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This study aims to identify several perspectives that affect the adoption of blockchain technology in India (BCTA) and evaluate their impact. To study the sector’s influence on adoption and the impact of BCTA on the performance of the Indian healthcare supply chain (HSCP) using BCTA as a mediating variable.

In this study, we first developed a conceptual model based on Organizational Information Processing Theory and Technology-Organization-Environment, then formulated hypotheses. Based on this, a questionnaire was developed, and data were gathered from experts in the Indian healthcare industry who were familiar with blockchain technology. AMOS 19 was used to analyze data using structural equation modelling.

All the factors have a significant positive influence on BCTA. Healthcare supply chain factors influenced the adoption most dominantly, followed by technological, environmental, organizational and record-keeping unit factors. Both the public and private sectors of HSCP benefited significantly from BCTA.

This research work is fruitful for healthcare practitioners, top management, academicians and policymakers in assessing BCTA’s impact on the HSCP.

We have attempted to evaluate the possible BCTA impact on HSCP. BCTA as a mediating variable and considering different perspectives for a holistic view of adoption in the Indian context add to this work’s originality.

This study investigates the impact of pre-IPO earnings management on investor demand in the Indian IPO market. It also examines whether earnings management by issuer firms affects IPO valuation, a topic that is underexplored in accounting research.

The study uses the data of 310 IPOs from India during the period 2000–2021. The association between pre-IPO earnings management with investor demand and valuation is tested using cross-sectional ordinary least squares regression models with heteroscedasticity-robust standard errors.

The study finds that the degree of pre-IPO earnings management impacts retail investor demand, measured as their over-subscription multiple. Pre-IPO earnings management is unrelated to institutional investor bidding. Further, this paper suggests no relation between pre-IPO earnings management and IPO valuation.

Future studies could explore various other forms of earnings management and their impact on investor demand and valuation.

The findings of this study will help the investors and regulators to understand the practice of earnings management among IPO firms and how it is related to IPO demand and valuation.

This study contributes to the existing literature on IPO-earnings management and investor demand by documenting that issuer firms engage in earnings management to influence investor demand, particularly retail investor demand. Analysis of IPO valuation reveals that earnings management is mostly unrelated to IPO valuation, contrary to the general perception in the literature.

This study aims at assessing entrepreneurial business performance (EBP) from dynamic capability (DC) and technology-organization-environment (TOE) framework perspectives, taking support from crowdfunding.

With the inputs from the literature, supported by TOE framework and the dynamic capability view (DCV), a model has been proposed. This model has been tested by the factor-based partial least squares structural equation modeling (PLS-SEM) technique through a survey and quantifying the responses of 406 respondents on a five-point Likert scale has been used.

The study has found that crowdfunding support (CFS) has an effective influence on the improvement of EBP. Also, the DC supports to improve the EBP. Environmental dynamism (END) has also a critical role in impacting business performance.

Crowdfunding involves investors who have a similar interest in the business, close friends, family members, venture capitalists, investment groups, etc. Thus, the proposed model can be used by these stakeholders for investment purposes as well as for improving EBP. This study is a cross sectional research work which has limitations. Moreover, the sample size of this project is limited and did not include global respondents, Thus, the findings of this study cannot be generalizable which is another limitation of this study.

Crowdfunding involves investors who have a similar interest in the business, such as close friends, family members, venture capitalists, investment groups, etc. Thus, the proposed model can be used by these stakeholders for investment purposes as well as for improving EBP. The study can help policymakers understand the importance of crowdfunding in promoting entrepreneurship activities in a region, which helps in the economic development of that region.

This research work enriches the extant literature in the fields of crowdfunding and investment, DC and entrepreneurship. Not many studies have dealt with the issues of CFS for the improvement of EBP. Hence, this study may be considered novel. Moreover, the proposed research framework related to crowdfunding possesses a high predictive power. This makes the study unique.

This paper aims to explore the underlying intention behind using blockchain technology (BLCT) in the agri-food supply chain (AFSC). This is achieved by using a conceptual framework based on technology acceptance models that considers various factors influencing user behavior toward implementing this technology in their practices.

