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When coping with uncertainties, three characteristics distinguish firms: agility, adaptability and alignment (triple-A). Based on significant field research, the triple-A highlights the significance of coordinating agility, adaptability and alignment. Start-ups are facing a lot of challenges in this turbulent environment. However, this sector is undergoing a major transformation. Agility, adaptability and alignment concepts have had a major influence on the supply chain, but their implementation in start-ups has been less visible. This paper aims to identify, analyze and categorize the enablers for agility, adaptability and alignment in start-ups using the total interpretive structural modeling (TISM) approach.

In addition to the scheduled interview, a closed-ended questionnaire was used to collect data. To identify how the factors interact, the TISM technique is used, and the Matriced’Impacts Croises-Multipication Applique’ and Classment method is used to rank and categorize the agility, adaptability and alignment enablers.

This study identified ten agility, adaptability and alignment factors for start-ups. It has been found that the key importance should be given to management involvement, conflict management, collaboration and information integration.

This study primarily focused on the agility, adaptability and alignment factors in start-ups.

This study will help academics and key stakeholders understand the aspects that lead to agility, adaptability and alignment in start-ups.

Agility, adaptability and alignment concepts have had a major influence on the supply chain, but their implementation in start-ups has been less visible. Therefore, this is a novel attempt in this industry’s agility, adaptability and alignment.

Natural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption, which is achieved by developing supply chain factors with Artificial Intelligence (AI) and Big Data Analytics (BDA).

This research examines how organizations using AI and BDA can bring resilience to supply chains. To achieve the objective, the authors developed the methodology to gather useful information from the literature studied and developed the Total Interpretive Structural Modeling (TISM) by consulting 44 supply chain professionals. The authors developed a quantitative questionnaire to collect 229 responses and further test the model. With the analysis, a conceptual and comprehensive framework is developed.

A major finding, this research advocates that supply chain resilience is contingent upon utilizing supply chain analytics. An empirical study provides further evidence that the utilization of supply chain analytics has a positive and favorable effect on the flexibility of demand forecasting to inventory management, resulting in increased efficiency.

Few studies demonstrate the impact of advanced technology in building resilient supply chains by enhancing their factors. To the best of the authors' knowledge, no earlier researcher has attempted to infuse AI and BDA into supply chain factors to make them resilient.

Facilities management (FM) organizations are pivotal in enhancing the resilience of buildings against climate change impacts. While existing research delves into the adoption of digital technologies by FM organizations, there exists a gap regarding the specific utilization of artificial intelligence (AI) to address climate challenges. This study aims to investigate the drivers and barriers influencing the adoption and utilization of AI by South African FM organizations in mitigating climate change challenges.

This study focuses on South Africa, a developing nation grappling with climate change’s ramifications on its infrastructure. Through a combination of systematic literature review and an online questionnaire survey, data was collected from representatives of 85 professionally registered FM organizations in South Africa. Analysis methods employed include content analysis, Relative Importance Index (RII), and Total Interpretative Structural Modeling (TISM).

The findings reveal that regulatory compliance and a responsible supply chain serve as critical drivers for AI adoption among South African FM organizations. Conversely, policy constraints and South Africa’s energy crisis emerge as major barriers to AI adoption in combating climate change challenges within the FM sector.

This study contributes to existing knowledge by bridging the gap in understanding how AI technologies are utilized by FM organizations to address climate challenges, particularly in the context of a developing nation like South Africa. The research findings aim to inform policymakers on fostering a conducive environment for FM organizations to harness AI in fostering climate resilience in built assets.

In the era of multiple global disruptions, firms are finding it to continue their business. MSMEs are impacted more as they have constrained resources. Organizational flexibility has emerged as an organizational and management principle that would help firms stay competitive even in volatile markets. This study aims to present a set of guidelines and insights for MSME managers to implement organizational flexibility in their organizations.

This study uses total interpretive structural modelling to study how the various factors contributing to organizational flexibility behave together. Behavioural theory is used to explain why organizations need to incorporate flexibility, and systems theory of organization is used to explain why an organization needs to have open boundaries.

Organizational flexibility is a principle that may be supported by the systems theory of organization. The study has shown that it is important for MSMEs to have supply chain collaborations to be more flexible. The study also shows pressure from competitors as the key driver that would make a firm more flexible, and that adequate support from management and technological skills are required to drive flexibility in an organization.

