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Employee performance and job satisfaction are crucial factors that influence organizational success, particularly in the insurance industry. The advent of data-driven approaches has led to the emergence of Employee-Performance Data Management (EPDM) practices, which play a pivotal role in shaping employee outcomes. This study, with its clear focus on the impact of EPDM on job satisfaction within the insurance sector, aims to provide an understanding of this relationship, employing a positivist perspective grounded in existing theories.

The primary objective of this research is to investigate the influence of EPDM variables, such as data integration, technology integration, and ethical considerations, on job satisfaction among employees in the insurance industry.

We adopted a causal-comparative research design. This design allowed us to discern the cause-and-effect relationships among the variables under study. We collected data through structured questionnaires, ensuring a diverse sample of 415 employees across various job roles within the insurance sector. Our analytical framework encompassed multiple regression analysis, f-tests, t-tests, and calculations of means and standard deviations, all of which were used to rigorously assess the data.

Our study's findings have significant implications for the insurance industry. We found that aspects of EPDM variables, including data integration, technology integration, and ethical consideration, have a profound impact on job satisfaction. These results underscore the critical role of effective data management in enhancing employee outcomes. They also highlight the need for insurance companies to invest in robust data management strategies, potentially leading to improved job satisfaction and enhanced organizational performance.

This study tries to examine the factors that influence the adoption of e-governance mobile applications among Indian citizens. In addition, this study aims to analyse the impact of these factors on the adoption process.

The study used convenience sampling procedure to collect the data from 431 citizens of India. Confirmatory factor analysis and structural equation modelling techniques were used to assess the validity of scale and test the hypotheses.

The finding reveals that the information quality (IQ), perceived usefulness, social influence and government appeal (GA) significantly and positively impacted the attitude of citizen towards the m-governance. In addition, perceived ease of use was not significantly and positively impacted the citizen towards the m-governance.

This study aims to contribute to the existing literature on m-governance adoption in the developing nation. The study intends to provide insightful information on the factors influencing the adoption of m-governance. Also, this study seeks to make a scholarly contribution and provide practical insights for professionals in the industry and government departments.

Mobile applications transform the government operations and enhance the efficiency of government service delivery. Although there are numerous benefits of m-government application, but still the adoption rate of m-governance is steady. The study uses technology acceptance model along with incorporated two additional constructs, i.e. IQ and GA, to make model more comprehensive and robust to understand the m-governance adoption intention.

Given the rising complexities around organizational resilience, this study identifies and explains the critical enablers of developing organizational resilience (OR). It offers logical reasoning into the interactions and interdependencies among the identified elements with the help of a hierarchical model of the antecedents of OR.

This paper deployed a mixed methodology research design. Firstly, critical enablers of OR are identified from the literature review. Second, contextual relationships and interactions between the enablers are examined using modified total interpretive structural modeling to derive a hierarchical model among the antecedents that characterize OR. Lastly, a survey study including industry experts is used to statistically verify the model links.

Developing resilience lies at the intersection of organization science and strategic management, involving the interaction of factors within an organization’s strategic behaviors, organizational practices, and people processes. The study identifies twelve antecedents of OR. The resultant interpretive hierarchical model helped decipher internal relationships among the antecedents. The proposed model helps determine how organizations move through different phases (before, during, and after) of turbulences and how organizational resilience helps overcome negative spirals.

This research is original and refreshing in its attempt to necessitate resilience as a processual characteristic needed to survive, thrive, and transform amidst business tensions. The hierarchical model of antecedents garners a better understanding of how their interactions and interdependencies help organizations enhance their capacity to adapt and build resilience in organizational systems and processes. It answers questions of “what,” “how,” and “why” relevant to theory building in OR.

The main purpose of this study is to identify the determinants that inhibit the adoption or usage of digital payment systems (DPSs) in India.

This study used a qualitative technique, including in-depth semi-structured interviews. Data analysis was conducted using thematic analysis, incorporating both deductive categorisation and inductive coding to identify factors responsible for the non-adoption or discontinuation of DPS use.

The findings are in the form of themes and sub-themes that were generated from the data analysis: digital divide (DD), which includes the digital access divide, digital capability divide and digital innovativeness divide; socio-demographic divide (SD), which includes education, geographical location, gender, age and income; psychological barriers, which include a lack of perceived ease of use, vulnerability to risks, technophobia and a lack of trust; and other barriers, which include a lack of awareness, a cash-dominated society and a lack of interoperability.

The factors identified in this research can be further validated and tested in future studies using quantitative data. This will enable stakeholders to better comprehend the impacts of these factors on DPS adoption or usage.

The study’s practical implications are specifically relevant to the Union Territory (UT) administration of Ladakh, as there is a DD and an SD among different sections of the population of the UT of Ladakh. UT administrations must prioritise efforts to eliminate these divides. The implications for banks and DPS providers are that they should conduct financial literacy training about DPSs in remote rural areas and invest in developing user-friendly and simplified DPS user interfaces to improve relationships with DPS users and their long-term retention.

