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Digital technology is crucial to improving a firm’s core competitiveness. However, the existing research on the relationship therein shows heterogeneity. Using digital technology can enhance competitive advantage, which is crucial for enterprises and scholars. Thus, based on the digital technology affordance theory, this study explores the relationship between digital technology affordance and digital competitive advantage.

Survey data were collected from 509 large and medium-sized manufacturing enterprises in China, and multiple regression and structural equation modelling were used to test the hypotheses. Specifically, we discuss the mediating role of digital business capability and the moderating role of organisational legitimacy.

Editability, association and visibility positively affect digital competitive advantage, and their coordination is strong. Further, they can help enterprises gain a competitive advantage through the mediating role of digital business capability (digital strategy, digital integration and regulation). However, the influence effect and action path differ per in different dimensions. Organisational legitimacy positively moderates the mediating effect of digital integration and regulation, and there is a moderated mediating effect. However, the moderating effect on the mediating effect of digital strategy is not significant.

Existing studies neglect the relationship between the coordination of digital technology functions and digital competitive advantage. This study provides a new theoretical explanation for an in-depth understanding of these issues. These findings promote the development of innovation theory and provide valuable insights for guiding the application of digital technology in enterprises.

This study aims to investigate the association of FinTech innovation (FinTechINN) and firm performance (FP) by considering the role of knowledge assets (KA) as a causal mechanism underlying the FinTechINN – FP association.

In this study, the authors consider panel data of 1,049 Chinese A-listed firm and construct a structural model for corporate FinTech innovation, knowledge assets and firm performance while considering endogeneity issues in analyses over the period of 2014–2022. The modified value added intellectual capital (VAIC) and research and development (R&D) expenses are used as a proxy measure for knowledge assets, considering governance and corporate performance measures.

According to the findings of this study FinTech innovation (FinTechINN) has a positive significant effect on firm performance. Particularly; the findings disclose that FinTech innovations has a link with knowledge assets, FinTech innovations indirectly affects firm performance, and the association between FinTech innovation and firm performance is partially mediated by knowledge assets (MVAIC and R&D expenses).

Rooted in the dynamic capability and resource-based view, this study pioneers an empirical exploration of the association of FinTech innovation with firm performance. Moreover, it introduces the novel dimension of knowledge assets (on firm-level), acting as a mediating factor with in this relationship.

Promoting electric vehicles (EVs) is an effective way to achieve carbon neutrality. If EVs are widely adopted, this will undoubtedly be good for the environment. The purpose of this study is to analyze the impact of network externalities and subsidy on the strategies of manufacturer under a carbon neutrality constraint.

In this paper, the authors propose a game-theoretic framework in an EVs supply chain consisting of a government, a manufacturer and a group of consumers. The authors examine two subsidy options and explain the choice of optimal strategies for government and manufacturer.

First, the authors find that the both network externalities of charging stations and government subsidy can promote the EV market. Second, under a relaxed carbon neutrality constraint, even if the government’s purchase subsidy investment is larger than the carbon emission reduction technology subsidy investment, the purchase subsidy policy is still optimal. Third, under a strict carbon neutrality constraint, when the cost coefficient of carbon emission reduction and the effectiveness of carbon emission reduction technology are larger, social welfare will instead decrease with the increase of the effectiveness of emission reduction technology and then, the manufacturer’s investment in carbon emission reduction technology is lower. In the extended model, the authors find the effectiveness of carbon emission reduction technology can also promote the EV market and social welfare (or consumer surplus) is the same whatever the subsidy strategy.

The network externalities of charging stations and the subsidy effect of the government have a superimposition effect on the promotion of EVs. When the network effect of charging stations is relatively strong, government can withdraw from the subsidized market. When the network effect of charging stations is relatively weak, government can intervene appropriately.

Comparing previous studies, this study reveals the impact of government intervention, network effects and carbon neutrality constraints on the EV supply chain. From a sustainability perspective, these insights are compelling for both EV manufacturers and policymakers.