Single respondent bias may have occurred in this study. This can be eliminated by interviewing multiple people from the same organization. Further research around the reasoning for linkages can be explored with theory-driven grounded studies.

This study attempts to use a multi-criteria decision-making technique to present insights to managers to help them make their organizations flexible.

The aim of this study is to examine the effect of aural accounting (AA) on companies’ stakeholder relationship capability in the Iranian capital market.

In terms of methodology, this study adopts a mixed approach based on both inductive and deductive foundations. The goal is to develop a comprehensive theoretical framework regarding the feasibility and practical implementation of AA and to assess its impact on the ability to interact with stakeholders. In this research, the components of AA were identified through a meta-synthesis process. Subsequently, data on these variables were collected using a researcher-made questionnaire. Additionally, a standard questionnaire was used to measure the stakeholders' interaction capability. The study used partial least squares structural equation modeling for hypothesis testing. A total of 412 participants, consisting of financial managers and heads of accounting departments of capital market companies, were involved in hypothesis testing.

The results of this study indicate that AA has a significant and positive impact on companies’ stakeholder relationship capabilities. In other words, AA establishes a bidirectional flow of information, enabling companies to demonstrate a more appropriate response to the changing needs of their stakeholders.

To the best of the authors’ knowledge, this study represents the first research to present a framework for AA and examine its impact on companies' interaction capabilities with stakeholders. Consequently, the findings not only contribute to the expansion of theoretical literature in accounting and financial reporting but also provide multiple practical implications for capital market policymakers and standard setters regarding the potential consequences of AA.

The purpose of this study is to explore the increased uptake of Artificial Intelligence (AI) technology by Facility Management (FM) organizations for enhanced operational efficiency and competitive advantage. While AI adoption in FM has been widely reported, limited attempts have been made to assess its impact on the social well-being of FM employees. To contribute towards addressing this gap, this study established the essential employee social well-being factors mostly impacted by the adoption of AI in South African FM organizations.

A four-stage design comprising a comprehensive review of literature, expert interviews, questionnaire census and focus group discussion sessions was used to elicit data from a sample of participants drawn from 22 South African FM organizations. The data was analyzed using a combination of content analysis, relative importance index and interpretative structural modeling for various data sets toward achieving the study’s objectives.

Sixteen employee social well-being factors, classified under job satisfaction, social relationship and knowledge development categories, respectively, were identified as being impacted by AI adoption in FM organizations. Furthermore, it was established that job security, job autonomy and professional status, which belong to the job satisfaction social well-being factor category, were deemed by FM employees as being mostly impacted by AI adoption.

The enhanced understanding of the impact of AI adoption on FM employees’ social well-being factors will contribute to the development of a collaborative intelligence framework for managing AI adoption in FM organizations toward engendering optimal AI–FM employee relationships for improved productivity.

Besides being one of the foremost studies to investigate the impact of AI adoption on FM employees’ social well-being, this study introduces a hierarchical framework of understanding employee social well-being factors based on multi-stakeholder perspectives.

Small and medium enterprises (SMEs) significantly contribute to economic growth, development, exports and employment of the nations. To maintain competitiveness in today's market, SMEs must explore and identify enablers to enhance their digital transformation process. This paper aims to shed light on some essential enablers SMEs can use to implement digital resilience successfully.

The quantitative assessment and validation of the enablers have been done using powerful and novel techniques, namely, the Delphi method, “fuzzy interpretive structural modelling” (F-ISM) method and “cross-impact matrix multiplication applied to classification (MICMAC)” analysis. The F-ISM model is developed using the information drawn from digital transformation experts and practitioners involved in the digital transformation process for SMEs. Furthermore, the F-ISM model provides four paths to complete the pathway to digital resilience.

The F-ISM and MICMAC analysis revealed four ways to enhance the digital transformation process in SMEs. These enterprises can utilise these path assessments to become digitally resilient in the present dynamic scenario. To enhance digital resilience among SMEs, the study identified ten enablers. Among these, “management competencies” was the most crucial, followed by “knowledge management” and “monitoring and controlling”.

The present study is limited in that the data used to develop the models were collected from a small group of industry experts whose opinions may not exhibit the comprehensive views of the population.

The findings can help SMEs enhance the digital transformation process by taking up different pathways to integrate the various enablers of digital resilience depending on resource availability.