The findings of this study reveal the three levels of the DD that determine DPS adoption or usage, which have not been discussed together in the literature in the DPS context and that must be addressed to expand DPS adoption, thus providing a more holistic view of the DD in the context of DPS.

Staffing is one of the most influential human resource (HR) activities and is the primary method of hiring and retaining human resources. Among staffing’s several activities, recruitment and selection are one of the most crucial activities. It is possible to rehire former firm employees using the talent management strategy known as “boomerang recruitment”. The boomerang recruitment trend has tremendously grown because many employees who believe they are qualified for the position now wish to return to their old employers. According to data, boomerang employees can be 50% less expensive than conventional ways of hiring. The purpose of this study is to identify the generic critical factors that play a role in the boomerang hiring process based on the literature review. Next, the objective is to determine the relative weight of each of these factors, rank the candidates, and develop a decision-making model for boomerang recruitment.

This paper focuses on the grey-based multicriteria decision-making (MCDM) methodology for recruiting some of the best candidates out of a few who worked for the organization earlier. The grey theory yields adequate findings despite sparse data or significant factor variability. Like MCDM, the grey methods also incorporate experts' opinions for evaluation. Furthermore, sensitivity analysis is also done to show the robustness of the suggested methodology.

Seven (7) recruitment criteria for boomerang employees were identified and validated based on the opinions of industry experts. Using these recruitment criteria, three candidates emerged as the top three and created a pool out of six. In addition, this study finds that Criteria 1 (C1), the employee's past performance, is the most significant predictor among all other criteria in boomerang hiring.

Since the weights and ratings of attributes and alternatives in MCDM methods are primarily based on expert opinion, a significant difference in expert opinions (caused by differences in their knowledge and qualifications) may impact the values of the grey possibility degree. However, enough attention was taken while selecting the experts for this study regarding their expertise and subject experience.

The proposed method provides the groundwork for HR management. Managers confronted with recruiting employees who want to rejoin may use this model. According to experts, each attribute is not only generic but also crucial. In addition, because these factors apply to all sectors, they are industry-neutral.

To the best of the authors’ knowledge, this is the first study to apply a grey-based MCDM methodology to the boomerang recruitment model. This study also uses an example to explain the computational intricacies associated with such methods. The proposed system may be reproduced for boomerang recruiting in any sector because the framework is universal and replicable. Furthermore, the framework is expandable to include new criteria for different work.

The study examines the impact of behavioral biases, such as herd behavior, overconfidence and reactions to ESG News, on Socially Responsible Investing (SRI) decisions in the Indian context. Additionally, it explores gender differences in SRI decisions, thereby deepening the understanding of the factors shaping SRI choices and their implications for sustainable finance and gender-inclusive investment strategies.

The study employs Bayesian linear regression to analyze the impact of behavioral biases on SRI decisions among Indian investors since it accommodates uncertainties and integrates prior knowledge into the analysis. Posterior distributions are determined using the Markov chain Monte Carlo technique, ensuring robust and reliable results.

The presence of behavioral biases presents challenges and opportunities in the financial sector, hindering investors’ SRI engagement but offering valuable opportunities for targeted interventions. Peer advice and hot stocks strongly predict SRI engagement, indicating external influences. Investors reacting to extreme ESG events increasingly integrate sustainability into investment decisions. Gender differences reveal a greater inclination of women towards SRI in India.

The sample size was relatively small and restricted to a specific geographic region, which may limit the generalizability of the findings to other areas. While efforts were made to select a diverse sample, the results may represent something different than the broader population. The research focused solely on individual investors and did not consider the perspectives of institutional investors or other stakeholders in the SRI industry.

The study's practical implications are twofold. First, knowing how behavioral biases, such as herd behavior, overconfidence, and reactions to ESG news, affect SRI decisions can help investors and managers make better and more sustainable investment decisions. To reduce biases and encourage responsible investing, strategies might be created. In addition, the discovery of gender differences in SRI decisions, with women showing a stronger propensity, emphasizes the need for targeted marketing and communication strategies to promote more engagement in sustainable finance. These implications provide valuable insights for investors, managers, and policymakers seeking to advance sustainable investment practices.

The study has important social implications. It offers insights into the factors influencing individuals' SRI decisions, contributing to greater awareness and responsible investment practices. The gender disparities found in the study serve as a reminder of the importance of inclusivity in sustainable finance to promote balanced and equitable participation. Addressing these disparities can empower individuals of both genders to contribute to positive social and environmental change. Overall, the study encourages responsible investing and has a beneficial social impact by working towards a more sustainable and socially conscious financial system.

This study addresses a significant research gap by employing Bayesian linear regression method to examine the impact of behavioral biases on SRI decisions thereby offering more meaningful results compared to conventional frequentist estimation. Furthermore, the integration of behavioral finance with sustainable finance offers novel perspectives, contributing to the understanding of investors, investment managers, and policymakers, therefore, catalyzing responsible capital allocation. The study's exploration of gender dynamics adds a new dimension to the existing research on SRI and behavioral finance.

