Productivity Management: a Systematic Review of Approaches, Trends and Future Research Agendas

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The article seeks to present the current landscape of studies, identify international scientific production trends, and propose an agenda with new challenges for future research. The present SLR used the Scopus database with constructs published from 2018 onwards. Bibliometric results highlight extensive scientific production on productivity, outlining key contributions from authors, countries, and institutions in the field. Thematic analysis provides a comprehensive view of well-developed, specialized, emerging or declining areas in productivity. A detailed analysis of different productivity measurement approaches offers valuable insights into the diversity of practices adopted by reviewed studies. Future challenges and research opportunities include a deeper understanding of qualitative aspects of productivity management, exploring the interaction between efficiency and effectiveness, and investigating productivity management in specific sectors. This SLR provides a solid foundation for future research, emphasizing existing gaps and opportunities for advancements in understanding productivity management. Productivity Productivity Management Systematic Literature Review International Production Research Challenges Figures Figure 1 Figure 2 1. Introduction The current discourse in increasing productivity emerges within a complex framework characterized by resource scarcity, environmental degradation, heightened competitiveness, an aging workforce, and rapid technological progress (Khanna & Sharma, 2018b ; Käpylä et al., 2010 ). This necessitates a deeper investigation into the prevailing trends and prospects for increasing productivity within a dynamic global landscape, with an overarching goal of fostering continuous societal and professional advancement (Käpylä et al., 2010 ; Haynes, 2007 ). Although the literature conventionally defines productivity as the relationship between inputs and outputs (Wiech et al., 2019 ; Czumanski & Lšdding, 2012 ; Oeij et al., 2011 ; Käpylä et al., 2010 ), it is a multidimensional concept. Depending on the context, its meaning can change (Günter & Gopp, 2021). This breadth of concepts has led to the emergence of many approaches to measuring productivity in literature and industry, resulting in conflicting definitions (Günter & Gopp, 2021; Oeij et al., 2011 ). Oeij et al. ( 2011 ) study explores that productivity is related to efficiency and effectiveness, tangible and intangible assets, profitability, quality, and value creation, making it a crucial determinant in organizational performance (Wiech et al., 2019 ; Käpylä et al., 2010 ; Tangen, 2005 ). However, the last two centuries have shown that the world has evolved into a service-based economy focused on the customer (Amirul et al., 2021 ). Even though manufacturing remains critical for economic growth, there is a clear need to investigate productivity in a scenario with intangible production, where the value of a service depends directly on customer experience (Günter & Gopp, 2021; Oeij et al., 2011 ). Additionally, technological advancement is one of the most relevant and crucial factors in economic growth, and investment in this sector drives productivity performance in companies (Khanna & Sharma, 2018b ). In a scenario where knowledge workers gain increasing prominence, there is a growing interest in exploring the productivity of workers in the organizational environment, considering that human resources constitute the largest share of both costs and revenues (Bortoluzzi et al., 2018 ). Considering also that the nature of work has changed in the last century (Amirul et al., 2021 ; Haynes, 2007 ), productivity is not well understood where the service sector is relevant in the global economy (Khanna & Sharma, 2018b ). Therefore, there is a lack of systematic views that allow for the classification of existing approaches to productivity measurement (Günter & Gopp, 2021), revealing an opportunity to explore the productivity landscape in the present. This article aims to analyze the state of the art in productivity, seeking to present the current research and propose an agenda with new challenges for future studies. The results can serve as inspiration for managers seeking to implement policies and practices that improve productivity. 1.1 A Historical Perspective Workplace productivity stands as a central concept in the fields of economics, management, and organizational psychology (Wiech et al., 2019 ; Haynes, 2007 ). It refers to the efficiency with which human resources are utilized to achieve goals and objectives within an organization (Rodríguez-Pose & Ganau, 2021 ). Increasing labor productivity has been a goal for companies and governments worldwide, as it is directly related to economic growth and social well-being (Oeji et al., 2011). One of the earliest theories addressing labor productivity was Taylor's theory, developed in the early 20th century. By applying a science of work that allowed for the selection and proper training of workers, productivity would substantially increase (Lauer Schachter, 2020 ). The systematic study of a workplace and the development of procedures to enhance productivity and worker satisfaction were embraced by organizations (Hill & Van Buren, 2018 ). The Human Relations Theory, developed by Mayo in the 1930s, introduced an innovative perspective on workplace productivity. It recognized the importance of social and psychological factors in worker motivation and performance, emphasizing the relevance of interpersonal relationships, recognition, and employee well-being as drivers of productivity (Trahair & Bruce, 2012 ). In the same context, starting in the 1950s, Maslow's Motivation Theory gained prominence, contributing to a movement that highlighted the importance of motivation to performance and commitment (Acevedo, 2015 ). Many approaches emerged, aiming to understand workers' needs to spontaneously generate feelings of collaboration and belonging to create a more productive work environment (Zoller & Muldoon, 2019 ). Subsequently, Total Quality Management (TQM), originating in the late 20th century, carries evolving concepts that impact productivity studies in various ways (Bazrkar et al., 2022 ). This theory was one of the most widely adopted management philosophies, where organizations enhance their management skills, boost performance, and strive for quality and excellence (Dahlgaard-Park et al., 2018 ). By seeking to improve quality in all functions and processes of the organization, carefully considering interactions between organizational elements, it is a strategy that can enhance learning and increase the competitive advantage of organizations, especially in markets where changes pose significant challenges (Bazrkar et al., 2022 ; Tortorella et al., 2019 ). In general, leadership and organizational behavior also play a crucial role in labor productivity (Antonacopoulou & Georgiadou, 2020 ). Different leadership theories, such as transformational and transactional leadership, discussed as early as the 1970s, emphasize how leaders motivate and influence their employees (Gerards et al., 2018 ). Therefore, leadership support contributes to organizational eligibility, playing a fundamental role in worker motivation and organizational performance improvement (Bae et al., 2019 ). Despite recent theories studying productivity, the term has been applied in various circumstances and at various levels of aggregation for over two centuries. It is one of the most important variables concerning competitive advantage, but often relegated to the background (Tangen, 2005 ). The lack of a common agreement on what the definition is noticeable, causing productivity to be disregarded in various organizational settings (Günter & Gopp, 2021). The study by Günter and Gopp (2021) presents a synthesis of standard productivity definitions, as outlined in Table 1 . Table 1 Exemplary collection of existing definitions of productivity Definition Reference Productivity = faculty to produce Littré (1883) Productivity = units of output/units of input Chew (1988) Productivity = actual output/expected resources used Sink and Tuttle (1989) Productivity = total income/(cost + goal profit) Fisher (1992) Productivity = value added/input of production factors Aspén et al. (1991) Productivity = efficiency*effectiveness = value adding time/total time Jackson and Petersson (1999) Productivity = (output/input)*quality = efficiency*utilisation*quality Al-Darrab (2000) Productivity = outputs/inputs Coelli et al. (2005) Productivity = (output quantity*output quality)/(input quantity*input quality) Oeji et al. (2011) Productivity = method*performance*utilisation*quality*design Almstrom (2012) Productivity = total outputs/total inputs Berhe et al. (2017) Productivity = actual output per combined unit of labour, machine and overhead, reflecting the contributions of all factors of manufacturing Rehman et al. (2020) (Günter & Gopp, 2021) 2. Method The present study, descriptive and qualitative in nature, adopted the systematic literature review (SLR) as the research method. SLR is a systematic, explicit, comprehensive, and reproducible method for identifying, evaluating and synthesizing the body of work produced on a specific theme (Paré et al., 2015 ; Okoli & Schabram, 2010 ). To ensure greater transparency and rigor in constructing this review, the protocol suggested by Templier and Paré (2015) was used, summarizing the following six steps: problem formulation, literature search, screening for inclusion and exclusion, quality assessment, data extraction and data analysis and synthesis. The steps are synthesized in Fig. 1 . The description of the scientific production scenario regarding productivity constitutes the first step of problem formulation (i). Regarding the literature search (ii), the Scopus database was used, known for its significant number of indexed journals and quality, especially in the subject area (Chadegani et al., 2013 ). The database search was conducted by topic and/or title, abstract and/or keywords, using the boolean OR operator for the following terms: ("productivity" OR "productivity measurement" OR "labour productivity" OR "labor productivity" OR "productivity measure"). There was also a filter by areas, with the following selected in Scopus: "Business, Management and Accounting" and "Economics, Econometrics and Finance," as done in the study by Singh et al. ( 2000 ). Finally, only articles and reviews published from 2018 were filtered, considering the temporal scope of the productivity review published by Günter and Gopp (2021). Considering the limitation of Scopus for the number of articles extracted, the literature search, conducted in March 2023, resulted in a total of 2,000 articles. For the screening for inclusion and exclusion step (iii), Howard et al. ( 1987 ) method was used to identify the authors who contributed most to the theme. This is based on the order of authors in a particular article, where the method assigns different weights to various authors, with the first author receiving a higher weight