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This study aims to investigate whether the changes in gender composition of bank board affects Vietnamese bank stability efficiency.

This research covers a panel of 27 commercial banks in Vietnam over a 14-year period from 2007 to 2020. The two-step system generalized method of moments is used to estimate the gender diversity–Vietnamese bank stability efficiency nexus.

The authors find that a greater degree of board gender diversification enhances bank stability efficiency and reduces bank risk-taking in Vietnam. The relationship between gender diversity and the stability efficiency of Vietnamese banks is still valid under the influence of regulatory capital sufficiency and during the financial crisis. These findings are robust to alternative proxies for risk indicators and consistent with the perspectives of stakeholder and behavior theory.

Although this research revisits the relationship between gender diversity and bank risk-taking, it is the first attempt to explore the role of women on board in enhancing the stability efficiency of banks, using the stochastic frontier approach. These findings shed light on the function of gender diversity as a governance instrument for mitigating risk in an emerging market context.

This study aims to explore Augmented Language Models (ALMs) for synthetic data generation in services marketing and research. It evaluates ALMs' potential in mirroring human responses and behaviors in service scenarios through comparative analysis with five empirical studies.

The study uses ALM-based agents to conduct a comparative analysis, leveraging SurveyLM (Bickley et al., 2023) to generate synthetic responses to the scenario-based experiment in Söderlund and Oikarinen (2018) and four more recent studies from the Journal of Services Marketing. The main focus was to assess the alignment of ALM responses with original study manipulations and hypotheses.

Overall, our comparative analysis reveals both strengths and limitations of using synthetic agents to mimic human-based participants in services research. Specifically, the model struggled with scenarios requiring high levels of visual context, such as those involving images or physical settings, as in the Dootson et al. (2023) and Srivastava et al. (2022) studies. Conversely, studies like Tariq et al. (2023) showed better alignment, highlighting the model's effectiveness in more textually driven scenarios.

To the best of the authors’ knowledge, this research is among the first to systematically use ALMs in services marketing, providing new methods and insights for using synthetic data in service research. It underscores the challenges and potential of interpreting ALM versus human responses, marking a significant step in exploring AI capabilities in empirical research.

This study investigates the relationship between credit booms and bank risk in Association of Southeast Asian Nations (ASEAN) countries, with credit information sharing acting as a moderator.

The authors use a two-step System Generalized Method of Moments (SGMM) estimator on a sample of 79 listed banks in 5 developing ASEAN countries: Indonesia, Philippines, Malaysia, Thailand and Vietnam in the period 2006–2019. In addition, the authors perform robustness tests with different proxies for credit booms and bank risk. The data are collected on an annual basis.

Bank risk is positively related to credit booms and is negatively associated with credit information sharing. Further, credit information sharing reduces the detrimental effect of credit booms on bank stability. The authors find that both public credit registries and private credit bureaus are effective in enhancing bank stability in ASEAN countries. These results are robust to regression models with alternative proxies for credit booms and bank risk.

Banks in ASEAN countries tend to have strong lending growth to support the economy, but this could be detrimental to stability of the sector. Credit information sharing schemes should be encouraged because these schemes might enable growth of credit without compromising bank stability. Therefore, policymakers could promote private credit bureaus (PCB) and public credit registries (PCR) to realize their benefits. The authors' research focuses on developing ASEAN countries, but future research could provide more evidence by expanding this study to other emerging economies. In-depth interviews and surveys with bankers and regulatory bodies about these concerns could provide additional insights in the future.

The study is the first to examine the role of PCB and PCR in alleviating the negative impact of credit booms on bank risk. Furthermore, the authors use both accounting-based and market-based risk measures to provide a fuller view of the impact. Finally, there is little evidence on the link between credit booms, credit information sharing and bank risk in ASEAN, so the authors aim to fill this gap.

Using AI to strengthen creativity and problem-solving capabilities of professionals involved in innovation management holds huge potential for improving organizational decision-making. However, there is a lack of research on the use of AI technologies by innovation managers. The study uses the theory of appropriation to explore how specific factors – agile leadership (AL), innovation orientation (IO) and individual creativity (IC) – impact innovation managers' use of generative AI tools, such as ChatGPT (CGA).

The research model is tested through a large-scale survey of 222 Italian innovation managers. Data have been analyzed using structural equation modeling following a two-step approach. First, the measurement model was assessed to ensure the constructs reliability. Subsequently, the structural model was analyzed to draw the conclusions on theorized model relationships and their statistical significance.