The results indicate the most critical and influential enablers for enhancing digital resilience among SMEs. This research can be valuable to academicians, industry practitioners and researchers for guiding their future work.

This study aims to address the critical challenge of implementing lean manufacturing (LM) in emerging economies, where sustainability complexities on the production floor hinder production efficiency and the transition towards a circular economy (CE). Addressing a gap in existing research, the paper introduces a path analysis model to systematically identify, prioritize and overcome LM implementation barriers, aiming to enhance performance through strategic removal.

The authors used a mixed-method approach, combining empirical survey data with literature reviews to pinpoint key LM barriers. Using the grey-based Decision-Making Trial and Evaluation Laboratory (DEMATEL) along with the Network Knowledge (NK) method, they mapped causal relationships and barrier intensities. This formed the basis for developing a path simulation algorithm, integrating heuristic considerations for practical decision-making.

This analysis reveals that the primary barriers to LM adoption is the negative perception and inadequate understanding of lean tools and CE principles. The study provides a strategic framework for managers, offering new insights into barrier prioritization and overcoming strategies to facilitate successful LM adoption.

This research provides a strategic pathway for overcoming LM implementation barriers, empowering managers in emerging economies to enhance sustainability and competitive advantage through LM and CE integration. It emphasizes the significance of structured barrier management in the manufacturing sector.

This research pioneers a systematic exploration of LM implementation barriers in the CE context, making a significant contribution to the literature. It identifies, evaluates barriers and proposes a practical model for overcoming them, enriching sustainable manufacturing practices in emerging markets.

Despite the wide use of quantitative assessment to identify the relationship between green logistics (GL) practices and the sustainability performance (SP) of firms, results of these studies are inconsistent. A lack of theoretical foundation has been cited as a potential reason for these contradictory findings. This study aims to explore the relationship between GL practices and SP qualitatively and to provide a theoretical foundation for this link.

Following a multi-methodology approach, the authors used the grounded theory method (GTM) to investigate perceived relationships through qualitative analysis and adopted the system thinking (ST) approach to identify causal relationships using causal loop diagrams (CLDs).

The authors identified different sustainability practices under three major categories: logistics capabilities, resource-related practices and people-related practices. This analysis showed the relationships among these practices are non-linear. Based on the results, the authors developed three propositions and introduced a theoretical foundation for the relationship between GL practices and SP.

Managerial personnel can use the theoretical foundation provided by this study when making decisions on GL practices adoption. This theoretical foundation suggests applying a holistic approach that can help optimize SP by selecting suitable practices. On the other hand, researchers can use a multi-methodology approach suggested by this study to explore complex social issues.

This study contributes to the knowledge from a methodology perspective as no previous studies have been conducted to identifying the relationship between GL practices and SP by combining GTM and ST approaches. This combination can be extended to build system dynamics models for sustainable logistics impacts bringing novelty to the research field of sustainable logistics.

The digital evolution of production and logistics systems requires seamless integration of various functions. Cyber-physical systems (CPS) can streamline operations, reduce costs and optimise freight transport dynamics. Therefore, CPS adoption is essential to transform existing logistics infrastructure by connecting logistical equipment to cyberspace. This research addresses the critical issue of low CPS adoption in logistics. By analysing the barriers, the study proposes effective strategies to increase CPS integration significantly.

This study used the technology-organisation-environment (TOE) framework and identified 18 barriers to CPS adoption in freight logistics. The barriers were then analysed using the fuzzy data-driven multi-criteria decision-making (MCDM) method, i.e. fuzzy-based decision-making trial and evaluation laboratory (Fuzzy-DEMATEL).

The computational analysis of the proposed framework shows that “the complexity of CPS and lagged data-driven logistics infrastructure” (T₁) is a major technological inhibitor to CPS adoption. The “high cost of CPS technology” (O₈) and a “lack of top management support” (O₃) are the top causal inhibitors at the organisational level. “Lack of data governance and regulatory framework” (E₂) is the main environmental inhibitor.

The causal mapping of the TOE-based barriers helps policymakers and logisticians to develop strategies or follow recommended adoption strategies to improve CPS adoption in logistics.

This study is a pioneering effort to unveil the adoption barriers of CPS in the freight logistics industry and suggest strategies for fostering adoption. This research could provide valuable insights for logistics organisations to support CPS-driven logistics modernisation.