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This study aims to evaluate the sustainable supply chain performance indicators. At a macro level, the identification of the sustainable supply chain management (SSCM) performance indicators is done through exhaustive literature survey and interviews with experts. Furthermore, these indicators are evaluated through a hybrid approach, i.e. total weighted interpretive structural modelling (TWISM) followed by analytic hierarchical process (AHP).

Micro small and medium enterprises (MSMEs) in India are a major contributor to nation’s GDP. However, this sector struggles to comprehend benefits from implementation of SSCM due to a lack of appropriate performance evaluation metrics. The purpose of this paper is to contribute to the body of knowledge in SSCM by proposing and evaluating a set of SSCM performance indicators.

The paper highlights the SSCM performance indicators and concludes that business strategies, implementation planning and impact of stakeholders are the top SSCM performance indicators (SPIs). Therefore, the decision-makers must initially focus on strategic requirements which foster the implementation of SSCM, thereby ensuring profitability for all stakeholders.

Although the proposed framework was validated through a case study on Indian automobile component manufacturing MSMEs, future research would explore the extension of the framework to other industries.

The originality of this study lies in the application of the novel TWISM-AHP tool. Furthermore, the SPIs identified in the study, consider the integration of the triple bottom line from the MSME perspective. The TWISM-AHP analysis will be beneficial for SC decision-makers to enhance the SSCM performance based on the identified indicators and their criticality.

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.

The purpose of this study is to explore how big data analytics (BDA) as a potential information technology (IT) innovation can facilitate the retail logistics supply chain (SC) from the perspective of outbound logistics operations in the United Kingdom. The authors' goal was to better understand how BDA can be integrated to streamline SCs and logistical networks by using the technology, organisational and environmental model.

The authors applied existing theoretical foundations for theory building based on semi-structured interviews with 15 SC and logistics managers.

The perceived benefits of using BDA in outbound retail logistics comprised the strongest predictor amongst technological, organisational and environmental issues, followed by top management support (TMS). A framework was proposed for the adoption of BDA in retail logistics. Contextual concepts from previous literature have helped us understand how environmental changes impact BDA decision-making, as such: (i) SC maturity levels and connectivity affect BDA utilisation, (ii) connected SCs improve data accessibility and information exchange, (iii) the benefits of BDAs also affect adoption and (iv) outsourcing complex tasks to experts allows companies to focus on core businesses instead of investing in IT infrastructure.

Outside the key findings listed, this study shows that there is no one-size-fits-it-all approach for use within all organisational settings. The proposed framework reveals that the perceived benefit of BDA is non-transferrable and requires top-level management support for successful implementation.

The existing literature focusses on the approaches to applying BDA in SC and logistics but fails to present a deep dive into retail outbound logistics activity. This study addresses the “how” and proposes a social-inclusive framework for a technology-enabled topic.

This study aims to investigate the mediating role of different types of intellectual capital (human, structural and relational) in the relationship between artificial intelligence-driven supply chain analytics capability (AI-SCAC) and various supply chain decision-making processes, specifically rational, bounded and tacit decision-making.

The study used a quantitative survey strategy to collect the data. A total of 320 valid questionnaires were received from manufacturing companies. The data were analysed using structural equation modeling with partial least squares (PLS-SEM) approach through SmartPLS software.

The results indicate that human and structural capital significantly mediate the relationship between AI-SCAC and rational and bounded decision-making processes. However, structural capital does not mediate the relationship between AI-SCAC and the tacit decision-making process. Moreover, relational capital does not show a significant mediating effect on all of the decision-making processes. Notably, structural capital has the strongest impact on rational and bounded decision-making, while human capital plays a critical role across all three decision-making processes, including tacit decision-making.

This study contributes to the literature by providing a nuanced understanding of the differentiated impact of intellectual capital components on various decision-making processes within the context of AI-SCAC. While previous studies have broadly acknowledged the role of intellectual capital in decision-making, this research provides more understanding of how specific types of intellectual capital interact with AI to influence distinct decision-making processes. Notably, the differential impact of structural capital on rational and bounded decision-making versus tacit decision-making highlights the need for organisations to adopt a more tailored approach in leveraging their intellectual capital.

This paper aims to investigate the impact of greenhouse gas (GHG) emissions and their components on the firm performance (FP) of select Indian companies.

The sample is 100 large Indian firms from 2019–20 to 2021–22. Panel data and quantile regression models are employed to examine the issues.

There is a negative relationship between GHG emissions and financial performance. Further, this relationship is heterogeneous at different levels of financial performance. However, environmental certification fails to moderate the relationship.

The study focuses on the top 100 Indian listed companies over three years.

The results highlight the need for management to reduce GHG emissions to improve the financial performance of the firms. Regulators and policymakers may develop guidelines for implementing environmental certification in India.

The study reveals the existence of stringent environmental regulations for limiting GHG emissions.

This study in India explores the moderating impact of environmental certification on the GHG emissions–FP relationship and investigates the influence of GHG emissions at different locations of the distribution of firm performance by using quantile regression.

As artificial intelligence (AI) continues to make inroads into several industries, it has taken over tasks previously performed by humans. However, given that individuals frequently have their self-esteem, identity and feelings of self-worth wrapped up in financial matters, will there be a difference in their satisfaction when their credit applications are processed and approved through AI versus humans?

This work uses five studies, including a field study, three online experiments and one laboratory study, to underline the difference in customer satisfaction when credit application processing occurs via AI versus humans.

The authors find that customers are more satisfied when credit application processing is performed through an AI algorithm rather than by humans. This effect is explained by reduced embarrassment. Furthermore, the authors show that for emotionally intelligent individuals, credit application processing through humans will mitigate the impact of embarrassment, leading to higher customer satisfaction. Finally, the authors identify an individual’s relationship with the financial organisation as the boundary condition stating that for first-time customers (vs continuous customers), credit application processing through humans causes less embarrassment.