than the second, and so on (Lopes & Neumann, 2021). Thus, the articles of the top 10 authors in the ranking were selected, limiting the sample to a total of 71 articles. Proceeding to the quality assessment step (iv), 17 articles that did not focus on labor productivity were excluded from the sample after analyzing titles, abstracts, and keywords. The final sample totaled 54 articles extracted from Scopus, as per the penultimate proposed step (v). Finally, the data analysis and synthesis step (vi) involved a bibliometric analysis of the reviewed articles to identify information about academic production on the topic, such as year of publication, countries, institutions, and journals that publish the most, as well as a general overview of the most published themes. These analyses were conducted using the R Bibliometrix software. In addition, from the comprehensive reading of the articles' texts, a spreadsheet was created based on the thematic categories used by Günter and Gopp (2021), representing the characteristics for productivity management used by the studies. Thus, the analyzed characteristics were categorized as level, type, measurement, sector, scope, quality, corporate level, and practicability, as shown in Table 2 . Table 2 Main characteristics of productivity No Main Characteristics No Subsidiary characteristics Definition 1 Level 1 2 Micro Macro Corporate level National or industry sector level 2 Type 1 2 3 4 5 Total productivity Partial productivity Labour productivity Machine productivity Material productivity Output related to multiple types of input factors Output related to one types of input factor Output related to the input factor labour Output related to the input factor machines Output related to the input factor materials 3 Measurement 1 2 3 Financial Quantitative Qualitative Productivity measured with monetary units Productivity measured with physical units Productivity measured with qualitative indicators 4 Sector 1 2 Manufacturing Service Output is physical goods Output is services 5 Scope 1 2 Efficiency Effectiveness Economical utilisation of resources (input) Fulfilment of customer requirements (output) 6 Quality - Quality aspects Product and process quality 7 Corporate level 1 2 Management Operational Implementation on the management level Implementation on the shop floor level 8 Practicability - Practically validated Validated with a practical example (Günter & Gopp, 2021) 3. Results and discussion 3.1. Contribution of authors, countries, and institutions The findings revealed in this section highlight the key authors, countries and institutions that have contributed to the construction of studies on the topic of productivity. It is important to note that the results presented here are not limited to the final set of 54 articles reached for this construct. Based on the scoring system outlined by Howard et al. (1987) protocol and operationalized by Lopes & Neumann (2021), scores for single and multiple author publications were valid and considered in the score construction (FU et al., 2023), summarized in Table 3. The table lists the authors who scored the highest in terms of article production in the field of productivity. As mentioned in the method section, after the quality assessment step (iv), some authors and articles were excluded from the analysis as their work did not focus on workplace productivity. Following this filtering process, Table 3 presents the top 10 authors selected and highlighted in bold, constituting the final set of articles, totaling 54 reviewed works. Table 3. Top authors in productivity research Author Score No Gurmu A.T. 19.23 8 Zondo R.W.D. 8.68 8 Sharma C. 8.35 11 Johari S. 8.35 5 Tzeremes N.G. 8.01 13 Chen C. 7.29 3 Jackson T. 7.21 4 Zhang D. 6.57 7 Lin B. 6.36 15 Crafts N. 6.08 11 Jha K.N. 6.04 5 Giampietro M. 5.98 2 Ricci A. 5.90 9 Wang X. 5.88 8 Kinfemichael B. 5.86 3 Yao Y. 5.78 2 Diao X. 5.68 5 Malanima P. 5.62 2 Alston J.M. 5.38 2 Caiani A. 5.22 3 Nishi H. 5.02 4 As observed, authors Gurmu A. T., Zondo R. W. D. and Sharma C. achieved the highest scores in the ranking, noting that authors of single-authored articles receive higher scores, thus boosting their respective scores, as seen with Gurmu A.T., who has 5 single-authored articles. The country ranking, as outlined in Table 4, is conducted in the same manner as that of authors, where the score consists of the sum of each author's score in their respective country. Therefore, the country corresponds to the research institution of each author, disregarding their nationality (Lopes & Neumann, 2021). The United States stands out quantitatively as the most influential country in the field of productivity, followed by China and the United Kingdom. Table 4. Top countries in productivity research Countries Score United States 468.31 China 273.12 United Kingdom 216.21 Italy 189.93 Australia 150.66 Spain 145.88 Russian Federation 138.45 India 114.21 Germany 88.48 France 82.05 Following the same logic as the rankings of authors and countries, it is possible to identify, in Table 5, the most influential institutions in the field. The spotlight is on the University of California, USA, Deakin University, Australia, and Xiamen University, China, with the three highest scores. Table 5. Top institutions in productivity research Institutions Score University of California 42.85 Deakin University 23.28 Xiamen University 18.95 University of Surrey 17.87 Roma Tre University 17.74 National Research University Higher School of Economics 17.55 University of Economics 14.87 University of Warwick 14.01 University of Vigo 13.54 Cornell University 13.13 3.2 Bibliometric Results This section unveils the bibliometric outcomes for the final set of articles selected after completing the literature search stages, totaling 54 articles. Consequently, Table 6 synthesizes the results for country scientific production, most relevant affiliations, annual scientific production, and most relevant sources. Table 6. Bibliometric results Countries Scientific Production Freq % Annual Scientific Production Freq % Italy 22 22,22 2018 13 24,07 UK 21 21,21 2019 12 22,22 India 14 14,14 2020 11 20,37 USA 14 14,14 2021 12 22,22 Greece 10 10,10 2022 4 7,41 South Africa 7 7,07 2023 2 3,70 Most Relevant Affiliations Freq % Most Relevant Sources Freq % Indian Institute of Management Lucknow 10 18,52 Applied Economics Letters 4 7,41 University of Thessaly 10 18,52 South African Journal of Economic and Management Sciences 4 7,41 University of Warwick 6 11,11 Economic Modelling 3 5,55 Durban University of Technology 5 9,26 Ecological Economics 2 3,70 International Food Policy Research Institute 4 7,41 As presented in Table 6, the assessment of scientific production across different countries is based on counting the contributions of authors and co-authors affiliated with these nations. In this context, Italy takes the lead with 22 contributions, followed by the United Kingdom, India, the USA, Greece, and South Africa. Concerning the top five institutions producing studies on productivity, the Indian Institute of Management Lucknow, India, and the University of Thessaly, Greece, were responsible for publishing ten articles each. The ranking continues with the University of Warwick, UK, with six articles, and Durban University of Technology, South Africa, and the International Food Policy Research Institute, USA, with five and four articles, respectively. Regarding annual scientific production, it is noteworthy that the peak of studies related to productivity occurred in 2018. In the three subsequent years, a similar average of publications was recorded. In contrast, the year 2022 showed a decrease in this proportion, with only four published contributions. In the contemporary business landscape, characterized by highly competitive and ever-changing global markets, survival requires mastery in the efficient and effective utilization of human, physical, technological, financial, and informational resources (Groen et al., 2019). In this context, the six most recent studies, spanning from 2022 to 2023, dedicated themselves to investigating various themes. Among them, the examination of the impact of new technologies on productivity (Cirillo et al., 2023; Crafts, 2022), the analysis of the relationship between productivity and flexibility in the labor market (Bloise et al., 2022; Cirillo & Ricci, 2022), the evaluation of the effect of tax reduction on incentives in job performance and average salaries (Damiani et al., 2023) and the exploration of the use of sigmoidal and bimodal growth curves in understanding how internalization affects organizational productivity (Tsionas & Tzeremes, 2022) stand out. The diversity of addressed topics reflects the search for understanding evolving trends in society and the professional environment. This approach provides a rich landscape for identifying new opportunities and challenges in the pursuit of productivity improvement, as already indicated by Käpylä et al. (2010). Concerning the most relevant journals, the renowned journals Applied Economics Letters, UK, and South African Journal of Economic and Management Sciences, South Africa, led with the publication of four articles each, while the last two, Economic Modelling and Ecological Economics, Netherlands, contributed with three and two studies, respectively. As outlined by Aria et al. (2022) and Cobo et al. (2011), the thematic map portrayed in Figure 2 allows for the identification of four distinct types of themes, varying based on the quadrant in which they are located: Motor Themes: These exhibit higher values of centrality and density, signifying well-developed themes that are crucial for framing the conceptual framework. Motor themes in Cluster 1 include labor productivity, manufacturing, and economic growth, while in Cluster 2, they encompass technological development, total factor productivity and economic history. Niche Themes: These display higher values of centrality and lower values of density. They possess well-developed internal ties, characterizing more specialized and peripheral themes. Among the niche themes are technological change, financial crisis and Canary Islands. Emerging or Declining Themes: These showcase lower values of centrality and density, defining themes that are less developed, either emerging or declining. As indicated in the map, population growth is identified as a declining theme. Basic Themes: These present lower values of centrality and higher values of density. While important for the research field, they are less developed, grouping together overarching and general themes. Examples include productivity and finance. Upon scrutinizing the motor themes, a total of 23 articles delved into the topics of Cluster 1, namely labor productivity, manufacturing, and economic growth. Understanding how changes in workforce distribution over recent decades can impact labor productivity is crucial. Many developing countries made significant strides in economic development in the 1990s, accompanied by a shift in labor allocation from agriculture to industry in developing countries and from industry to services in high-income countries, as indicated in the studies by Kinfemichael (2019). However, it