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This paper aims to study the motives that led to conflict between two groups – Employees of state-owned power distribution companies and the Government – over permitting parallel licensing to a private company for power distribution services in selected areas of the Maharashtra state in India. The study also seeks to comprehend the reconciliation process and the role of leadership in thwarting the strike that could have impacted common citizens.

The research is built on a case-based approach to analyze the pre- and poststrike environment along with the impact of the power shortage during the few hours of the strike. A semi-structured interview method wherein government and employee union representatives were interviewed to understand their version of the incident is used. Related literature, reports and news were reviewed to realize the impacts and consequences of similar situations in the past.

The strike was called off within a few hours with the intervention of state government to resolve the issue, promising the union the government’s intention not to privatize but to invest INR 500bn in the three government companies. The parallel licensing may impact government-owned power distribution companies as well as customers in the future.

It will pave the way for lessons related to such incidence where the Government and the Unions are at loggerheads over issues like privatization or ownership of the company and help the involved and other parties to seek a viable solution. The role of resilient leadership demonstrated by both parties led to a win-win solution within a few hours of the strike.

The paper is a case study on an issue that is very contemporary; the role of leadership and its swiftness in decision-making that led to a solution to a very complex situation is something that was not done earlier in the context of the State vs Union issue.

This study investigates the mediating role of wage and workforce adjustments, along with the moderating influence of collective bargaining system and employees’ localization, in elucidating the relationship between the COVID-19 shock and workplace employee relations (ER) tension.

Survey data from 1,483 enterprises across 21 prefectural cities in China’s Guangdong Province are collected. The hypotheses are tested by logistic regression.

The study reveals a positive correlation between the COVID-19 shock and workplace ER tension across crisis-hit enterprises, irrespective of their size or industrial sector. Wage reduction and mass layoffs emerge as significant mediators, while the collective bargaining system (CBS) and employees’ localization act as moderators.

The measurement of ER is limited in a single-item scale. Representation of China is also limited since the study exclusively focuses on Guangdong province. The study offers some contributions that firm-level data reveal the pathway through which COVID-19 creates ER tension.

On the one hand, the authors recommend the establishment of an effective communication system between employers and employees. On the other hand, managers should consider the role of informal institutions. Furthermore, the authors suggest implementing tailored strategies at the enterprise level.

Intense external shocks result in widespread layoffs and increased wage reductions within workplaces, and under such circumstances, formal or informal institutions may be insufficient to alleviate ER tension. In this case, the state authorities – including governments and other public agencies or bodies – are necessary to intervene in to organize tripartite dialogue.

While numerous emerging studies on COVID-19 explore how different countries manage industrial relations tension at the national level, few focus on ER at workplace level, particularly in developing countries. Understanding how workplace ER evolve during external shocks and identifying institutional measures to mitigate their negative impact is crucial for future crisis management.

The paper tries to investigate the impact of applications of the resource-based view (RBV) theory in the management field to improve the firm’s profitability. Global firms are innovating and adopting new technology, paving the way to improve their performance.

We have adopted RBV in management practices such as marketing, strategy, finance, and human resources.

RBV has gained researchers' attention with the growing competitive world and new challenges to retaining customers and achieving their pre-defined targets. We attempt to identify the issues related to the usage of RBV in management.

Using RBV in management may help researchers create a competitive mindset and be prepared for uncertain challenges in the business world.

This study aims to analyze the effect of cognitive load and social value orientation on managers’ preferences when they face with two types of restructuring choices in financially distressed firms: the first belonging to the family of organizational restructuring (massive layoffs) and the second to the family of financial restructuring (debt increases).

The authors investigate experimentally the impact of managers’ cognitive load and social value orientation on the decision to restructure leveraged buyout (LBO) firms in financial distress by using either massive layoffs or debt increases.

By investigating the impact of managers’ cognitive load and social value orientation on the restructuring decision of an LBO firm in financial distress, the research reveals that, on average, cognitively loaded managers prefer massive layoffs over increased debt levels. The massive layoffs seemingly provide a relatively easier way to avoid conflict with influential, residual claimants. In contrast, social value–oriented managers actively avoid massive layoffs and prefer to increase debt.