The conceptual framework developed is empirically validated using structural equation modeling (SEM). A total of 258 respondents from agri-food domain in India were involved in this survey, and their responses were analyzed through SEM to validate our conceptual framework.

The findings state that food safety and security, traceability, transparency and cost highly influence the intention to use BLCT. Decision-makers of the AFSCs are more inclined to embrace BLCT if they perceive the usefulness of the technology as valuable and believe it will enhance their productivity.

This study contributes to the existing literature by providing thorough examination of the variables that influence the intention to adopt BLCT within the AFSC. The insights aim to benefit industry decision-makers, supply chain practitioners and policymakers in their decision-making processes regarding BLCT adoption in the AFSC.

This study investigates how decision-makers’ perceptions of BLCT influence their intention to use it in AFSCs, as well as the impact of the different underlying factors deemed valuable in the adoption process of this technology.

The importance of workplace inclusion continues to gain scholarly acclaim. However, in reality, many employees choose to ostracize their colleagues post workplace relationship strife. With this notion the present study aims to delve into the intricate linkages between workplace relationship conflict (WRC) and employee ostracism behavior (EOB), exploring the serial mediating roles of relational identification (RI) and emotional energy (EE). Additionally, the study examines the potential moderating effect of perceived forgiveness climate (PFC) to understand how forgiveness climate may serve as a boundary condition in shaping these crucial relationships.

The analysis utilized five-wave time-tagged data collected from 228 employees through scenario and survey methods. The Hayes PROCESS Macro was employed to examine the proposed hypotheses.

The results indicate a positive influence of WRC on EOB. Additionally, RI and EE sequentially mediate the relationship between WRC and EOB. Furthermore, PFC moderates the serial mediation process (RI and EE) between WRC and EOB as well as the adverse effects of WRC on RI.

Grounded in the theoretical framework of conservation of resource (COR) theory and cognitive-affective personality system (CAPS) theory, the present study offers new insights. By establishing the complicated interplay of RI and EE between WRC and EOB along with the moderating role of PFC, the study extends the understanding of the mechanisms involved, providing a more comprehensive perspective. By shedding light on these complicated interconnected links, the study paved the way for positive social dynamics at work.

This paper provides new evidence on Indian tourism firms by investigating the role of a firm's financial conditions typified by its leverage, earnings, size, cash holdings, and excess cash in moderating the pandemic-led idiosyncratic volatility in its stock prices.

The authors employ a firm-level panel comprising 82 publicly-listed tourism firms from India. Firm risk is estimated for the period beginning January 2020 to December 2020.

This paper finds non-linear effects of the pandemic on the idiosyncratic risk of the sample firms. Precisely, stock price volatility rises, but as the market absorbs this information, volatility subsides even as the disease spreads further. Further, lower levels of past debt and earnings and higher cash holdings ameliorate the pandemic's effects on tourism firms' risk. Contrasting the view that “excess” cash reflects poor operational performance, we show that “excess” cash firms are better prepared to face the adverse effects of the pandemic.

This study’s sample period fully encompasses the first wave of the pandemic (January–December 2020) of the novel coronavirus infection spread.

To the best of the authors’ knowledge, this is the first study to assess the moderating effects of company fundamentals on the risk of Indian tourism firms. In doing so, the authors account for non-linear effects of the pandemic on firms' idiosyncratic volatility over time.

In recent years, there has been a growing emphasis on competency-based systems as a means of assessing employee performance. These systems assess the degree to which the competencies of employees align with the requirements of their employment positions. This study aims to identify, prioritize, and make contextual interrelationships of the competency dimensions that are relevant for evaluating employees in the context of Indian manufacturing MSMEs.

These dimensions were identified through an extensive literature review and interviews with industry experts. Further, a mixed-methods approach, including the “Bayesian Best-Worst Method” (BBWM), is applied for prioritizing important dimensions, whereas for making mutual relationships, the “Interpretive Structural Modeling” (ISM) method is utilized. “Matrice d'impacts croisés multiplication appliquée á un classment” (MICMAC) is also known as “cross-impact matrix multiplication applied to classification” is used for clustering competency dimensions based on their “driving power” and “dependence power”.