The research findings reveal positive associations between IO and IC with CGA, demonstrating that innovation managers who exhibit strong innovation orientations and higher Individual Creativity are more likely to adopt and personalize ChatGPT. However, the study did not confirm a significant association between AL and CGA.

Our findings have important implications for organizations seeking to maximize the potential of generative AI in innovation management. Understanding the factors that drive the adoption and customization of generative AI tools can inform strategies for better integration into the innovation process, thereby leading to enhanced innovation outcomes and improved decision-making processes.

Social media influencers have become constant companions of a large audience of young consumers, but a crucial yet underexplored area of examination relates to the implications of exposure to influencers for an individual’s self-concept. This study aims to examine if and how individuals experience self-discrepancies when exposed to influencers and the impact of such discrepancies on their affect, cognition and behaviors toward the influencers and the brands they endorse.

The authors thematically analyze 17 semistructured interviews, develop a conceptual model and present a set of hypotheses. The hypotheses are tested by analyzing survey data from 503 respondents using structural equation modeling.

Individuals actively engage in comparisons with influencers’ virtual self-presentation and treat them as emblematic of an ideal self. The associated self-discrepancy can lead to both negative and positive affect, but while the latter has a positive impact on e-word of mouth (WOM) and purchase intent, the former has a negative impact. Perceived homophily dampens the impact of exposure to influencer content on discrepancy and strengthens the link between discrepancy and positive affect. Self-acceptance and mindfulness positively moderate the impact of discrepancy on positive affect and negatively on negative affect. Perceived authenticity strengthens the impact of positive affect on e-WOM and dampens the impact of negative affect on purchase intention.

The authors contribute to the literature on self-discrepancies by identifying a consumer context where, in addition to the theoretically predicted negative affect, an individual may experience more positive emotions like feeling motivated or inspired because of the perceived attainability of an influencer as an ideal self. The authors contribute to the influencer marketing literature by examining the influencer–follower relationship and its implications for an individual’s self-concept, including the role played by perceived homophily and authenticity. The authors also contribute to the literature on consumer well-being and identify the role of self-acceptance and mindfulness in shaping consumer experiences.

The authors provide a nuanced analysis of the impact of influencer marketing on consumer behavior with a focus on its impact on an individual’s self-concept. The authors argue for the role of perceived homophily and authenticity in shaping favorable consumer behavior outcomes and offer evidence for more inclusive approaches to marketing.

The authors identify the influencer–follower relationship as a unique social exchange where the source of self-discrepancy is also a homophilic solution provider for achieving one’s ideal self and report both positive and negative effects as outcomes of experiencing a self-discrepancy induced by a target perceived as more attainable. The authors situate understandings of perceived homophily and authenticity along these relationships and identify self-acceptance and mindfulness as mechanisms used by individuals to deal with discrepancies.

The mechanical properties, including elastic constants and moduli, indicate the material’s stiffness and stability. Our calculations reveal that CuMg2GaS4 is a direct bandgap semiconductor, 2.18 eV. A detailed analysis of the electronic structure provides an insight into the bonding characteristics and charge distribution within the material.

This work presents a comprehensive investigation of the structural, electronic, optical, mechanical properties of the CuMg2GaS4 compound using density functional theory (DFT) calculations. Unlike its counterpart CuMg2InS4, which exhibits a tetragonal WS structure, CuMg2GaS4 is found to be an energetically stable in the monoclinic phase.

The calculated effective masses of electrons (0.38 m0) and holes (1.28 m0) suggest promising charge carrier mobility within the compound. Furthermore, based on the evaluation of electronic structure and optical absorption properties of CuMg2GaS4 in relation with the redox potentials of water, this demonstrates its potential as a promising candidate for efficient photocatalytic water splitting under visible light irradiation. These findings contribute to the understanding of the structural and functional properties of CuMg2GaS4 and pave the way for its potential applications in optoelectronic and energy conversion devices.

The prime novelty is to employ ab initio self-consistent Full-Potential Linearized augmented plane wave + local orbital method (FP-LAPW + lo) and investigate the properties of CuMg2GaS4 of structural, mechanical, thermodynamic stabilities, linear optical response.

For successful management of construction projects, a precise analysis of the balance between time and cost is imperative to attain the most effective results. The aim of this study is to present an innovative approach tailored to tackle the challenges posed by time-cost trade-off (TCTO) problems. This objective is achieved through the integration of the multi-verse optimizer (MVO) with opposition-based learning (OBL), thereby introducing a groundbreaking methodology in the field.

