% of respondents, indicating a traditional approach to documentation. Additionally, Other is chosen by 13.2% of respondents, suggesting that there might be diverse and context-specific communication methods beyond the provided options. These findings highlight the complexity of risk communication as well as the need of participatory settings like meetings and visualisation tools.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGender and Effectiveness of the current risk evaluation procedures\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"16\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLess effective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFairly effective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVery effective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eeffective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; Tests\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\"\u003eSource: Field data, 2023\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eA thorough breakdown of answers across all efficacy levels and gender is shown in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Among male participants, a significant number of 84 respondents indicated that the procedures were categorized as Very Effective, while 27 respondents found them Less Effective. Notably, 12 males responded positively to the Fairly Effective category, and 21 expressed a Neutral viewpoint. Additionally, 33 males deemed the procedures Effective. In contrast, the pattern diverged among female participants. Specifically, 30 respondents rated the procedures as Very Effective, whereas 27 found them Less Effective. The Fairly Effective category garnered 12 positive responses from females. Further, 12 females indicated a Neutral stance, while 15 were categorized as Effective. The \u0026chi;\u0026sup2; test, yielding a calculated value of 11.8 with 4 degrees of freedom, yields a p-value of 0.019. The p-value shows a strong correlation between gender and the efficacy of risk assessment techniques. Individuals\u0026apos; perceptions of these procedures are greatly influenced by gender, highlighting the significance of taking gender-specific aspects into account when evaluating risk. This demonstrates the necessity of include gender-related viewpoints in risk assessment methodologies.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEducational Level and the effectiveness of the current risk evaluation procedures\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"17\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eeducational level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLess effective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFairly effective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVery effective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eeffective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHND/Diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost-graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\"\u003eSource: Field data, 2023\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBased on the respondents\u0026apos; educational backgrounds, this table provides a thorough breakdown of the responses spread across different efficacy levels (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Notably, within the HND/Diploma group, all respondents reported considering the procedures Very Effective. Among respondents with a First Degree, 54 participants viewed the procedures as Less Effective, 24 as Fairly Effective, 33 as Neutral, 96 as Very Effective, and 39 as Effective. In the Post-graduate category, 15 respondents regarded the procedures as Very Effective, and 9 as Effective. The computed \u0026chi;\u0026sup2; test statistic demonstrated a substantial outcome, reaching a value of 23.9, and with 8 degrees of freedom, it yielded a p-value of 0.002. The p-value demonstrates a strong relationship between respondents\u0026apos; educational backgrounds and how well they believe risk assessment processes are working. This emphasises how vital it is to take into account participants\u0026apos; credentials while assessing these procedures, as their level of education has a big influence on how they perceive things.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe main benefits observed from implementing risk evaluation procedures in Transportation Logistics supply chain\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"15\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEffect Size\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost reduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent\u0026apos;s t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCohen\u0026apos;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWilcoxon W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRank biserial correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImproved supply chain resilience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent\u0026apos;s t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCohen\u0026apos;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWilcoxon W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRank biserial correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBetter decision-making\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent\u0026apos;s t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCohen\u0026apos;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWilcoxon W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRank biserial correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnhanced customer satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent\u0026apos;s t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCohen\u0026apos;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWilcoxon W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRank biserial correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncreased operational efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent\u0026apos;s t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCohen\u0026apos;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWilcoxon W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRank biserial correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003eSource: Field data, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe One Sample T-Test results provide important information on the key benefits of applying supply chain risk evaluation techniques. For the benefit of Cost reduction, the Student\u0026apos;s t-statistic was 33.2 with 272 degrees of freedom, yielding an extremely low p-value of \u0026lt;\u0026thinsp;.001. The effect size, measured by Cohen\u0026apos;s d, was substantial at 2.01. Similarly, for