These results imply that the performance mechanisms emphasized to improve agency relations, for example, in LBOs, have their own limitations during periods of financial distress. This study shows that one of these limits is related to cognitive distortions and personality traits.

In this research, the originality lies in understanding how managers’ internal factors affect their restructuring decision-making, in the case of LBO firms in financial distress.

The discourse on global value chains (GVC) is undergoing a transformation in terms of its conceptualisation, theorisation and pragmatic applications. Today, the production systems have become more complex as global economic order continues to witness marked geo-economic manoeuvring. Thus, the direction of discourse on GVC ought to move from mere theoretical propositions toward becoming more evidence based. There have been recent studies that have used the governance and upgrading propositions by Gary Gereffi and others to seek quantitative evidence. This study aims to decipher the quantitative discourse on GVC and to set the emerging and future research agenda.

Through a systematic literature review, the authors first analyse the quantitative studies on GVC carried out during the last two decades. The authors then outline a future research agenda and examine a few relevant modelling techniques that could potentially be used to solicit newer evidence in GVC research.

The authors categorise the quantitative discourse on GVC into three crucial themes, namely, GVC framework, GVC participation and position, environmental aspects and regionalisation in GVC. The most commonly used quantitative techniques are gravity model, panel data estimation, structural decomposition analysis and computable general equilibrium modelling.

This paper contributes to the GVC discourse in two ways. Firstly, the authors argue that the theoretical frameworks within the GVC discourse should be complemented by evidence-based quantitative studies. Secondly, the authors suggest potential modelling techniques that can be used on the emerging and future research agenda.

The paper explores the notion of worker voice in terms of its implications for supply chain justice. The paper proposes the value of the recognition perspective on social justice for framing workers’ experiences in global supply chains and identifies opportunities for the advancement of the worker voice agenda with recognition justice in mind.

The paper adopts a conceptual approach to explore the notion of worker voice in supply chains in terms of the recognition perspective on social justice.

Sustainable supply chain management (SSCM) scholarship has considered worker voice in terms of two key paradigms, which we term communication and representation. To address recognition justice for workers in global supply chains, the worker voice agenda must consider designing worker voice mechanisms to close recognition gaps for workers with marginalised identities; the shared responsibilities of supply chain actors to listen alongside the expectation of workers to use their voice; and the expansion of the concept of worker voice to cut across home-work boundaries.

The paper offers conceptual clarity on the emerging notion of worker voice in SSCM and is the first to interrogate the implications of recognition justice for the emergent worker voice agenda. It articulates key opportunities for future research to further operationalise worker voice upon a recognition foundation.

Digital labor platforms (DLPs) are transforming the nature of the work for an increasing number of workers, especially through extensively employing automated algorithms for performing managerial functions. In this novel working setting – characterized by algorithmic governance, and automatic matching, rewarding and punishing mechanisms – gig-workers play an essential role in providing on-demand services for final customers. Since gig-workers’ continued participation is crucial for sustainable service delivery in platform contexts, this study aims to identify and examine the antecedents of their working outcomes, including burnout and engagement.

We suggested a theoretical framework, grounded in the job demands-resources heuristic model to investigate how the interplay of job demands and resources, resulting from working in DLPs, explains gig-workers’ engagement and burnout. We further empirically tested the proposed model to understand how DLPs' working conditions, in particular their algorithmic management, impact gig-working outcomes.

Our findings indicate that job resources – algorithmic compensation, work autonomy and information sharing– have significant positive effects on gig-workers’ engagement. Furthermore, our results demonstrate that job insecurity, unsupportive algorithmic interaction (UAI) and algorithmic injustice significantly contribute to gig-workers’ burnout. Notably, we found that job resources substantially, but differently, moderate the relationship between job demands and gig-workers’ burnout.