is noteworthy that, despite the world evolving into a service-based economy (Amirul et al., 2021), there is still a focus on studies addressing productivity in the manufacturing sector, as seen in the works of Khanna and Sharma (2018a), Zondo (2018) and Singh and Sharma (2020). Continuing with the motor themes, Cluster 2 (technological development, total factor productivity and economic history), with an incidence of 13 articles, allows for interpreting how technological change is the ultimate source of sustained productivity growth and, consequently, long-term improvements in living standards (Bakker et al., 2019). The context of artificial intelligence development, the digital revolution and robotics heightens concerns about how technologies will impact productivity in organizations (Crafts, 2018). Economic growth may result from the use of additional labor or capital resources, or from the more efficient use of these resources, which already constitutes technological change, represented by the growth of total factor productivity (TFP) (Bakker et al., 2019). The niche themes, with nine articles, focused on topics such as technological change, financial crisis and the Canary Islands. Tzeremes' (2020) findings, investigating the productivity levels of hotels operating in the Balearic Islands and the Canary Islands during the pre-crisis period, the 2008 U.S. real estate crisis, the global financial crisis of 2010, and the post-crisis period, showed that productivity did not deteriorate due to the adverse effects of the economic crisis, mainly due to long-term investment in innovation policies that helped the hotel industry quickly recover from the Great Recession. As for emerging or declining themes, two articles debate population growth, primarily investigating productivity growth during the British Industrial Revolution. Crafts (2021) concluded that the literature has become overly pessimistic about the implications of productivity growth on real consumption gains and the labor income share in the economy, emphasizing the importance of thinking in terms of hypothetical scenarios, especially regarding the implications of population growth, but the interactions between demography and technology are still not well understood. The cluster of basic themes, featuring productivity and finance, had an incidence of seven articles. As pointed out by Sharma (2019), to remain competitive, organizations need to invest in technology and innovation, enabling increased productivity. In lower-income countries, companies may hesitate to invest in technology due to risks, uncertainties, and a lack of access to external financing. Additionally, the decision to invest in R&D may depend on the relationship between costs, risks, and potential returns, as well as the ability to acquire technology from more developed countries. This underscores that the economic context significantly impacts organizations seeking to innovate and increase their productivity. The characteristics used by studies to manage productivity are presented in the next section. 3.3 Productivity management characteristics Concerning the characteristics used for productivity management, the 54 reviewed articles presented distinct features in productivity management. Therefore, the studies were categorized into eight main characteristics, considering the multidimensional aspect of productivity. The results can be found in Table 7. Table 7. Main characteristics of reviewed studies Characteristics Subsidiary characteristics No % 1 Level Micro 26 44% Macro 33 56% 2 Type Labour productivity 27 49% Total productivity 28 51% 3 Measurement Quantitative 43 67% Financial 21 33% Qualitative 0 0% 4 Sector Manufacturing 42 55% Service 30 39% Not applicable 4 5% 5 Scope Efficiency 46 77% Effectiveness 6 10% Not applicable 8 13% 6 Quality aspects Yes 3 6% No 51 94% 7 Corporate level Operational 7 13% Management 16 30% Not applicable 31 57% 8 Practically validated Yes 45 83% No 9 17% Micro perspective was the most common characteristic in the studies when it comes to productivity management, with a total of 33 cases, in contrast to a less frequent macro-level approach, with 26 identified cases. Regarding types of productivity, it is interesting to note that, although Günter and Gopp's original study (2021) presented various categories, this review mainly identified two types of productivity: labor productivity, with 27 cases, and total productivity, with 28 cases. The quantitative approach to measuring productivity was the most frequent, with 43 cases, while the financial perspective was adopted 21 times, and qualitative measurement was not adopted in any of the analyzed studies. We also identified that productivity management was widely recognized in both the manufacturing sector, with 42 cases, and the service sector, with 30 cases. Furthermore, the identification of four studies that did not restrict themselves to a specific sector highlights the multifaceted approach to productivity management in different domains. The analysis of the scope adopted for productivity management reveals that efficiency was widely addressed in 46 cases, while effectiveness received less attention, with only six cases. In eight situations, it was not possible to clearly identify the scope of productivity management. This raises the question of how these studies defined and operationalized these concepts and whether there is room for a deeper understanding of effectiveness in the context of productivity management. On the other hand, it is noteworthy that qualitative aspects of productivity management were not identified in most cases, with only three studies addressing this dimension. This gap suggests an opportunity for future research to explore qualitative aspects of productivity management and how they can complement the predominant quantitative approaches. Regarding the selection of the corporate level for productivity management, we note that in 16 cases, the focus was on the managerial level, while in seven cases, the operational level was the central point of analysis. However, it is important to highlight that in a significant portion of studies, precisely 31 of them, there was no clear specification regarding the specific corporate level addressed in their analyses. Finally, it is worth noting that 45 articles sought to validate the practical feasibility of productivity management, while only nine articles did not address this analysis. 4. Conclusion This systematic literature review aimed to provide a comprehensive and in-depth understanding of the various approaches and characteristics associated with productivity management, addressing the identified research gap. From the contributions of classical theories like Taylor's Scientific Management to contemporary perspectives such as Total Quality Management, an evolution in the understanding and practice of productivity management over time was identified. The bibliometric results not only highlighted the extensive scientific production on productivity but also outlined the significant contributions of authors, countries, and institutions in this field, reflecting the globalization of interest and knowledge related to productivity. The thematic analysis, as expressed in the thematic map, revealed distinct clusters such as motor themes, niche themes, emerging themes, and basic themes, providing a comprehensive overview of well-developed, specialized, emerging, or declining areas in the field of productivity. The detailed analysis of productivity management characteristics, covering different corporate levels, measurement approaches and scopes, offered valuable insights into the diversity of practices and approaches adopted by the reviewed studies. 4.1 Future Challenges Despite the significant advances identified in this review, several research opportunities stand out for future investigations. Firstly, a deeper exploration of the qualitative aspects of productivity management, given their relative neglect in the reviewed literature, can enrich the predominant quantitative analyses. Exploring the interaction between efficiency and effectiveness in the context of productivity management can provide valuable insights for organizations seeking to optimize their resources sustainably. A more in-depth analysis of the factors influencing these two elements can open new theoretical and practical perspectives. Research into productivity management in specific sectors, such as the service industry, and consideration of contextual variables, such as the impact of technology and innovation, also represent promising areas for future research. Understanding how different organizational contexts respond to productivity management demands can contribute to more adaptive and effective strategies. A more detailed analysis of the practical implications of productivity management at different corporate levels can provide more specific guidance for leaders and managers, helping them make informed decisions. In summary, this review provides a solid foundation for future exploratory and confirmatory research, highlighting existing gaps and opportunities to advance the understanding and practice of productivity management. Declarations Author Contribution B.B. - Writing - original draft; Conceptualization; Methodology; Formal analysis; Data curation; Software.M.L. - Writing - original draft; Methodology; Formal analysis; Data curation.L.C. - Writing - review & editing; Data curation; Formal analysis.J.S., L.C. and D.B. - Data curation; Formal analysis.P.M. - Project administration; Supervision; Conceptualization.A.S. - Project administration; Funding acquisition.C.N. - Methodology; Funding acquisition; Conceptualization; Software. Statements and Declarations No funding was received to assist with the preparation of this manuscript. The authors certify that this scientific text does not constitute multipart or partial results of the same study and represents original research. The authors also declare no financial interests or conflicts of interest regarding this scientific text. 