The paper aims to develop a new hybrid meta-heuristic algorithm. This is achieved by integrating the MVO with OBL, thereby forming the iMVO algorithm. The integration enhances the optimization capabilities of the algorithm, notably in terms of exploration and exploitation. Consequently, this results in expedited convergence and yields more accurate solutions. The efficacy of the iMVO algorithm will be evaluated through its application to four different TCTO problems. These problems vary in scale – small, medium and large – and include real-life case studies that possess complex relationships.

The efficacy of the proposed methodology is evaluated by examining TCTO problems, encompassing 18, 29, 69 and 290 activities, respectively. Results indicate that the iMVO provides competitive solutions for TCTO problems in construction projects. It is observed that the algorithm surpasses previous algorithms in terms of both mean deviation percentage (MD) and average running time (ART).

This research represents a significant advancement in the field of meta-heuristic algorithms, particularly in their application to managing TCTO in construction projects. It is noteworthy for being among the few studies that integrate the MVO with OBL for the management of TCTO in construction projects characterized by complex relationships.

This paper aims to examine the role of social media communication, tourist satisfaction and destination brand equity components in enhancing destination brand equity based on the Stimulus – Organism – Response (S-O-R) theory.

The conceptual model and research hypotheses were assessed using covariance-based structural equation modeling (SEM). An online survey was used to collect data from 369 domestic tourists who had traveled to Danang and knew about content related to Danang generated by either DMOs or other users on social media.

Except for the effect of DMO-generated social media communication on tourist satisfaction and the impact of destination brand awareness on destination brand loyalty, the findings confirmed the sequential causal relationships between research concepts based on the S-O-R model.

Future research should explore the proposed model based on comparisons of different nationalities to better understand the impact of cultural factors.

DMOs should associate social media with their marketing strategies to enhance destination brand equity, using cutting-edge technologies to create content and update information in a significant way to make communications by DMOs more effective. The findings especially suggest that UGC plays a vital role in improving brand equity dimensions, so DMOs could exploit UGC to engage existing customers and build relationships with potential customers. This research provides guidance for DMOs to improve their brand equity based on social media.

This study has contributed to the destination marketing literature by applying the S-O-R theory to propose a pathway for effectively increasing destination brand equity and highlight the importance of social media communication as a driver to achieve a hierarchical relationship between destination brand equity components and tourist satisfaction from stimulus to organism (e.g. cognition to affect).

Given the importance of identifying the antecedents of innovation for firms to follow and achieve it, the purpose of this paper is to investigate the effect of inclusive leadership (IL) and knowledge sharing (KS) on ambidextrous innovation capabilities, namely exploratory and exploitative innovation. It also explores the possible moderating role of environmental uncertainty in the relationship between KS and ambidextrous innovation.

This study used structural equation modeling (SEM) to examine the relationship among latent factors in the proposed research model based on the data gathered from 118 manufacturing and service firms.

The empirical findings support the significant and positive impact of IL on exploratory and exploitative innovation through the mediating role of KS. It highlights the moderating mechanism of environmental uncertainty in fostering the relationship between KS and ambidextrous innovation.

To gain a deeper understanding of the benefits and important role of knowledge resources, future research should investigate the potential role of IL practices in creating a KS culture for promoting specific forms of innovation such as open innovation or frugal innovation.

The paper provides a valuable understanding and novel approach for managers and directors of firms in developing and emerging countries to improve ambidextrous innovation by practicing IL for fostering KS in organizations.

The paper is unique in its attempts to bridge the research gaps in the literature and provide deeper insights on the mediating role of KS and the moderating effect of environmental uncertainty in pursuing both exploratory and exploitative innovation.

This study aims to identify various factors that have driven the knowledge transfer process for succession purposes in family business since the 1920s and discuss their implications for advancing effective succession planning in this business sector for the next 100 years.

The paper reviews and synthesizes current research on knowledge transfer drivers in family business from 1923 to 2023 and draws out future perspectives on what will influence the knowledge-transferring process in the next 100 years.

Trust, organizational culture and environmental factors such as the need to create competitive advantages, technological advancement and new sources of knowledge have been identified as the most prominent drivers of knowledge transfer for effective succession in family business throughout the 1920–2020s. In the future, the ability to create value, the internationalization process and business innovations will play an essential role in knowledge-sharing among family and non-family members during succession.

The paper provides a review of past development and a future perspective on factors enhancing the effectiveness of knowledge transfer for succession in family firms.