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The objective of the study is to systematically review the existing research in the topical domain of farmers’ direct link with supermarkets. The authors present a state-of-the-art structure of the field and provide directions for future research in the domain. The major aim of this study is to synthesize the research field and answer some specific questions, like what do we know about this field and where should we be heading.

A pool of 275 articles published from 2002 to 2022 were retrieved from the Scopus database and analyzed using the R-based Biblioshiny and visualized using VOSviewer. The research design is a mix of quantitative bibliometric technique and qualitative content analysis. Bibliometric method ensures the objectivity while content analysis ensures the scholarly evaluation.

Major findings include production trend, dominant keywords, leading publication outlets and country-wise analysis of the selected articles. The authors found that sub-domains like economic aspects, participation hurdles and the rise of supermarkets are the most researched topics while operational issues, their pragmatic solutions, sustainability and innovation are the emerging sub-fields that need more academic attention.

The major limitation is the use of single data source, i.e. Scopus, and it is quite possible that useful studies that are not covered by Scopus remain excluded.

This study is one of the first attempts to systematically review the previous research on the selected topic. It will help researchers to understand the present status, identify future research directions, and pursue more reasonable and relevant topics of research.

The purpose of this paper is to investigate the role of firms’ features on innovation performance in Industry 4.0, focusing on the concepts of breadth and depth of openness in the innovation process.

Using data gathered from 96 firms active in Industry 4.0 (I4.0) in Italy, a Poisson regression analysis is conducted to investigate the relationship between the openness of firms’ innovation processes at the level of knowledge sources and their innovation performance in I4.0.

The results highlight the relationship between the level of openness and innovative performance in I4.0. In particular, the breadth of the openness of the innovation process of enterprises is curvilinearly related to innovation in I4.0, taking an inverted U-shape.

Managers of firms operating in I4.0 should consider openness as a strategic response to the knowledge requirements and risks associated with the innovation process in complex technologies.

Through the questionnaires administered mainly to highly qualified individuals, an original and unique database has been created with information on the openness of the innovative process and the innovation performances in I4.0.

The research explores the shift to Quality 4.0, examining the move towards a data-focussed transformation within organizational frameworks. This transition is characterized by incorporating Industry 4.0 technological innovations into existing quality management frameworks, signifying a significant evolution in quality control systems. Despite the evident advantages, the practical deployment in the Indian manufacturing sector encounters various obstacles. This research is dedicated to a thorough examination of these impediments. It is structured around a set of pivotal research questions: First, it seeks to identify the key barriers that impede the adoption of Quality 4.0. Second, it aims to elucidate these barriers' interrelations and mutual dependencies. Thirdly, the research prioritizes these barriers in terms of their significance to the adoption process. Finally, it contemplates the ramifications of these priorities for the strategic advancement of manufacturing practices and the development of informed policies. By answering these questions, the research provides a detailed understanding of the challenges faced. It offers actionable insights for practitioners and policymakers implementing Quality 4.0 in the Indian manufacturing sector.

Employing Interpretive Structural Modelling and Matrix Impact of Cross Multiplication Applied to Classification, the authors probe the interdependencies amongst fourteen identified barriers inhibiting Quality 4.0 adoption. These barriers were categorized according to their driving power and dependence, providing a richer understanding of the dynamic obstacles within the Technology–Organization–Environment (TOE) framework.

The study results highlight the lack of Quality 4.0 standards and Big Data Analytics (BDA) tools as fundamental obstacles to integrating Quality 4.0 within the Indian manufacturing sector. Additionally, the study results contravene dominant academic narratives, suggesting that the cumulative impact of organizational barriers is marginal, contrary to theoretical postulations emphasizing their central significance in Quality 4.0 assimilation.

This research provides concrete strategies, such as developing a collaborative platform for sharing best practices in Quality 4.0 standards, which fosters a synergistic relationship between organizations and policymakers, for instance, by creating a joint task force, comprised of industry leaders and regulatory bodies, dedicated to formulating and disseminating comprehensive guidelines for Quality 4.0 adoption. This initiative could lead to establishing industry-wide standards, benefiting from the pooled expertise of diverse stakeholders. Additionally, the study underscores the necessity for robust, standardized Big Data Analytics tools specifically designed to meet the Quality 4.0 criteria, which can be developed through public-private partnerships. These tools would facilitate the seamless integration of Quality 4.0 processes, demonstrating a direct route for overcoming the barriers of inadequate standards.