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Research productivity in psychology based on publication in the journals of the american psychological association. American Psychologist , 42 (11), 975–986. https://doi.org/10.1037/0003-066x.42.11.975 Haynes, B. P. (2007). Office productivity: A theoretical framework. Journal of Corporate Real Estate , 9 (2), 97–110. https://doi.org/10.1108/14630010710828108 Isham, A., Mair, S., & Jackson, T. (2021). Worker wellbeing and productivity in advanced economies: Re-examining the link. Ecological Economics , 184 , 106989. https://doi.org/10.1016/j.ecolecon.2021.106989 Käpylä, J., Jääskeläinen, A., & Lönnqvist, A. (2010). Identifying future challenges for productivity research: Evidence from Finland. International Journal of Productivity and Performance Management , 59 (7), 607–623. https://doi.org/10.1108/17410401011075620 Kinfemichael, B., & Morshed, A. K. M. M. (2019). Unconditional convergence of labor productivity in the service sector. Journal of Macroeconomics , 59 , 217–229. https://doi.org/10.1016/j.jmacro.2018.12.005 Khanna, R., & Sharma, C. (2018a). Do infrastructure and quality of governance matter for manufacturing productivity? Empirical evidence from the Indian states. Journal of Economic Studies , 45 (4), 829–854. https://doi.org/10.1108/jes-04-2017-0100 Khanna, R., & Sharma, C. (2018b). Testing the effect of investments in IT and R&D on labour productivity: New method and evidence for Indian firms. Economics Letters , 173 , 30–34. https://doi.org/10.1016/j.econlet.2018.09.003 Lauer Schachter, H. (2020). The uses of Frederick Winslow Taylor: How management theorists have interpreted scientific management over the years and why. In Handbook of research on management and organizational history (pp. 39–55). Edward Elgar Publishing. https://doi.org/10.4337/9781788118491.00009 Oeij, P. R. A., De Looze, M. P., Ten Have, K., Van Rhijn, J. W., & Kuijt‐Evers, L. F. M. (2011). Developing the organization's productivity strategy in various sectors of industry. International Journal of Productivity and Performance Management , 61 (1), 93–109. https://doi.org/10.1108/17410401211187525 Okoli, C., & Schabram, K. (2010). A guide to conducting a systematic literature review of information systems research. SSRN Electronic Journal . https://doi.org/10.2139/ssrn.1954824 Paré, G., Trudel, M.-C., Jaana, M., & Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information & Management , 52 (2), 183–199. https://doi.org/10.1016/j.im.2014.08.008 Rodríguez-Pose, A., & Ganau, R. (2021). Institutions and the productivity challenge for European regions. Journal of Economic Geography . https://doi.org/10.1093/jeg/lbab003 Sharma, C. (2019). Effects of R&D and foreign technology transfer on productivity and innovation: An enterprises-level evidence from Bangladesh. Asian Journal of Technology Innovation , 27 (1), 46–70. https://doi.org/10.1080/19761597.2019.1597634 Singh, A. P., & Sharma, C. (2020). Does India do IT? The nexus of IT, skills, organizational factors and productivity. Journal of Economic Studies , 47 (3), 597–626. https://doi.org/10.1108/jes-03-2019-0100 Singh, H., Motwani, J., & Kumar, A. (2000). A review and analysis of the state‐of‐the‐art research on productivity measurement. Industrial Management & Data Systems , 100 (5), 234–241. https://doi.org/10.1108/02635570010335271 Tangen, S. (2005). Demystifying productivity and performance. International Journal of Productivity and Performance Management , 54 (1), 34–46. https://doi.org/10.1108/17410400510571437 Tortorella, G., Giglio, R., Fogliatto, F. S., & Sawhney, R. (2019). Mediating role of learning organization on the relationship between total quality management and operational performance in Brazilian manufacturers. Journal of Manufacturing Technology Management , 31 (3), 524–541. https://doi.org/10.1108/jmtm-05-2019-0200 Trahair, R. C. S., & Bruce, K. (2012). Human relations and management consulting: Elton mayo and eric trist . Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199235049.013.0003 Tzeremes, N. G. (2020). Robust malmquist productivity measurement: Evidence from Spanish hotel industry during the Great Recession. International Journal of Productivity and Performance Management , ahead-of-print (ahead-of-print). https://doi.org/10.1108/ijppm-01-2019-0037 Wiech, B. A., Kourouklis, A., & Johnston, J. (2019). Understanding the components of profitability and productivity change at the micro level. International Journal of Productivity and Performance Management , 69 (5), 1061–1079. https://doi.org/10.1108/ijppm-10-2018-0366 Zoller, Y. J., & Muldoon, J. (2019). Illuminating the principles of social exchange theory with Hawthorne studies. Journal of Management History , 25 (1), 47–66. https://doi.org/10.1108/jmh-05-2018-0026 Zondo, R.W.D. (2018). The influence of a 360-degree performance appraisal on labour productivity in an automotive manufacturing organisation. South African Journal of Economic and Management Sciences, 21 (1), a2046. https://doi.org/10.4102/sajems.v21i1.2046 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-3824465","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264737471,"identity":"5ee7436c-9bb7-467b-ad54-bd091b30249d","order_by":0,"name":"Bruna 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Brasília","correspondingAuthor":false,"prefix":"","firstName":"Clóvis","middleName":"","lastName":"Neumann","suffix":""}],"badges":[],"createdAt":"2023-12-30 16:29:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3824465/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3824465/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49156678,"identity":"eac4a756-7c56-42d2-99a6-1c49128b376a","added_by":"auto","created_at":"2024-01-04 04:10:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":443551,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the research methodology stages\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3824465/v1/a46e764ac5e9c4fab0668d1f.png"},{"id":49156677,"identity":"9ea0b4d0-01dc-4b69-a408-b58525e1c79d","added_by":"auto","created_at":"2024-01-04 04:10:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":269755,"visible":true,"origin":"","legend":"\u003cp\u003eThematic Map\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3824465/v1/39e3ba2f95d264fd850c4512.png"},{"id":49496849,"identity":"207556cf-42e7-4a3b-b393-4c0ce3b78328","added_by":"auto","created_at":"2024-01-11 20:52:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1140843,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3824465/v1/7c4f9bba-1965-4756-8cec-b19b8faba4f5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eProductivity Management: a Systematic Review of Approaches, Trends and Future Research Agendas\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe current discourse in increasing productivity emerges within a complex framework characterized by resource scarcity, environmental degradation, heightened competitiveness, an aging workforce, and rapid technological progress (Khanna \u0026amp; Sharma, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e; K\u0026auml;pyl\u0026auml; et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This necessitates a deeper investigation into the prevailing trends and prospects for increasing productivity within a dynamic global landscape, with an overarching goal of fostering continuous societal and professional advancement (K\u0026auml;pyl\u0026auml; et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Haynes, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the literature conventionally defines productivity as the relationship between inputs and outputs (Wiech et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Czumanski \u0026amp; Lšdding, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Oeij et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; K\u0026auml;pyl\u0026auml; et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), it is a multidimensional concept. Depending on the context, its meaning can change (G\u0026uuml;nter \u0026amp; Gopp, 2021). This breadth of concepts has led to the emergence of many approaches to measuring productivity in literature and industry, resulting in conflicting definitions (G\u0026uuml;nter \u0026amp; Gopp, 2021; Oeij et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Oeij et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) study explores that productivity is related to efficiency and effectiveness, tangible and intangible assets, profitability, quality, and value creation, making it a crucial determinant in organizational performance (Wiech et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; K\u0026auml;pyl\u0026auml; et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tangen, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the last two centuries have shown that the world has evolved into a service-based economy focused on the customer (Amirul et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Even though manufacturing remains critical for economic growth, there is a clear need to investigate productivity in a scenario with intangible production, where the value of a service depends directly on customer experience (G\u0026uuml;nter \u0026amp; Gopp, 2021; Oeij et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Additionally, technological advancement is one of the most relevant and crucial factors in economic growth, and investment in this sector drives productivity performance in companies (Khanna \u0026amp; Sharma, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn a scenario where knowledge workers gain increasing prominence, there is a growing interest in exploring the productivity of workers in the organizational environment, considering that human resources constitute the largest share of both costs and revenues (Bortoluzzi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Considering also that the nature of work has changed in the last century (Amirul et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Haynes, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), productivity is not well understood where the service sector is relevant in the global economy (Khanna \u0026amp; Sharma, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, there is a lack of systematic views that allow for the classification of existing approaches to productivity measurement (G\u0026uuml;nter \u0026amp; Gopp, 2021), revealing an opportunity to explore the productivity landscape in the present. This article aims to analyze the state of the art in productivity, seeking to present the current research and propose an agenda with new challenges for future studies. The results can serve as inspiration for managers seeking to implement policies and practices that improve productivity.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 A Historical Perspective\u003c/h2\u003e \u003cp\u003eWorkplace productivity stands as a central concept in the fields of economics, management, and organizational psychology (Wiech et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Haynes, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). It refers to the efficiency with which human resources are utilized to achieve goals and objectives within an organization (Rodr\u0026iacute;guez-Pose \u0026amp; Ganau, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Increasing labor productivity has been a goal for companies and governments worldwide, as it is directly related to economic growth and social well-being (Oeji et al., 2011).