This study contributes a theoretically accurate and empirically grounded understanding of two clusters of conditions – job demands and resources– as a result of algorithmic management practice in DLPs. We developed nuanced insights into how such conditions are evaluated by gig-workers and shape their engagement or burnout in DLP emerging work settings. We further uncovered that in gig-working context, resources do not similarly buffer against the negative effects of job demands.

The purpose of this study is to identify and analyze the barriers associated with the adoption of Industry 4.0 technologies in agricultural supply chains.

The study initially identified thirteen barriers by conducting a literature review and semi-structured interviews with key stakeholders. Subsequently, these barriers were validated and modeled using an integrated Fuzzy Delphi-ISM approach. Finally, MICMAC analysis was employed to categorize the barriers into distinct clusters.

The results provide considerable insights into the hierarchical structure and complex interrelationships between the barriers as well the driving and dependence power of barriers. Lack of information about technologies and lack of compatibility with traditional methods emerged as the two main barriers which directly and indirectly influence the other ones.

The robust hybrid Fuzzy Delphi and ISM techniques used in this study can serve as a useful model and benchmark for similar studies probing the barriers to Industry 4.0 adoption. From a theoretical standpoint, this study expands the scope of institutional theory in explaining Industry 4.0 adoption barriers.

The study is timely for the post-COVID-19 recovery and growth of the agricultural sector. The findings are helpful for policymakers and agriculture supply chain stakeholders in devising new strategies and policy interventions to prioritize and address Industry 4.0 adoption barriers.

It is the first comprehensive, multi-country and multi-method empirical study to comprehensively identify and model barriers to Industry 4.0 adoption in agricultural supply chains in emerging economies.

Based on signaling theory, this paper aims to explore the impact of supply chain transparency (SCT) on firms' bank loan (BL) and supply chain financing (SCF) in the context of voluntary disclosure of supplier and customer lists.

Based on panel data collected from Chinese-listed firms between 2012 and 2021, fixed-effect models and a series of robustness checks are used to test the predictions.

First, improving SCT by disclosing major suppliers and customers promotes BL but inhibits SCF. Specifically, customer transparency (CT) is more influential in SCF than supplier transparency (ST). Second, supplier concentration (SC) weakens SCT’s positive impact on BL while reducing its negative impact on SCF. Third, customer concentration (CC) strengthens the positive impact of SCT on BL but intensifies its negative impact on SCF. Last, these findings are basically more pronounced in highly competitive industries.

This study contributes to the SCT literature by investigating the under-explored practice of supply chain list disclosure and revealing its dual impact on firms' access to financing offerings (i.e. BL and SCF) based on signaling theory. Additionally, it expands the understanding of the boundary conditions affecting the relationship between SCT and firm financing, focusing on supply chain concentration. Moreover, it advances signaling theory by exploring how financing providers interpret the SCT signal and enriches the understanding of BL and SCF antecedents from a supply chain perspective.

The purpose of this study is to explore the coopetition relationships between platform owners and complementors in complementary product markets. Drawing on the coopetition theory, the authors examined the evolutionary trends of the coopetition relationships between platform owners and complementors and explore the main influence factors.

The authors used Lotka–Volterra model to analyze the coopetition relationship between platform owners and complementors, including the evolutionary trends as well as the results. Considering the feasibility of sample data collection, simulation is used to verify the effects of different factors on the evolution of coopetition relationships.

The results show that there are four possible results of the competition in the complementary products market. That comprises “winner-take-all for platform owners,” “winner-take-all for complementors,” “stable competitive coexistence” and “unstable competitive coexistence,” where “stable competitive coexistence” is the optimal evolutionary state. Moreover, the results of competitive evolution are determined by innovation subjects’ interaction parameters. However, the natural growth rate, the initial market benefits of the two innovators and the overall benefits of the complementary product markets influence the time to reach a steady state.

The study provides new insights into the entry of platform owners into complementary markets, and the findings highlight the fact that in complementary product markets, platform owners and complementors should seek “competitive coexistence” rather than “winner-takes-all.” Moreover, the authors also enrich the coopetition theory by revealing the core factors that influence the evolution of coopetition relationships, which further enhance the analysis of the evolutionary process of coopetition relationships.