This research delineates specific obstacles to Quality 4.0 adoption by applying the TOE framework, detailing how these barriers interact with and influence each other, particularly highlighting the previously overlooked environmental factors. The analysis reveals a critical interdependence between “lack of standards for Quality 4.0” and “lack of standardized BDA tools and solutions,” providing nuanced insights into their conjoined effect on stalling progress in this field. Moreover, the study contributes to the theoretical body of knowledge by mapping out these novel impediments, offering a more comprehensive understanding of the challenges faced in adopting Quality 4.0.

This paper aims to identify the basis of the technological anxiety phenomenon by defining the differences and similarities in terms of barriers of the implementation of Industry 4.0 technologies across industrial processing sector.

This paper presents a qualitative, exploratory research, and the authors apply the cross-case study method. The study is based on interviews with representatives of 11 medium-sized and large companies from industrial processing sector; specifically, the authors focus on three industries: automotive, food and furniture.

The research showed that there are similarities as well as differences in terms of identified barriers between individual industries. Taking into account the various dimensions of technological anxiety, similarities are visible, in particular, in the case of Internal processes and infrastructure and human resources, while in the other two dimensions, i.e. strategic planning and standards and security, differences between the sectors were noted.

The developed list of barriers can be a starting point for middle and senior managers of manufacturing companies to understand the sources of technological anxiety. The planning and introducing preventive and protective tools during Industry 4.0 implementation may reduce the occurrence of technological anxiety and thus ensure a smoother adoption of technologies 4.0, while respecting the organizational culture.

This work contributes to in-depth understanding of multifaced technological anxiety phenomenon. This paper classifies dimensions of existing barriers, increases the awareness on the difficulties during transformation process and, thus enables the improvement of the use of company’s internal potential.

Unlike traditional industries, the e-cigarette is an epoch-making innovative product originating in China and occupying an absolute competitive advantage in the international market. The traditional A-U model describes the laws and characteristics of technological innovation in developed countries. In contrast, the inverse A-U model depicts the process of “secondary innovation” in late-developing countries through digestion and absorption. This paper aims to find out that if the e-cigarette, as a “first innovation” industry in a late-developing country, conform to the A-U model or conform to the “inverse A-U model”.

This paper takes the patent data of e-cigarettes from 2004 to 2021 as the research object, and uses Python’s Jieba segment words to divide product innovation and process innovation, and then uses statistical analysis methods to conduct empirical analyses on these data.

Thus, an improved A-U model suitable for the e-cigarette industry is proposed. In this model, product innovation in the e-cigarette industry appeared earlier than process innovation, but the synchronous development of product and process innovation is not lagging. The improved A-U model in the e-cigarette industry is not only different from the traditional A-U model but also does not conform to the inverse A-U model.

It is conducive to expanding and clarifying the theoretical contribution and applicable boundaries of the A-U model and has sparked thinking and exploration of the A-U model in e-cigarettes and emerging industries.

On this basis, suggestions on the development path and countermeasures of the e-cigarette industry are put forward.

Based on the e-cigarette industry, this paper takes patents as the research object and provides the method of dividing product innovation and process innovation, and proposes an A-U model suitable for the e-cigarette industry on this basis. By comparing the traditional A-U model with the inverse A-U model in latecomer countries, the background and causes of e-cigarette A-U model heterogeneity are analyzed from different stages and overall morphology. Based on this, the heterogeneity characteristics of e-cigarette innovation are summarized and sorted out.

This study aims to conduct performance and clustering analyses with the help of Digital Government Reference Library (DGRL) v16.6 database examining the role of emerging technologies (ETs) in public services delivery.

VOSviewer and SciMAT techniques were used for clustering and mapping the use of ETs in the public services delivery. Collecting documents from the DGRL v16.6 database, the paper uses text mining analysis for identifying key terms and trends in e-Government research regarding ETs and public services.

The analysis indicates that all ETs are strongly linked to each other, except for blockchain technologies (due to its disruptive nature), which indicate that ETs can be, therefore, seen as accumulative knowledge. In addition, on the whole, findings identify four stages in the evolution of ETs and their application to public services: the “electronic administration” stage, the “technological baseline” stage, the “managerial” stage and the “disruptive technological” stage.

The output of the present research will help to orient policymakers in the implementation and use of ETs, evaluating the influence of these technologies on public services.

The research helps researchers to track research trends and uncover new paths on ETs and its implementation in public services.

Recent research has focused on the need of implementing ETs for improving public services, which could help cities to improve the citizens’ quality of life in urban areas. This paper contributes to expanding the knowledge about ETs and its implementation in public services, identifying trends and networks in the research about these issues.