\u003c/p\u003e \u003cp\u003eOne of the earliest theories addressing labor productivity was Taylor's theory, developed in the early 20th century. By applying a science of work that allowed for the selection and proper training of workers, productivity would substantially increase (Lauer Schachter, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The systematic study of a workplace and the development of procedures to enhance productivity and worker satisfaction were embraced by organizations (Hill \u0026amp; Van Buren, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Human Relations Theory, developed by Mayo in the 1930s, introduced an innovative perspective on workplace productivity. It recognized the importance of social and psychological factors in worker motivation and performance, emphasizing the relevance of interpersonal relationships, recognition, and employee well-being as drivers of productivity (Trahair \u0026amp; Bruce, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In the same context, starting in the 1950s, Maslow's Motivation Theory gained prominence, contributing to a movement that highlighted the importance of motivation to performance and commitment (Acevedo, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Many approaches emerged, aiming to understand workers' needs to spontaneously generate feelings of collaboration and belonging to create a more productive work environment (Zoller \u0026amp; Muldoon, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubsequently, Total Quality Management (TQM), originating in the late 20th century, carries evolving concepts that impact productivity studies in various ways (Bazrkar et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This theory was one of the most widely adopted management philosophies, where organizations enhance their management skills, boost performance, and strive for quality and excellence (Dahlgaard-Park et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). By seeking to improve quality in all functions and processes of the organization, carefully considering interactions between organizational elements, it is a strategy that can enhance learning and increase the competitive advantage of organizations, especially in markets where changes pose significant challenges (Bazrkar et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tortorella et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn general, leadership and organizational behavior also play a crucial role in labor productivity (Antonacopoulou \u0026amp; Georgiadou, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Different leadership theories, such as transformational and transactional leadership, discussed as early as the 1970s, emphasize how leaders motivate and influence their employees (Gerards et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, leadership support contributes to organizational eligibility, playing a fundamental role in worker motivation and organizational performance improvement (Bae et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite recent theories studying productivity, the term has been applied in various circumstances and at various levels of aggregation for over two centuries. It is one of the most important variables concerning competitive advantage, but often relegated to the background (Tangen, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The lack of a common agreement on what the definition is noticeable, causing productivity to be disregarded in various organizational settings (G\u0026uuml;nter \u0026amp; Gopp, 2021). The study by G\u0026uuml;nter and Gopp (2021) presents a synthesis of standard productivity definitions, as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExemplary collection of existing definitions of productivity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u0026thinsp;=\u0026thinsp;faculty to produce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLittr\u0026eacute; (1883)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u0026thinsp;=\u0026thinsp;units of output/units of input\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChew (1988)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u0026thinsp;=\u0026thinsp;actual output/expected resources used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSink and Tuttle (1989)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u0026thinsp;=\u0026thinsp;total income/(cost\u0026thinsp;+\u0026thinsp;goal profit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFisher (1992)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u0026thinsp;=\u0026thinsp;value added/input of production factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsp\u0026eacute;n et al. (1991)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u0026thinsp;=\u0026thinsp;efficiency*effectiveness\u0026thinsp;=\u0026thinsp;value adding time/total time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJackson and Petersson (1999)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity = (output/input)*quality\u0026thinsp;=\u0026thinsp;efficiency*utilisation*quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl-Darrab (2000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u0026thinsp;=\u0026thinsp;outputs/inputs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoelli et al. (2005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity = (output quantity*output quality)/(input quantity*input quality)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOeji et al. (2011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u0026thinsp;=\u0026thinsp;method*performance*utilisation*quality*design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlmstrom (2012)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u0026thinsp;=\u0026thinsp;total outputs/total inputs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBerhe et al. (2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u0026thinsp;=\u0026thinsp;actual output per combined unit of labour, machine and overhead, reflecting the contributions of all factors of manufacturing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRehman et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e(G\u0026uuml;nter \u0026amp; Gopp, 2021)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2. Method","content":"\u003cp\u003eThe present study, descriptive and qualitative in nature, adopted the systematic literature review (SLR) as the research method. SLR is a systematic, explicit, comprehensive, and reproducible method for identifying, evaluating and synthesizing the body of work produced on a specific theme (Par\u0026eacute; et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Okoli \u0026amp; Schabram, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). To ensure greater transparency and rigor in constructing this review, the protocol suggested by Templier and Par\u0026eacute; (2015) was used, summarizing the following six steps: problem formulation, literature search, screening for inclusion and exclusion, quality assessment, data extraction and data analysis and synthesis. The steps are synthesized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe description of the scientific production scenario regarding productivity constitutes the first step of problem formulation (i). Regarding the literature search (ii), the Scopus database was used, known for its significant number of indexed journals and quality, especially in the subject area (Chadegani et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe database search was conducted by topic and/or title, abstract and/or keywords, using the boolean OR operator for the following terms: (\"productivity\" OR \"productivity measurement\" OR \"labour productivity\" OR \"labor productivity\" OR \"productivity measure\"). There was also a filter by areas, with the following selected in Scopus: \"Business, Management and Accounting\" and \"Economics, Econometrics and Finance,\" as done in the study by Singh et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Finally, only articles and reviews published from 2018 were filtered, considering the temporal scope of the productivity review published by G\u0026uuml;nter and Gopp (2021). Considering the limitation of Scopus for the number of articles extracted, the literature search, conducted in March 2023, resulted in a total of 2,000 articles.\u003c/p\u003e \u003cp\u003eFor the screening for inclusion and exclusion step (iii), Howard et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) method was used to identify the authors who contributed most to the theme. This is based on the order of authors in a particular article, where the method assigns different weights to various authors, with the first author receiving a higher weight than the second, and so on (Lopes \u0026amp; Neumann, 2021). Thus, the articles of the top 10 authors in the ranking were selected, limiting the sample to a total of 71 articles.\u003c/p\u003e \u003cp\u003eProceeding to the quality assessment step (iv), 17 articles that did not focus on labor productivity were excluded from the sample after analyzing titles, abstracts, and keywords. The final sample totaled 54 articles extracted from Scopus, as per the penultimate proposed step (v).\u003c/p\u003e \u003cp\u003eFinally, the data analysis and synthesis step (vi) involved a bibliometric analysis of the reviewed articles to identify information about academic production on the topic, such as year of publication, countries, institutions, and journals that publish the most, as well as a general overview of the most published themes. These analyses were conducted using the R Bibliometrix software. In addition, from the comprehensive reading of the articles' texts, a spreadsheet was created based on the thematic categories used by G\u0026uuml;nter and Gopp (2021), representing the characteristics for productivity management used by the studies. Thus, the analyzed characteristics were categorized as level, type, measurement, sector, scope, quality, corporate level, and practicability, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain characteristics of productivity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMain Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubsidiary characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMicro\u003c/p\u003e \u003cp\u003eMacro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCorporate level\u003c/p\u003e \u003cp\u003eNational or industry sector level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal productivity\u003c/p\u003e \u003cp\u003ePartial productivity\u003c/p\u003e \u003cp\u003eLabour productivity\u003c/p\u003e \u003cp\u003eMachine productivity\u003c/p\u003e \u003cp\u003eMaterial productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOutput related to multiple types of input factors\u003c/p\u003e \u003cp\u003eOutput related to one types of input factor\u003c/p\u003e \u003cp\u003eOutput related to the input factor labour\u003c/p\u003e \u003cp\u003eOutput related to the input factor machines\u003c/p\u003e \u003cp\u003eOutput related to the input factor materials\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasurement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFinancial\u003c/p\u003e \u003cp\u003eQuantitative\u003c/p\u003e \u003cp\u003eQualitative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProductivity measured with monetary units\u003c/p\u003e \u003cp\u003eProductivity measured with physical units\u003c/p\u003e \u003cp\u003eProductivity measured with qualitative indicators\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eManufacturing\u003c/p\u003e \u003cp\u003eService\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOutput is physical goods\u003c/p\u003e \u003cp\u003eOutput is services\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEfficiency\u003c/p\u003e \u003cp\u003eEffectiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEconomical utilisation of resources (input)\u003c/p\u003e \u003cp\u003eFulfilment of customer requirements (output)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuality aspects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProduct and process quality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorporate level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eManagement\u003c/p\u003e \u003cp\u003eOperational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImplementation on the management level\u003c/p\u003e \u003cp\u003eImplementation on the shop floor level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePracticability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePractically validated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eValidated with a practical example\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e(G\u0026uuml;nter \u0026amp; Gopp, 2021)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1. Contribution of authors, countries, and institutions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings revealed in this section highlight the key authors, countries and institutions that have contributed to the construction of studies on the topic of productivity. It is important to note that the results presented here are not limited to the final set of 54 articles reached for this construct.