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The purpose of this paper spans twofold. Firstly, to investigate Human Resource Management practices (HRMP) adopted by organisations during the pandemic. Secondly, to bundle similar HRMP into Human Resource Management (HRM) bundles that provided unhindered organisational support to employees during the crisis.

The authors conducted 39 in-depth interviews across industries using a semi-structured interview schedule. Thereafter, the authors transcribed the interviews verbatim and analysed them thematically using MAXQDA 2021.

The study identifies effective practices during times of uncertainty and how soft HRM practices helped organisations survive during a crisis. When bundled together, these practices enabled organisations to continue operations during the pandemic, keeping their employees engaged and motivated.

Based on the learnings from the COVID-19 pandemic, the study provides a toolkit of HRMP bundles that organisations can adopt for future crisis management, enhancing the organisations’ absorptive capacity.

The study investigates the practices incorporated during COVID-19, leading to the identification of soft HRM bundles. The study adds value to the existing domain of HRM by including a unique set of soft HRMP bundles that have not been discussed in earlier studies and could be of high utility to organisations during the crisis.

The purpose of this paper is to examine how managers orchestrate, bundle and leverage resources from big data for value creation in emerging economies.

The authors grounded the theoretical framework in two perspectives: the resource management and entrepreneurial orientation (EO). The study utilizes an inductive, multiple-case research design to understand the process of creating value from big data.

The findings suggest that EO is vital through which companies based in emerging economies can create value through big data by bundling and orchestrating resources thus improving performance.

This is one of the first studies to have integrated resource orchestration theory and EO in the context of big data and explicate the utility of such theoretical integration in understanding the value creation strategies through big data in the context of emerging economies.

The purpose of this paper is to explain cloud service transformation in small and medium enterprises (SMEs) based on technology-environment-organization paradigm and understand the role of cloud service trust in transformation process.

A survey involving 107 SMEs was conducted to examine the research model and hypotheses.

First, cloud service trust is found to have a significant positive effect on the SMEs’ cloud service transformation intention. The second finding is about significant influences of technological drivers (reliability and information security), environmental drivers (institutional pressure, structure assurance, and vendor scarcity) and entrepreneurship on SMEs’ cloud service trust. Further, the authors found mediating effects of trust on relationships between external drivers and cloud service transformation.

For vendors, it suggests building cloud service trust by distinguishing advantages of their cloud service and by establishing strategic alliances with existing users in marketing to attract potential clients. Vendors should target entrepreneurial organizations as initial customers and then expand to other types of organizations. For users, the study implies the need of cultivating entrepreneurship, if they have innovative IT initiatives and need to speed up the IT innovation absorption. Market regulators can provide adequate structural assurances and survival-of-the-fittest market mechanism to stimulate cloud service market.

This study is on the leading edge of systematically investigating drivers for SMEs’ cloud service transformation and further reveals a mediating process, in which technological and environmental aspects have primary effects on cloud service trust that sequentially influences cloud service transformation. These mediating effects imply an essential trust building process of cloud service transformation.

This study aims to investigate the adoption intention of artificial intelligence (AI) in family businesses through the perspectives of digital entrepreneurship and entrepreneurship orientation.

The study examines contributing factors explaining the adoption intention of AI in the context of family businesses. The developed research model is examined and validated using structural equation modelling based on 631 respondents' data. Purposeful sampling is used to collect the respondents' data.

The proposed model included two endogenous (i.e. business innovativeness and adoption intention) and six exogenous variables (i.e. affordances, culture and flexible design, entrepreneurial orientation, generativity, openness and technology orientation) through ten direct paths and three indirect paths. The results depicted the significant influence of all the exogenous variables on the endogenous variable reflecting support of all the hypotheses. The business innovativeness partially mediates the relationships of culture and flexible design, entrepreneurial orientation and technology orientation with adoption intention. Further, the results demonstrated a model variance of 24.6% for business innovativeness and 64.2% for adoption intention of artificial intelligence in the family business.

The study contributes to theoretical developments in entrepreneurship and family business research and AI's theoretical progress, especially to digital entrepreneurship.

Theoretically, it contributes to the literature of entrepreneurship, particularly digital entrepreneurship. Additionally, the research model adds to the role of entrepreneurial orientation and digital entrepreneurship in the emerging family entrepreneurship literature. Considering the scarcity of research in this field, the empirically validated model explaining critical antecedents of AI adoption intention in the family business is a foundation for discussion, critique and future research.

This study proposes a methodology based on data source triangulation to measure the “strategic fit” for the automotive supply chain.

At first, the authors measured the responsiveness of the Indian automobile supply chain, encompassing the top ten major automobile manufacturers, using both sentiment and conjoint analysis. Second, the authors used data envelopment analysis to identify the frontiers of their supply chain. The authors also measured the supply chain's efficiency, using the balance sheet. Further, the authors analyzed the “strategic fit” zone and discussed the results.

The results indicate that both the proposed methods yield similar outcomes in terms of strategic fitment.

The study outcomes facilitate measuring the strategic fit, thereby leveraging the resources available to align. The methodology proposed is both easy to use and practice. The methodology eases time and costs by eliminating hiring agencies to appraise the strategic fit. This valuable method to measure strategic fit can be considered feedback for strategic actions. This methodology could also be incorporated possibly as an operative measurement and control tool.

Data triangulation meaningfully enhances the accuracy and reliability of the analyses of strategic fit. Data triangulation leads to actionable insights relevant to top managers and strategic positioning of top managers within a supply chain.