This research makes significant contributions in the realm of consumer psychology and credit application processing. First, it advances the existing literature on AI versus human interactions by investigating their comparative impact on customer satisfaction within financial processes such as credit approval. In addition, it identifies credit application processing (whether by AI or humans) as an unexplored antecedent of embarrassment. Moreover, this study enhances the body of work on emotional intelligence by demonstrating its role as a coping mechanism for dealing with embarrassment. Finally, it uncovers a novel driver of embarrassment: the nature of individuals’ relationships with financial organisations, differentiating between continuous customers and first-time applicants.

This study suggests deploying AI for credit approval and adopting strategies to reduce customer embarrassment to boost consumer satisfaction. In addition, managers should consider customers’ emotional intelligence levels and use humans for first-time credit applications to minimise embarrassment.

Arguably, to the best of the authors’ knowledge, this study is the first to identify AI versus human processing as a novel factor influencing customer embarrassment in financial service satisfaction. It also provides a new aspect of emotional intelligence as a coping mechanism for embarrassment. Furthermore, it uncovers a unique driver of embarrassment: the nature of individuals’ relationships with financial organisations, distinguishing between continuous customers and first-time applicants.

In the turbulence of a business-to-business (B2B) environment, employees often face role conflict as the result of changes in task allocations due to work requirements. This research uses organizational role theory to investigate the impact of role conflict on active lurking in B2B online communities. Active lurking, defined as the behavior of seeking information and acquiring knowledge without contributing, contrasts with posters who actively share knowledge. By focusing on these dynamics, this study aims to deepen the understanding of knowledge management (KM) practices and behaviors within B2B contexts.

This research conducts two experimental studies with a behavioral outcome to examine the impact of role conflict on active lurking and the mediating role of knowledge self-efficacy in this regard.

The results also indicate the moderating role of transformational leadership, such that when transformational leadership is low, role conflict decreases knowledge self-efficacy and active lurking. In contrast, this effect is attenuated in a high transformational leadership environment. The findings contribute to the KM literature by demonstrating how role conflict influences knowledge-sharing behaviors in B2B contexts and by capturing active lurking using a behavioral measure.

The implications of this study offer strategies to address role conflict and mitigate its negative impact. By integrating the concepts of posters and lurkers into the research framework, this paper offers a fresh perspective on organizational role conflict and KM. It provides insights into lurkers and active lurking behavior in B2B online communities, thereby extending the KM literature.

The findings of this research are novel as they first show the moderating role of transformational leadership, wherein when transformational leadership is low, role conflict decreases knowledge self-efficacy and active lurking and second it extends the literature on online knowledge sharing in a B2B context by capturing the role of active lurking through a behavioral measure.

The study aims to investigate the association between climate change financial disclosure and financial performance, considering the moderating effect of industry sensitivity on developing nations.

The study analyzes a panel data set of 93 non-financial companies from developing countries listed in the Fortune Global 500 from 2018 to 2022. The authors have used system generalized method of moments model followed by two-stage least square model and fixed effects model to test the hypotheses. Three cultural dimensions and a sub-sample analysis have been included to check the robustness of the results.

The findings indicated that climate change financial disclosure negatively affects financial performance, supporting the propositions of neoclassical theory of corporate social responsibility. Also, climate sensitivity negatively moderates the relationship between climate change disclosure and market performance. The results are robust to alternative estimation techniques, country differences and sectors.

To the best of the authors’ knowledge, this is a novel attempt to examine the impact of climate change disclosure on financial performance in a cross-country context using the task force on climate-related financial disclosure (TCFD) framework. It also contributes to the existing literature by incorporating climate-sensitive sectors as moderating variables. The study recommends a mandatory “framework of law” to protect the environment.

This study analyzes whether industry relatedness between a corporate borrower and its group peers significantly affects that firm's borrowing cost.

A regression analysis is run on bank-loan data of a sample of Indonesian companies for 2010–2020. The main variables of interest are the natural logarithms of the borrowing firm's number of affiliates classified within either similar 2- or 4-digit GICS industries, and the Caves weighted index of these firms' related diversification. This index measures how firms in a group are diversified in relation to the borrower. The dependent variable is the all-in credit spread, stated in basis points, over the LIBOR or similar benchmark, as of the loan issuance date.

Findings support the industry-relatedness hypothesis and contradict the risk-reduction hypothesis and show that banks charge lower loan spreads on a borrowing firm that either operates within a similar industry as its affiliate or diversifies into related sectors or industries. Consistent with the co-insurance-effect hypothesis, the results also underline the importance of the parent and first-layer firms as supporting instead of the tunneling vehicles within business groups. These conclusions hold even after segregating the sample and using the loan maturity as the dependent variable.

This study uses a unique diversification measurement based on the borrowing firm's sector or industry, relative to other group members, and offers new insights on business group diversification and bank loan costs.

This study aims to examine the moderating impact of audit quality (AQ) on the relationship between environmental, social and governance (ESG) disclosure and financial performance (FP) in the Indian context.

The study sample consists of 218 Indian firms, for which Credit Rating Information Services of India Limited has published the ESG scores. Panel data estimation technique is used to examine the direct and moderating impacts. Furthermore, the two-stage least square estimation technique is used for robustness checks.

The study finds a positive impact of ESG score and its components on FP. The findings support the positive “revisionist” hypothesis. The results demonstrate that AQ significantly moderates the relationship between ESG scores and FP. It implies that the impact of ESG disclosure on FP is considerably greater for the Big-4.

The study enriches the existing knowledge by providing empirical evidence on the moderating role of AQ on the ESG disclosure and FP relationship. The findings have several policy implications.