\u003c/p\u003e\n\u003cp\u003eBased on the scoring system outlined by Howard et al. (1987) protocol and operationalized by Lopes \u0026amp; Neumann (2021), scores for single and multiple author publications were valid and considered in the score construction (FU et al., 2023), summarized in Table 3. The table lists the authors who scored the highest in terms of article production in the field of productivity. As mentioned in the method section, after the quality assessment step (iv), some authors and articles were excluded from the analysis as their work did not focus on workplace productivity. Following this filtering process, Table 3 presents the top 10 authors selected and highlighted in bold, constituting the final set of articles, totaling 54 reviewed works.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eTop authors in productivity research\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eScore\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eGurmu A.T.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e19.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eZondo R.W.D.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e8.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSharma C.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e8.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eJohari S.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e8.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTzeremes N.G.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e8.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChen C.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e7.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eJackson T.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e7.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eZhang D.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e6.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eLin B.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e6.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrafts N.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eJha K.N.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e6.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eGiampietro M.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e5.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRicci A.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eWang X.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKinfemichael B.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e5.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eYao Y.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiao X.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eMalanima P.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e5.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eAlston J.M.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eCaiani A.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNishi H.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e5.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs observed, authors Gurmu A. T., Zondo R. W. D. and Sharma C. achieved the highest scores in the ranking, noting that authors of single-authored articles receive higher scores, thus boosting their respective scores, as seen with Gurmu A.T., who has 5 single-authored articles.\u003c/p\u003e\n\u003cp\u003eThe country ranking, as outlined in Table 4, is conducted in the same manner as that of authors, where the score consists of the sum of each author\u0026apos;s score in their respective country. Therefore, the country corresponds to the research institution of each author, disregarding their nationality (Lopes \u0026amp; Neumann, 2021). The United States stands out quantitatively as the most influential country in the field of productivity, followed by China and the United Kingdom.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eTop countries in productivity research\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountries\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eScore\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnited States\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e468.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChina\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e273.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnited Kingdom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e216.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItaly\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e189.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAustralia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e150.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e145.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRussian Federation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e138.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e114.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGermany\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e88.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e82.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFollowing the same logic as the rankings of authors and countries, it is possible to identify, in Table 5, the most influential institutions in the field. The spotlight is on the University of California, USA, Deakin University, Australia, and Xiamen University, China, with the three highest scores.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e Top institutions in productivity research\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstitutions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eScore\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUniversity of California\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e42.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeakin University\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e23.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eXiamen University\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e18.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUniversity of Surrey\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e17.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRoma Tre University\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e17.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNational Research University Higher School of Economics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e17.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUniversity of Economics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e14.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUniversity of Warwick\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e14.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUniversity of Vigo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e13.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCornell University\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e13.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Bibliometric Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis section unveils the bibliometric outcomes for the final set of articles selected after completing the literature search stages, totaling 54 articles. Consequently, Table 6 synthesizes the results for country scientific production, most relevant affiliations, annual scientific production, and most relevant sources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u0026nbsp;\u003c/strong\u003eBibliometric results\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"573\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountries Scientific Production\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFreq\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6808%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnual Scientific Production\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFreq\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2751%;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e22,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6808%;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e24,07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2751%;\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e21,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6808%;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e22,22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2751%;\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e14,14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6808%;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e20,37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2751%;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e14,14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6808%;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e22,22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2751%;\"\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e10,10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6808%;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e7,41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2751%;\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e7,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6808%;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e3,70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMost Relevant Affiliations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFreq\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6808%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMost Relevant Sources\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFreq\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2751%;\"\u003e\n \u003cp\u003eIndian Institute of Management Lucknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e18,52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6808%;\"\u003e\n \u003cp\u003eApplied Economics