Improved supply chain resilience, the student\u0026rsquo;s t-statistic was 40.6 with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;.001, and Cohen\u0026apos;s d was 2.46. Better decision-making also showed a strong effect, with a Student\u0026apos;s t-statistic of 62.4 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;.001, accompanied by a large Cohen\u0026apos;s d of 3.78. Enhanced customer satisfaction had a student\u0026rsquo;s t-statistic of 54.1 with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;.001, and Cohen\u0026apos;s d was 3.27. Lastly, increased operational efficiency demonstrated an immensely significant result, with a student\u0026rsquo;s t-statistic of 210.5 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;.001, and a considerable Cohen\u0026apos;s d of 12.74. The Wilcoxon W tests further supported the statistical significance and effect sizes, with all tests showing p-values\u0026thinsp;\u0026lt;\u0026thinsp;.001 and a Rank biserial correlation of 1.00 for each benefit. These findings underscore the substantial positive impact of implementing risk evaluation procedures in the supply chain across various dimensions.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab8\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe key challenges or limitations encountered in using risk evaluation procedures in Transport Logistics supply chain\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"16\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026plusmn;%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEffect Size\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLack of data availability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent\u0026apos;s t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCohen\u0026apos;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBayes factor₁₀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63e\u0026thinsp;+\u0026thinsp;233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.33e-238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWilcoxon W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank biserial correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifficulty in risk identification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent\u0026apos;s t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCohen\u0026apos;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBayes factor₁₀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.52e\u0026thinsp;+\u0026thinsp;139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.46e-148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWilcoxon W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank biserial correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsufficient resources for risk evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent\u0026apos;s t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCohen\u0026apos;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBayes factor₁₀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.63e\u0026thinsp;+\u0026thinsp;167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50e-172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWilcoxon W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank biserial correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimited employee engagement and commitment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent\u0026apos;s t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCohen\u0026apos;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBayes factor₁₀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.94e\u0026thinsp;+\u0026thinsp;173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.97e-181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWilcoxon W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank biserial correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInadequate integration of risk evaluation into decision-making\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent\u0026apos;s t\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCohen\u0026apos;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBayes factor₁₀\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31e\u0026thinsp;+\u0026thinsp;157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09e-162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWilcoxon W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRank biserial correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\"\u003eSource: Field data, 2023\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe results of the One Sample T-Test provided a description of the major difficulties or restrictions associated with supply chain risk evaluation techniques. For the challenge of Lack of data availability, the student\u0026rsquo;s t-statistic was notably high at 120.0, with 272 degrees of freedom, resulting in an extremely low p-value of \u0026lt;\u0026thinsp;.001. The effect size, as measured by Cohen\u0026apos;s d, was substantial, registering at 7.26. The Bayes factor₁₀ also indicated overwhelming evidence in support of the findings. Similarly, for Difficulty in risk identification, the student\u0026rsquo;s t-statistic was 52.1, with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;.001, and Cohen\u0026apos;s d was 3.16, indicating a significant effect size. The Bayes factor₁₀ further supported the statistical significance. Insufficient resources for risk evaluation had a student\u0026rsquo;s t-statistic of 67.4, a p-value\u0026thinsp;\u0026lt;\u0026thinsp;.001, and Cohen\u0026apos;s d of 4.08, indicating a substantial effect size. Limited employee engagement and commitment showed a student\u0026rsquo;s t-statistic of 71.3, a p-value\u0026thinsp;\u0026lt;\u0026thinsp;.001, and Cohen\u0026apos;s d of 4.31, denoting a substantial effect size. The challenge of Inadequate integration of risk evaluation into decision-making demonstrated a student\u0026rsquo;s t-statistic of 61.4, a p-value\u0026thinsp;\u0026lt;\u0026thinsp;.001, and Cohen\u0026apos;s d of 3.72, signifying a significant effect size. The Wilcoxon W tests further supported the statistical significance and effect sizes for all challenges, with p-values\u0026thinsp;\u0026lt;\u0026thinsp;.001 and a Rank biserial correlation of 1.00 for each challenge. These findings emphasise the important challenges faced when applying risk evaluation techniques in the supply chain and place emphasis on the areas that need attention and development.