This study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for precise information categorization and decision support across various management departments.

This study leverages the ALBERT–TextCNN algorithm to determine the appropriate department for managing online appeals. ALBERT is selected for its advanced dynamic word representation capabilities, rooted in a multi-layer bidirectional transformer architecture and enriched text vector representation. TextCNN is integrated to facilitate the development of multi-label classification models.

Comparative experiments demonstrate the effectiveness of the proposed approach and its significant superiority over traditional classification methods in terms of accuracy.

The original contribution of this study lies in its utilization of the ALBERT–TextCNN algorithm for the classification of online appeals, resulting in a substantial improvement in accuracy. This research offers valuable insights for management departments, enabling enhanced understanding of public appeals and fostering more scientifically grounded and effective decision-making processes.

This paper aims to synthesize and critically explore the available knowledge about the linkages between compulsory citizenship behavior, knowledge hiding and employee innovative performance through a systematic literature review. It proposes a conceptual framework, highlighting the core relationships between these constructs.

Using two databases (Dimensions and Scopus), the authors critically examine 93 peer-reviewed publications from 2006 to 2023 to identify the arguments supporting the associations between the studied constructs.

Through an integrative conceptual model, this study provides insights into the devastating impact of compulsory citizenship behavior on employee innovation performance, further suggesting how knowledge hiding may play a mediating role.

The study's main limitation lies in its conceptual nature. Future research should empirically validate the model to gain richer perspectives into the linkages.

The existing body of literature lacks a comprehensive understanding of the nexus between compulsory citizenship behavior, knowledge hiding and employee innovative performance. This study is a pioneer since it explores the emerging concepts of compulsory citizenship behavior and knowledge hiding and accentuates their presence in the context of innovative performance at the individual level through coercive persuasion theory and cognitive dissonance theory.

This study investigates the influence of the wisdom of the crowd (WSC), trust and perceived value on consumer purchase intentions within social commerce (s-commerce) platforms. By constructing a novel theoretical model, the research aims to delineate the interrelationships among these variables, addressing an emerging area in social interactions and information technology.

To empirically test and validate the proposed model, the study collected data from 442 Facebook users familiar with online shopping. The analysis employed Structural Equation Modeling – Partial Least Squares (SEM-PLS) to test the hypotheses and examine the relationships between WSC, trust, perceived value and purchase intentions.

The findings confirm that WSC significantly influences trust, perceived value and the intention to purchase on s-commerce platforms. Both perceived value and trust are substantial determinants of purchase intentions. Notably, the PLS analysis revealed that WSC accounts for 36.8% of the variance in trust and 33.1% of the variance in perceived value related to purchasing decisions on s-commerce platforms.

This research contributes uniquely to the ongoing discourse on s-commerce adoption by integrating WSC as a pivotal factor in understanding perceived value and consumer adoption intentions. It provides a fresh perspective on how collective intelligence affects consumer behavior in digital marketplaces, enriching the theoretical and practical understanding of s-commerce dynamics.

We empirically investigate the impact of fintech innovation on dividend payout (DP) decisions. In addition, we also examine the mediated and moderated role of intellectual capital (IC) and board characteristics (BC) respectively in the fintech innovation-DP relationship.

Using a sample of 9,441 firm-year observations over the period 2014–2022, we develop a structural model that encompasses fintech innovation, IC, BC and DP decisions. We utilize fixed effects regression to empirically test the model. A battery of tests such as the two-step Generalized Method of Moment, Heckman’s two-stage selection correction and Difference-in-Difference regression are used to check the robustness and sensitivity of the estimates.

Our results suggest that fintech innovation significantly and positively impacts DP decisions and IC partially mediates the fintech innovation–DP relationship. In addition, BC such as independence, age and gender diversity are found to moderate this relationship.

This study’s originality lies in its micro-level analysis of the impact of fintech innovation on DP decisions, considering a novel firm-level innovation metric derived from patent applications. To our knowledge, no previous work has empirically examined the mediating role of IC and the moderating influence of BC in the fintech innovation–DP relationship, offering a unique perspective on the complex interactions shaping dividend policies in the digital era.