Letters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e7,41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2751%;\"\u003e\n \u003cp\u003eUniversity of Thessaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e18,52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6808%;\"\u003e\n \u003cp\u003eSouth African Journal of Economic and Management Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e7,41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2751%;\"\u003e\n \u003cp\u003eUniversity of Warwick\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e11,11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6808%;\"\u003e\n \u003cp\u003eEconomic Modelling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e5,55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2751%;\"\u003e\n \u003cp\u003eDurban University of Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e9,26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6808%;\"\u003e\n \u003cp\u003eEcological Economics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e3,70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.2751%;\"\u003e\n \u003cp\u003eInternational Food Policy Research Institute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\n \u003cp\u003e7,41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6808%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9965%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5256%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs presented in Table 6, the assessment of scientific production across different countries is based on counting the contributions of authors and co-authors affiliated with these nations. In this context, Italy takes the lead with 22 contributions, followed by the United Kingdom, India, the USA, Greece, and South Africa.\u003c/p\u003e\n\u003cp\u003eConcerning the top five institutions producing studies on productivity, the Indian Institute of Management Lucknow, India, and the University of Thessaly, Greece, were responsible for publishing ten articles each. The ranking continues with the University of Warwick, UK, with six articles, and Durban University of Technology, South Africa, and the International Food Policy Research Institute, USA, with five and four articles, respectively.\u003c/p\u003e\n\u003cp\u003eRegarding annual scientific production, it is noteworthy that the peak of studies related to productivity occurred in 2018. In the three subsequent years, a similar average of publications was recorded. In contrast, the year 2022 showed a decrease in this proportion, with only four published contributions. In the contemporary business landscape, characterized by highly competitive and ever-changing global markets, survival requires mastery in the efficient and effective utilization of human, physical, technological, financial, and informational resources (Groen et al., 2019). In this context, the six most recent studies, spanning from 2022 to 2023, dedicated themselves to investigating various themes. Among them, the examination of the impact of new technologies on productivity (Cirillo et al., 2023; Crafts, 2022), the analysis of the relationship between productivity and flexibility in the labor market (Bloise et al., 2022; Cirillo \u0026amp; Ricci, 2022), the evaluation of the effect of tax reduction on incentives in job performance and average salaries (Damiani et al., 2023) and the exploration of the use of sigmoidal and bimodal growth curves in understanding how internalization affects organizational productivity (Tsionas \u0026amp; Tzeremes, 2022) stand out. The diversity of addressed topics reflects the search for understanding evolving trends in society and the professional environment. This approach provides a rich landscape for identifying new opportunities and challenges in the pursuit of productivity improvement, as already indicated by K\u0026auml;pyl\u0026auml; et al. (2010).\u003c/p\u003e\n\u003cp\u003eConcerning the most relevant journals, the renowned journals Applied Economics Letters, UK, and South African Journal of Economic and Management Sciences, South Africa, led with the publication of four articles each, while the last two, Economic Modelling and Ecological Economics, Netherlands, contributed with three and two studies, respectively.\u003c/p\u003e\n\u003cp\u003eAs outlined by Aria et al. (2022) and Cobo et al. (2011), the thematic map portrayed in Figure 2 allows for the identification of four distinct types of themes, varying based on the quadrant in which they are located:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eMotor Themes: These exhibit higher values of centrality and density, signifying well-developed themes that are crucial for framing the conceptual framework. Motor themes in Cluster 1 include labor productivity, manufacturing, and economic growth, while in Cluster 2, they encompass technological development, total factor productivity and economic history.\u003c/li\u003e\n \u003cli\u003eNiche Themes: These display higher values of centrality and lower values of density. They possess well-developed internal ties, characterizing more specialized and peripheral themes. Among the niche themes are technological change, financial crisis and Canary Islands.\u003c/li\u003e\n \u003cli\u003eEmerging or Declining Themes: These showcase lower values of centrality and density, defining themes that are less developed, either emerging or declining. As indicated in the map, population growth is identified as a declining theme.\u003c/li\u003e\n \u003cli\u003eBasic Themes: These present lower values of centrality and higher values of density. While important for the research field, they are less developed, grouping together overarching and general themes. Examples include productivity and finance.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eUpon scrutinizing the motor themes, a total of 23 articles delved into the topics of Cluster 1, namely labor productivity, manufacturing, and economic growth. Understanding how changes in workforce distribution over recent decades can impact labor productivity is crucial. Many developing countries made significant strides in economic development in the 1990s, accompanied by a shift in labor allocation from agriculture to industry in developing countries and from industry to services in high-income countries, as indicated in the studies by Kinfemichael (2019). However, it is noteworthy that, despite the world evolving into a service-based economy (Amirul et al., 2021), there is still a focus on studies addressing productivity in the manufacturing sector, as seen in the works of Khanna and Sharma (2018a), Zondo (2018) and Singh and Sharma (2020).\u003c/p\u003e\n\u003cp\u003eContinuing with the motor themes, Cluster 2 (technological development, total factor productivity and economic history), with an incidence of 13 articles, allows for interpreting how technological change is the ultimate source of sustained productivity growth and, consequently, long-term improvements in living standards (Bakker et al., 2019). The context of artificial intelligence development, the digital revolution and robotics heightens concerns about how technologies will impact productivity in organizations (Crafts, 2018). Economic growth may result from the use of additional labor or capital resources, or from the more efficient use of these resources, which already constitutes technological change, represented by the growth of total factor productivity (TFP) (Bakker et al., 2019).\u003c/p\u003e\n\u003cp\u003eThe niche themes, with nine articles, focused on topics such as technological change, financial crisis and the Canary Islands. Tzeremes\u0026apos; (2020) findings, investigating the productivity levels of hotels operating in the Balearic Islands and the Canary Islands during the pre-crisis period, the 2008 U.S. real estate crisis, the global financial crisis of 2010, and the post-crisis period, showed that productivity did not deteriorate due to the adverse effects of the economic crisis, mainly due to long-term investment in innovation policies that helped the hotel industry quickly recover from the Great Recession.\u003c/p\u003e\n\u003cp\u003eAs for emerging or declining themes, two articles debate population growth, primarily investigating productivity growth during the British Industrial Revolution. Crafts (2021) concluded that the literature has become overly pessimistic about the implications of productivity growth on real consumption gains and the labor income share in the economy, emphasizing the importance of thinking in terms of hypothetical scenarios, especially regarding the implications of population growth, but the interactions between demography and technology are still not well understood.\u003c/p\u003e\n\u003cp\u003eThe cluster of basic themes, featuring productivity and finance, had an incidence of seven articles. As pointed out by Sharma (2019), to remain competitive, organizations need to invest in technology and innovation, enabling increased productivity. In lower-income countries, companies may hesitate to invest in technology due to risks, uncertainties, and a lack of access to external financing. Additionally, the decision to invest in R\u0026amp;D may depend on the relationship between costs, risks, and potential returns, as well as the ability to acquire technology from more developed countries. This underscores that the economic context significantly impacts organizations seeking to innovate and increase their productivity. The characteristics used by studies to manage productivity are presented in the next section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Productivity management characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcerning the characteristics used for productivity management, the 54 reviewed articles presented distinct features in productivity management. Therefore, the studies were categorized into eight main characteristics, considering the multidimensional aspect of productivity. The results can be found in Table 7.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7.