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion of the Findings","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 The Effectiveness and Efficiency of Existing Risk Evaluation Procedures in Supply Chains\u003c/h2\u003e \u003cp\u003eThe outcomes of this study resonate with the literature on supply chain risk management. Garvey \u0026amp; Carnovale (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) highlight the nuanced impact of risk factors on supply chain structures, suggesting that the distribution of response frequencies in this study may stem from the diverse nature of risks and their variable effects on different supply chain aspects. Fan et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Kauppi et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) provide insights into the preferred risk assessment methods and holistic risk management approaches, which align with the study's emphasis on the prominence of Scenario Analysis. The communication modes identified in this study align with the findings of previous research. Meetings and presentations as dominant modes of communication resonate with the interpersonal and interactive nature of risk discussions, as suggested by Simoa et al. (2016) and Wiengarten (2016). The increasing role of dashboards and visualizations is consistent with the trend towards using visual aids for conveying complex risk data, in line with Fugate et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe significance of gender and educational levels on perceived effectiveness of risk evaluation procedures corresponds with existing research on the influence of individual characteristics on risk perceptions. Similar to the outcomes of Zhao et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Kwak et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), this study acknowledges the impact of gender and education on risk assessment perceptions, emphasizing the need to consider these factors in risk management strategies. In terms of the implications, the positive impact of risk evaluation procedures on cost reduction aligns with the financial benefits suggested by Fan et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the notion that proactive risk management can lead to cost savings. The link between risk evaluation and supply chain resilience supports the emphasis on resilience strategies in Simoa et al. (2016) and the need for continuity in uncertain environments highlighted by Wiengarten (2016). The alignment between risk evaluation and decision-making effectiveness resonates with the strategic value of data-driven insights emphasized in Fugate et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Kauppi et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The connection between risk evaluation and enhanced customer satisfaction is consistent with the customer-centric focus discussed by Zhao et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and the importance of customer experiences in Simoa et al. (2016). Finally, the impact of risk evaluation on operational efficiency is in line with the notion of streamlining processes and optimizing operations discussed by Wang et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Hsieh et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Together, the study's findings and their alignment with existing literature reinforce the multidimensional advantages of robust risk evaluation procedures for supply chain management and organizational performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Key Challenges or Limitations Encountered in Using Risk Evaluation Procedures\u003c/h2\u003e \u003cp\u003eThe challenges associated with risk evaluation procedures are well-documented in the literature and addressing them is indeed crucial for realizing the benefits outlined above. Garvey \u0026amp; Carnovale (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) emphasize the significance of data availability in risk assessment, highlighting the need for robust data collection to accurately evaluate risks. Wang et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) discuss the difficulty of risk identification, suggesting that collaboration across departments, as mentioned by Fan et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Kauppi et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), is essential to comprehensively recognize risks.\u003c/p\u003e \u003cp\u003eThe challenge of resource constraints is acknowledged in the literature. Smith \u0026amp; Morrato (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) emphasize the importance of efficient resource allocation in risk-minimization initiatives. By strategically allocating resources to new ways, Wang et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) highlight the significance of logistics innovation in reducing supply chain risks. Smith \u0026amp; Morrato (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) emphasise the significance of employee involvement in risk management in the context of pharmaceutical risk-minimization activities. Wiengarten (2016) emphasizes the significance of organizational culture in integrating supplier integration practices with risk management, highlighting the role of communication and engagement. The challenge of integrating risk evaluation into decision-making aligns with findings from multiple studies. Hsieh et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) highlight the practical implementation of risk evaluation models, emphasizing their integration into decision-making. Kwak et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) stress the importance of holistic risk analysis, which requires integrating risk elements and interactions. This provides valuable insights into the challenges of risk evaluation procedures and offers strategies that organizations can adopt to address these challenges effectively. By learning from these studies, organizations can develop more resilient and effective risk management practices that contribute to improved supply chain performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Implications for Best Practices and Guidelines\u003c/h2\u003e \u003cp\u003eThe analysis on the creation of best practices and recommendations for risk assessment and management in supply networks is in good agreement with the literature already available in the area of risk management and supply chain operations. The emphasis on effective methodologies, comprehensive risk identification and mitigation, and alignment with broader supply chain strategies resonates with the findings of Fan et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) regarding the positive impact of risk-sharing mechanisms on operational performance. This suggests that a holistic approach to risk management, involving collaboration and alignment with broader strategies, is preferred for achieving optimal outcomes. The incorporation of developing technologies like AI and IoT into risk assessment processes highlights the dynamic character of risk management, which is reflected in the recognition of emerging technologies and cooperation with regulatory agencies. This is consistent with Liu et al.'s (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) observations when they proposed a technique combining suppliers' risk assessment based on material flow analysis, stressing the significance of creative approaches.