The purpose of this study is to examine the association between the combined roles of chief executive officer (CEO)-chairman titles (CEO duality) and investment efficiency, defined as a lower deviation from expected investment for targeted S-curve firms used to propel an innovation-driven economy. This study also aims to investigate the moderating effect of financial reporting quality on this association.

This paper focuses on the ten targeted S-curve industries – under the definition of the Thailand 4.0 model – listed on the Stock Exchange of Thailand (SET) from 2000 to 2019. Data related to CEO/chairman titles and investment supports were manually collected from the annual reports, the SET market analysis and reporting tool database and the company websites. Financial data used to estimate investment behaviors and discretionary accruals were extracted from 1999. The study analyzes unbalanced panel data using fixed-effects regressions. Additional tests embrace replacing the sample with nontargeted firms, partitioning into granted and nongranted firms, adding CEOs’ demographic moderators, using alternative variable measures and analyzing for lagged independent variables.

The main findings show that CEO duality reduces overinvestment but worsens underinvestment in targeted firms. Financial reporting quality (FRQ) appears to strengthen CEO duality in mitigating extreme spending but has no impact on the association between CEO duality and underinvestment. Additional results, for example, conclude that CEO duality has no association with both over- and underinvesting at nontargeted firms, but its effect becomes positively significant on overinvestment when financial reporting quality is high. The negative association between CEO duality and overinvestment is found only in government-granted and targeted firms. FRQ encourages CEO duality in lowering overinvestment among targeted firms without grants. CEOs’ female and serviced early years appear to elevate those main findings.

These findings assist innovative corporations in choosing a proper leadership structure to cope with investment inefficiency. The research gives the government and regulatory bodies an insight into the qualifications of the leadership structure and financial information that helps them put forward effective policies.

To the best of the author’s knowledge, this study is among the first to establish the association between CEO duality and investment efficiency for innovation-driven firms in a transforming economy. The study fills the gap in the literature on management, accounting and finance by unveiling the interplay between dual leadership and financial reporting in affecting the efficiency of investments.

The purpose of this paper is to evaluate if, given personal, supply-related features, and labour demand-related variables, there is a difference in the share of women finding more stable jobs with respect to men, in an eight-year time span.

Fragmentation leads to a lower probability of transitioning into more certain, full-time work positions. The authors analyse a rich cohort of dependent workers in Emilia-Romagna to investigate whether part-time jobs lead to full-time jobs in a “stepping-stone” fashion and whether this happens with the same probability for men and women. The focus is on the cost of part-time jobs rather than the contrast between permanent and temporary jobs, as often observed in the literature. The authors also evaluate the transition between part-time job formulae and open-ended work arrangements to determine whether women's transition to full-fledged, stable work positions is slightly rarer than their male counterparts. Even if the authors allow for the fact that part-time contracts can be a choice and not an obligation, these contracts generate more flexibility in managing the equilibrium between private and work life and create more uncertainty than full-time contracts because of the fragmentation associated with these arrangements.

The authors find that women have a more fragmented working career than men, in that they hold more contracts than men in the same time span; moreover, the authors find that part-time jobs act more as bottlenecks for women than for men.

The authors use a large administrative dataset with over 600,000 workers observed in the 2008–2015 time span, in Emilia Romagna, Italy. The authors can disentangle the number of contracts per worker and observe individual, anonymise personal features, that the authors consider in the authors' propensity score estimate. The authors ran a robustness check of the PSM estimates through coarsened exact matching (CEM).

This paper aims to critically examine the accounting and information systems literature to understand the changes that are occurring in the management accounting profession. The changes the authors are interested in are linked to technology-driven innovations in managerial decision-making and in organizational structures. In addition, the paper highlights research gaps and opportunities for future research.

The authors adopted a grounded theory literature review method (Wolfswinkel et al., 2013) to achieve the study’s aims.

The authors identified four research themes that describe the changes in the management accounting profession due to technology-driven innovations: structured vs unstructured data, human vs algorithm-driven decision-making, delineated vs blurred functional boundaries and hierarchical vs platform-based organizations. The authors also identified tensions mentioned in the literature for each research theme.

Previous studies display a rather narrow focus on the role of digital technologies in accounting work and new competences that management accountants require in the digital era. By contrast, the authors focus on the broader technology-driven shifts in organizational processes and structures, which vastly change how accounting information is collected, processed and analyzed internally to support managerial decision-making. Hence, the paper focuses on how management accountants can adapt and evolve as their organizations transition toward a digital environment.

The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.

Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.

The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.

The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.

The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.

The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.

This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.