It is widely acknowledged that the ability of a firm to develop and exploit their innovative capabilities is a critical determinant that maintains their competitive advantage. The purpose is to evaluate the research and development (R&D) inputs and outcomes on the performance of firms in different stages.

Drawing on a sample of 30 firms over 8 years (2009–2016), the results from a three-stage Bayesian stochastic frontier analysis model support were used.

Some interesting findings were discovered. First, the R&D intensity is positively associated with the number of patents granted, which is negatively associated with the number of new drug approvals (NDAs). Second, R&D inputs, including expenditures and human resources, are negatively related to the number of NDAs and firm performance. Third, state-owned firms perform better and have more patents granted than private-owned firms in China. Finally, the traditional Chinese medicine firms and non-coastal firms both gain fewer profits, but they generate more new drugs than chemical drug firms and coastal firms in terms of policy support.

It is revealed that there are no common factors among Chinese pharmaceutical firms except for ownership, and this heterogeneous behavior indicates that there is no common factor for enhancing the efficiency of all Chinese pharmaceutical firms.

This study aims to investigate the antecedents of social interaction among Indian retail investors and fund managers to understand how these factors influence investment decisions. By identifying and examining these antecedents, the study aims to shed light on the social dynamics that shape investment behavior in the Indian financial market.

The researchers have mainly adopted an interpretive strategy for the present study. Qualitative data elicited through semistructured interviews with six retail investors and two fund managers were subjected to qualitative thematic analysis.

Our research found several factors that make Indian retail investors and fund managers connect and make financial decisions. Peers can improve a person’s investing performance through social facilitation, and discussing investment suggestions and lessons learned can affect a group’s investment behavior. Social norms also influenced investors’ financial decisions, demonstrating compliance. Investor closeness increased information sharing. Finally, the fear of missing out (FOMO), a psychological phenomenon where people fear missing out on rewarding experiences, encouraged social engagement as investors sought appealing prospects.

The researchers interviewed eight carefully selected interviewees across the divide between retail investors and fund managers. Adopting other grouping criteria, conducting a focus group discussion with more respondents or adopting a mixed-methods approach may increase our understanding of the investment decision behaviors of Indian retail investors and fund managers.

The findings have far-reaching consequences, from deepening our knowledge of investors’ motivations and actions to directing individual savers, informing the development of financial literacy initiatives, influencing fund management practices and inspiring additional research in this study area.

This research, including retail investors and fund managers, significantly contributes to the literature on investment decisions and behavioral finance, particularly in the context of Indian investors and managers. This study’s unique perspective and comprehensive approach make it a valuable addition to the field, sparking interest and further exploration among academics, practitioners and investors alike.

Success in the transition between the university and the labor market is an important indicator of the adaptation of newly graduates to the worker’s role in life. This study aims to describe the validity and reliability of the University-to-Work Success Scale based on its internal structure and relationship with measures of career success, protean career orientation and life satisfaction in newly Portuguese graduates.

Using an online protocol, responses were collected from 576 graduates for less than twelve months (74.1% women), aged between 20 and 64 years (M = 25.8, SD = 6.693). Instruments included a socio-demographic questionnaire and measures of transition success, career success and life satisfaction.

The internal structure of the scale was evaluated through exploratory and confirmatory factor analyses that supported a four-factor hierarchical structure with a good fit. The reliability of the factors evaluated by Cronbach’s Alpha was satisfactory. The scale consists of 29 items divided into four subscales (professional insertion and satisfaction, confidence in the future of career, income and financial independence and adaptation to work).

These results support the use of the scale as a valid and reliable measure to assess success in the transition between university and the labor market in newly Portuguese graduates.

This study is very important because this measure can serve as a basis for both preventive and corrective career interventions and policies. The preventive approach can help graduates in their transition to the labor market by promoting career resources. The corrective approach can include re-evaluating organizational integration practices after employment, with an emphasis on promoting gender equality.