\u003c/p\u003e \u003cp\u003eThe implications of standardizing risk evaluation procedures in transportation logistics supply chains are also well-supported by the literature. The idea that standardization ensures a unified approach to risk assessment and promotes transparency resonates with the findings of Kwak et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who developed a comprehensive risk analysis model that considers risk elements and their interactions, enhancing transparency and consistency in risk evaluation. Similarly, the concept that standardization facilitates benchmarking and collective response to challenges is in line with the observations of Smith \u0026amp; Morrato (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), who recommended utilizing models and frameworks to guide successful outcomes. The acknowledgement of challenges in achieving standardization and the importance of collaborative efforts and open communication align with the findings of Wiengarten (2016) in the context of supplier integration practices. This suggests that addressing challenges in standardization involves fostering collaboration and effective communication among stakeholders. The analysis provides insights that are consistent with the existing literature, emphasizing the importance of best practices, holistic approaches, technology integration, and standardization in risk assessment and management in transportation logistics supply chains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4 The impact of risk evaluation procedures on transport logistics performance\u003c/h2\u003e \u003cp\u003eThe examination of the impact of risk evaluation procedures on transport logistics performance, as evidenced by this analysis, closely mirrors the established body of literature, illuminating the intricate relationship between distinct risk assessment methodologies and the holistic effectiveness of transport logistics operations. The substantial and positive influence of Scenario Analysis on transport logistics performance is harmonious with the discoveries by Garvey \u0026amp; Carnovale (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who underscore the necessity of encompassing diverse scenarios in risk appraisal. This concurrence aligns with the prevailing notion that delving into potential outcomes and devising strategies for multiple eventualities substantially enhances an organization's adeptness in confronting disruptions, as echoed by Kwak et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The affirmative impact of Quantitative Risk Assessment on transport logistics performance gains further affirmation from the practical evidence offered by Wang et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which establishes a link between logistics competence, the uncertainty of supply chains, and risk. Organizations that strategically leverage quantitative data and rigorous calculations for risk evaluation stand better poised to arrive at judicious decisions, supported by a more accurate grasp of potential risks, as demonstrated in the work of Fan et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe robustly significant positive correlation between Qualitative Risk Assessment and transport logistics performance resonates harmoniously with the observations of Simoa et al. (2016), who dissect the consequences of logistics and packing postponement strategies. This alignment underscores the significance of harnessing the expertise and qualitative insights of seasoned professionals for discerning and addressing crucial risks that might elude quantification yet hold pivotal relevance for logistics operations. The apparent absence of statistically significant impact of Supply Chain Mapping on transport logistics performance adheres to the perspective that visualization techniques may not invariably translate into direct performance enhancements. This outcome underscores the nuanced and intricate interplay between visualization tools and logistical performance, as articulated by Zhao et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) in their study on the interrelation between supply chain integration and company performance.\u003c/p\u003e \u003cp\u003eThe positive consequence of Root Cause Analysis on transport logistics performance harmonizes with the findings of Wang et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who dissect the role of innovation in logistics capability in mitigating supply chain risks. The significance of identifying the root causes of issues and implementing targeted resolutions aligns well with the observations of Kauppi et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), emphasizing the critical nature of addressing challenges at their core for sustainable improvements in performance. The lack of apparent statistically significant effect of Real-time Monitoring and Tracking on transport logistics performance converges with the multifaceted nature of integrating technology, as underscored in the discourse on emerging technologies. This result acknowledges the intricate dynamics at play in the implementation and impact of real-time monitoring, a fact that Holzmeister \u0026amp; Stefan (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) highlight in their study on risk preferences.\u003c/p\u003e \u003cp\u003eThe affirmative impact of Supply Chain Diversification on transport logistics performance echoes the concept of bolstering supply chain resilience, as expounded by Wiengarten (2016). This finding aligns well with the idea that diversified supply chains fortify an organization's capacity to navigate disruptions, as also emphasized by Page et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in their study concerning risk assessment related to reporting biases.To conclude, this analysis augments our understanding by affirming the correlation between varied risk assessment techniques and transport logistics performance, in harmony with the existing literature. These findings, characterized by their alignment with prior research, provide invaluable insights for organizations endeavoring to optimize risk assessment strategies and elevate the efficiency of logistics operations.