Tech start-ups (TSs) functioning in different domains have a responsibility of ensuring that domestic knowledge and capabilities are leveraged to minimize dependence on foreign organizations. Despite the growth of the ecosystem, while numerous TSs emerge, very few of them are able to survive, and of those that survive, very few scale up. The aim of this study is to identify the factors influencing the competitiveness of technological start-ups and to study the interrelationship and interdependence of these factors.

Modified total interpretative structural modeling (m-TISM) was employed for the current research. The analysis of what factors have an effect on competitiveness, how they affect it and why they affect it should be explored. The study begins by developing the list of factors through literature search, and further it is validated by expert opinion. A hierarchical model has been developed using m-TISM and MICMAC analysis to analyze the driving and dependency power of factors at each level.

Results show that the competitiveness of TSs is affected by organizational agility and internationalization. Factors present at the bottom level, namely entrepreneurial intensity, act as a strong driver for TSs. Team member commitment, transformational leadership, strategic alliances, knowledge sharing and organizational ambidexterity are middle-level factors.

This study is among the few articles that have explored competitiveness of TSs in the Indian context.

Ethereum-based blockchain technology (EBT) affords members of the Enterprise Ethereum Alliance (EEA) a market advantage in deploying blockchain within their organizations, including cybersecurity and operational benefits, that leads firms to strategically invest in this nascent technology. However, the impact of such strategic investments in EBT has yet to be explored in the context of its relationship to firm value. Therefore, this study explores EBT-specific firm-level characteristics that result in a stock market reaction to announcements of strategic investments.

The authors use the event study methodology, strategic investment literature and signaling theory as contextualizing frameworks for their study. Additionally, the authors explore a new method for examining technology investments as a strategic counter to cybersecurity threats.

Firms that signal to the market their strong commitment to their strategic investment by developing an EBT proof of concept see significantly higher market returns. Firms that have had prior cybersecurity incidents are rewarded by the market for strategically investing in EBT, and when firms with large undistributed free cash flows utilize this cash for strategic EBT investment, the market is more likely to reward these firms, indicating the market views EBT investment positively in these circumstances.

The results of this study provide new evidence of the value impact of EBT for firms that suffered cybersecurity events in the past. The authors provide empirical evidence of firm-level characteristics that investors use to discern whether a strategic investment in EBT will drive organizational value. Likewise, the authors demonstrate how signaling affects investor perceptions of strategic information technology (IT) investments in EBT.

This paper aims to provide empirical evidence on adopting artificial intelligence (AI), including generative AI, in knowledge management (KM) processes and its impact on organisational decision-making. Specifically, the study addresses three key research questions: RQ1: How is (generative) AI adopted within KM processes in organisations? RQ2: What factors influence the adoption of AI in these processes, either facilitating or inhibiting it? RQ3: How does AI adoption in KM processes affect organisational decision-making?

An explorative investigation has been conducted through semi-structured interviews with KM and AI experts from a worldwide sample of 52 mostly private, large and for-profit organisations. Interviews have been analysed through a mixed thematic analysis.

The study provides an original framework in which the three investigated concepts are interconnected according to a dual relationship: linear and retroactive and 20 factors affecting AI adoption within KM processes.

The provided model guides managers in improving their organisational decision-making through AI adoption in KM processes. Moreover, according to the rational decision-making model, the authors propose a six-step systematic procedure for managers.

To the best of the authors’ knowledge, this is the first study that simultaneously addresses AI, KM and decision-making and provides an integrated framework showing the relationships between them, allowing organisations to better and practically understand how to ameliorate their decision-making through AI adoption in KM processes.

Based on the job-demands resources (JD-R) model and the self-determination theory (SDT), this paper aims to explore team empowerment (TEMP) as a mediating mechanism through which team climate (TC) marked by innovativeness, cohesion and trust and knowledge management (KM) in teams.

Using a convenience sampling method, data were gathered from 246 employees of Tunisian knowledge-intensive firms (KIFs) and involved within 69 R&D teams. The partial least square-structural equation modeling approach through SMART PLS 3.2 software was used to evaluate the constructs’ psychometric properties and hypotheses. The mediating effect in the model was evaluated through the non-parametric bootstrapping method.

Results highlight that TC marked by innovativeness, cohesion and trust represents a key team contextual antecedent promoting TEMP and KM in teams. In turn, TEMP, as a critical intrinsic task motivation factor, is revealed as a driver of KM practices. This research demonstrates that TEMP partially mediates the relationship between TC and KM in teams.

This study pioneers the examination of TEMP’s mediating role between a TC marked by innovativeness, trust and cohesion and KM. By applying insights from the JD-R model and SDT to team-level dynamics, it uniquely positions TEMP as an intrinsic motivational factor explaining the mechanism through which the contextual resources provided by a supportive TC promote KM practices. It provides practical insights for KIFs’ managers through highlighting how intrinsically motivated teams of knowledge workers, empowered by a cohesive, innovative and trust-based TC, can effectively navigate the challenges inherent in knowledge-intensive teamwork, leading to enhanced KM practices.