The findings reveal that among the primary dimensions, “creative performance,” and among the sub-dimensions, “innovative behaviors,” are the most critical competency dimensions for an employee assessment. The study also found that “smart working”, “factual and theoretical knowledge”, “empathy at work”, “understanding of specific knowledge”, and “engagement ideas and activities” are the main dimensions driving employees' competency.

This paper provides contribution to the competence literature by identifying and evaluating competency dimensions for assessing employees' performance within manufacturing MSMEs in an emerging economy such as India. The study also assesses the rank and contextual relationship between the identified dimensions as no past research focused on the same by using BBWM and ISM in the Indian manufacturing MSMEs context.

The study aims to synthesize existing knowledge and proposes a research framework for building a resilient supply chain (SC) through artificial intelligence (AI) technology. It also identifies existing literature gaps and paves the way for a future research agenda.

A systematic literature review has been carried out to identify the peer-reviewed articles from Scopus and Web of Science databases. Then, the selected articles published between 2012 and 2023 are analyzed using descriptive and thematic analysis methods to unearth research gaps and offer new research directions.

Descriptive and thematic analysis reveals the overall development of literature on the role of AI for supply chain resilience (SCR). Based on the findings of the thematic analysis, the motivation, application, capability and outcome (MACO) framework has been developed and propositions have been proposed. Several future research directions have also been suggested in terms of theory, context and methodology (TCM).

The study provides a fresh perspective on the integration of AI technology within the realm of SCR. The developed MACO framework serves as a practical tool for supply chain management (SCM) professionals, offering a nuanced understanding of AI's applications across various functional areas to streamline operations, minimize waste and optimize resource utilization, thereby helping them in strategic planning.

This study contributes to the literature on the role of AI for building SCR by uncovering gaps, offering research directions and developing propositions for future research directions.

The authors examine whether internal corporate governance mechanisms are effective in curbing cash flow manipulation through real activities, misclassification, and timing.

The sample comprises of firms from an emerging market, India with data for years 2004 through 2015. The authors use the methodology given in Roychowdhury (2006).

The authors find that corporate boards in India play an active role in curbing cash flow manipulation through real activities but fail to control cash flow manipulation through misclassification and timing.

The study suggests that corporate boards should pay more attention to the reported cash flow numbers. Regulators can reduce the opportunities available for cash flow misclassification by fixing relevant accounting and governance norms. Auditors can also help by critically focusing on the cash flow classifications presented by management.

This study, to the authors’ knowledge, is the first study that talks about the role of internal governance in a trade-off between different cash flow manipulation techniques.

Additive Manufacturing technology (AMT) is swiftly gaining prominence to induce automation and innovation in manufacturing systems. It holds immense potential to change supply chain dynamics by providing the possibility of printing objects on demand. This study thus formulates and analyzes the framework to incorporate AMT to handle the spare parts supply chain management (SPSCM) in capital-intensive industries by identifying and assessing the critical success factors (CSFs).

Assessment of the CSFs is performed using the novel Grey Causal Modeling method (GCM) with the objective of making SPSCM resilient and efficient. GCM conducts causal analysis by taking into consideration cause, effects, the objectives, and the situations.

Findings indicate that; Logistics Lead Time (SD4), Time to manufacture (SD3), Management Support (SD11), and Risk Management (SD20) are the most prominent causal factor having a maximum impact when incorporating AMT in SPSCM. The results also reveal that the performance of manufacturing organizations that adopt AMT is substantially influenced by internal and external factors such as Management Support (SD11) and Government Regulations (SD16).

This research provides valuable information for getting the global spare parts supply chain equipped for the post-COVID age, where digital technologies such as AMT will be fundamental for bolstering supply chain resilience and efficiency.

This research proposes a framework for performance assessment when incorporating AMT in SPSCM. Study also demonstrates methodological application of novel Grey Causal Modelling technique using a real case in a spare parts manufacturing industry in India.