\u003c/strong\u003e Main characteristics of reviewed studies\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"566\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSubsidiary characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMicro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.80904522613066%\"\u003e\n \u003cp\u003eMacro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e56%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLabour productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.80904522613066%\"\u003e\n \u003cp\u003eTotal productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e51%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eMeasurement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQuantitative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.80904522613066%\"\u003e\n \u003cp\u003eFinancial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e33%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.80904522613066%\"\u003e\n \u003cp\u003eQualitative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eSector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eManufacturing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.80904522613066%\"\u003e\n \u003cp\u003eService\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e39%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.80904522613066%\"\u003e\n \u003cp\u003e\u0026nbsp;Not applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eScope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEfficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.80904522613066%\"\u003e\n \u003cp\u003eEffectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.80904522613066%\"\u003e\n \u003cp\u003eNot applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e13%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eQuality aspects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.80904522613066%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e94%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eCorporate level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOperational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.80904522613066%\"\u003e\n \u003cp\u003eManagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.80904522613066%\"\u003e\n \u003cp\u003eNot applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e57%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003ePractically validated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.80904522613066%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.09547738693467%\"\u003e\n \u003cp\u003e17%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMicro perspective was the most common characteristic in the studies when it comes to productivity management, with a total of 33 cases, in contrast to a less frequent macro-level approach, with 26 identified cases. Regarding types of productivity, it is interesting to note that, although G\u0026uuml;nter and Gopp\u0026apos;s original study (2021) presented various categories, this review mainly identified two types of productivity: labor productivity, with 27 cases, and total productivity, with 28 cases.\u003c/p\u003e\n\u003cp\u003eThe quantitative approach to measuring productivity was the most frequent, with 43 cases, while the financial perspective was adopted 21 times, and qualitative measurement was not adopted in any of the analyzed studies. We also identified that productivity management was widely recognized in both the manufacturing sector, with 42 cases, and the service sector, with 30 cases. Furthermore, the identification of four studies that did not restrict themselves to a specific sector highlights the multifaceted approach to productivity management in different domains.\u003c/p\u003e\n\u003cp\u003eThe analysis of the scope adopted for productivity management reveals that efficiency was widely addressed in 46 cases, while effectiveness received less attention, with only six cases. In eight situations, it was not possible to clearly identify the scope of productivity management. This raises the question of how these studies defined and operationalized these concepts and whether there is room for a deeper understanding of effectiveness in the context of productivity management. On the other hand, it is noteworthy that qualitative aspects of productivity management were not identified in most cases, with only three studies addressing this dimension. This gap suggests an opportunity for future research to explore qualitative aspects of productivity management and how they can complement the predominant quantitative approaches.\u003c/p\u003e\n\u003cp\u003eRegarding the selection of the corporate level for productivity management, we note that in 16 cases, the focus was on the managerial level, while in seven cases, the operational level was the central point of analysis. However, it is important to highlight that in a significant portion of studies, precisely 31 of them, there was no clear specification regarding the specific corporate level addressed in their analyses. Finally, it is worth noting that 45 articles sought to validate the practical feasibility of productivity management, while only nine articles did not address this analysis.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis systematic literature review aimed to provide a comprehensive and in-depth understanding of the various approaches and characteristics associated with productivity management, addressing the identified research gap. From the contributions of classical theories like Taylor's Scientific Management to contemporary perspectives such as Total Quality Management, an evolution in the understanding and practice of productivity management over time was identified.\u003c/p\u003e \u003cp\u003eThe bibliometric results not only highlighted the extensive scientific production on productivity but also outlined the significant contributions of authors, countries, and institutions in this field, reflecting the globalization of interest and knowledge related to productivity. The thematic analysis, as expressed in the thematic map, revealed distinct clusters such as motor themes, niche themes, emerging themes, and basic themes, providing a comprehensive overview of well-developed, specialized, emerging, or declining areas in the field of productivity. The detailed analysis of productivity management characteristics, covering different corporate levels, measurement approaches and scopes, offered valuable insights into the diversity of practices and approaches adopted by the reviewed studies.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Future Challenges\u003c/h2\u003e \u003cp\u003eDespite the significant advances identified in this review, several research opportunities stand out for future investigations. Firstly, a deeper exploration of the qualitative aspects of productivity management, given their relative neglect in the reviewed literature, can enrich the predominant quantitative analyses.\u003c/p\u003e \u003cp\u003eExploring the interaction between efficiency and effectiveness in the context of productivity management can provide valuable insights for organizations seeking to optimize their resources sustainably. A more in-depth analysis of the factors influencing these two elements can open new theoretical and practical perspectives. Research into productivity management in specific sectors, such as the service industry, and consideration of contextual variables, such as the impact of technology and innovation, also represent promising areas for future research. Understanding how different organizational contexts respond to productivity management demands can contribute to more adaptive and effective strategies. A more detailed analysis of the practical implications of productivity management at different corporate levels can provide more specific guidance for leaders and managers, helping them make informed decisions. In summary, this review provides a solid foundation for future exploratory and confirmatory research, highlighting existing gaps and opportunities to advance the understanding and practice of productivity management.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eB.B. - Writing - original draft; Conceptualization; Methodology; Formal analysis; Data curation; Software.M.L. - Writing - original draft; Methodology; Formal analysis; Data curation.L.C. - Writing - review \u0026amp; editing; Data curation; Formal analysis.J.S., L.C. and D.B. - Data curation; Formal analysis.P.M. - Project administration; Supervision; Conceptualization.A.S. - Project administration; Funding acquisition.C.N. - Methodology; Funding\u0026nbsp;acquisition; Conceptualization; Software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatements and Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received to assist with the preparation of this manuscript. The authors certify that this scientific text does not constitute multipart or partial results of the same study and represents original research. The authors also declare no financial interests or conflicts of interest regarding this scientific text. This manuscript has not been submitted to any other journal for consideration, nor is it currently under review by another periodical.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcevedo, A. (2015). A personalistic appraisal of maslow\u0026rsquo;s needs theory of motivation: From \u0026ldquo;humanistic\u0026rdquo; psychology to integral humanism. \u003cem\u003eJournal of Business Ethics\u003c/em\u003e, \u003cem\u003e148\u003c/em\u003e(4), 741\u0026ndash;763. https://doi.org/10.1007/s10551-015-2970-0\u003c/li\u003e\n\u003cli\u003eAmirul, S. R., Pazim, K. H., Amirul, S. M., Mail, R., \u0026amp; Dasan, J. (2021). 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The influence of a 360-degree performance appraisal on labour productivity in an automotive manufacturing organisation. \u003cem\u003eSouth African Journal of Economic and Management Sciences,\u003c/em\u003e \u003cem\u003e21\u003c/em\u003e(1), a2046. https://doi.org/10.4102/sajems.v21i1.2046\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":"Productivity, Productivity Management, Systematic Literature Review, International Production, Research Challenges","lastPublishedDoi":"10.21203/rs.3.rs-3824465/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3824465/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis systematic literature review (SLR) aims to explore various approaches and characteristics related to productivity management, addressing an identified gap in the literature. The article seeks to present the current landscape of studies, identify international scientific production trends, and propose an agenda with new challenges for future research. The present SLR used the Scopus database with constructs published from 2018 onwards. Bibliometric results highlight extensive scientific production on productivity, outlining key contributions from authors, countries, and institutions in the field. Thematic analysis provides a comprehensive view of well-developed, specialized, emerging or declining areas in productivity. A detailed analysis of different productivity measurement approaches offers valuable insights into the diversity of practices adopted by reviewed studies. Future challenges and research opportunities include a deeper understanding of qualitative aspects of productivity management, exploring the interaction between efficiency and effectiveness, and investigating productivity management in specific sectors. This SLR provides a solid foundation for future research, emphasizing existing gaps and opportunities for advancements in understanding productivity management.\u003c/p\u003e","manuscriptTitle":"Productivity Management: a Systematic Review of Approaches, Trends and Future Research Agendas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-04 04:09:45","doi":"10.21203/rs.3.rs-3824465/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":"125b6a87-ffdb-4829-b3ea-2efaae4b73a6","owner":[],"postedDate":"January 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-11T20:44:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-04 04:09:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3824465","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3824465","identity":"rs-3824465","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}