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eA deep understanding of Supply chain dynamics requires the adoption of a comprehensive approach to risk assessment that include cutting-edge technology, standardised practices, best practices and stakeholder participation come with the challenging nature of systematic risk assessment on productivity, adaptability, and competitiveness. The strides to successful risk management in the transportation logistics supply chain management necessitates resourcefulness, collaboration and commitment to excellence. The results of this study could render supply chains more effective and adequately prepared for transportation logistics in a connected and unpredictably dynamic world, which would be highly beneficial to supply chains for transportation logistics.\u003c/p\u003e"},{"header":"6. Recommendations","content":"\u003cp\u003eOrganisations should proactively engage in developing technologies like Big Data analytics, IoT, AI, and Blockchain given the favourable image of these systems. These kinds of emerging technologies would greatly increase the accuracy of risk assessment, decision-making, and real-time monitoring. In managing the risks of a dynamic supply chain, strategically integrating them might give an advantage. Organisation should combine qualitative, quantitative methods. Scenario analysis for a comprehensive risk assessment, they should adopt to enhance their potential benefits in risk management. Also, the improvement of real-time monitoring and tracking systems in transportation logistics is very vital. While the study found that real-time monitoring was not strongly linked to changes in logistics performance, enhancing these systems may provide more timely insights into emerging risks.\u003c/p\u003e"},{"header":"7. Limitations","content":"\u003cp\u003eDespite the study findings and conclusion drawn it come limitations to be mindful of which include the limited sample size of the study and potential self-reporting bias, both of which may affect the generalization of the results. Also, it's feasible that the analysis's static strategy and industry-specific concentration could accurately dazzling the dynamic changes in risk management in the transport logistics. The study encourages the requisite the embracing of best practices, new know-hows, and diverse risk assessment approaches. It also backs for these wide-ranging approaches. Future Research on the longitudinal studies on the advancing environment of risk management practices on sustainable and environmental factors within the transport logistic.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eLEY wrote the main manuscript and ROM and DOA assisted in the preparation of the tables and figures. All authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e Ethical review and approval were waived by Takoradi Technical University (TTU) Research Ethics Committee for the study. This was due to anonymity of interviewees and also no sensitive data were collected. All procedures for data collection were treated with confidentiality according to TTU\u0026rsquo;s Declaration on ethics. Informed consent was sought from all individual participants (respondents) included in the study. They were informed that the survey was anonymous and that participation was voluntary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eNo funding was received to assist with the preparation of the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number :\u0026nbsp;\u003c/strong\u003enot applicable\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBraun, V., \u0026amp; Clarke, V. (2019). Thematic analysis. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, \u0026amp; K. J. Sher (Eds.), APA Handbook of Research Methods in Psychology, Vol. 2: Research Designs: Quantitative, Qualitative, Neuropsychological, and Biological (pp. 57-71). American Psychological Association.\u003c/li\u003e\n \u003cli\u003eBryman, A. (2016). 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The impact of supply chain risk on supply chain integration and company performance: a global investigation. Supply Chain Management, 18, 115-131. https://doi.org/10.1108/13598541311318773.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"Risk evaluation, transportation logistics, standardization, technology integration, supply chain, Industrial sociology ","lastPublishedDoi":"10.21203/rs.3.rs-6691764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6691764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSupply chain risk management is critical to the effectiveness and resilience in transportation logistics operations. The purpose of this study is to examine the effectiveness and proficiency of current risk evaluation processes in transportation logistics supply chains. Through quantitative research approach, the data was sourced through surveys of supply chain professionals, logistics directors, and corporate executives. The study shows that Scenario Analysis, Quantitative Risk Assessment, Qualitative Risk Assessment, Supply Chain Mapping and Root Cause Analysis as well as Supply Chain Diversification significantly impacts transport logistics performance. However, Real-time Monitoring and Tracking are not strongly linked to changes in transport logistics performance. \u0026nbsp;The development of best practises and standardization for efficient risk assessment and management in supply chains can be influenced by these findings. The study also perceives the challenge of integrating standardized risk evaluation procedures with existing systems as highly significant and practically impactful. Several anticipated benefits arise from incorporating new technology in improving supply chain risk evaluation processes. However, the cost of implementation and maintenance, along with addressing organizational culture and change resistance, as highly significant incorporating emerging technologies into risk assessment procedure. It emerged that when integrating new technologies, the costs of installation and upkeep, data security and privacy concerns as well as dealing with organisational culture and change resistance, are quite important. The study underscores the value of robust risk assessment practices in enhancing supply chain.\u003c/p\u003e","manuscriptTitle":"Assessing the Benefits and Challenges in Transportation Logistics: Testing the Effectiveness and Efficiency of Existing Risk Evaluation Procedures in Supply Chain ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-28 11:03:51","doi":"10.21203/rs.3.rs-6691764/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":"70498fc5-90b3-4371-9408-137d6a0e6682","owner":[],"postedDate":"May 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-28T12:08:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-28 11:03:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6691764","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6691764","identity":"rs-6691764","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}