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While past research has begun exploring digital-free tourism, tourism digital detox and their benefits, no study to date has comprehensively mapped trends, findings and limitations across this growing body of literature. This study aims to conduct the first bibliometric analysis and systematic literature review to address this gap.

This study utilized a mixed methodology of bibliometric analysis and systematic literature review. Structured search strings were applied to databases to identify relevant papers, which were screened according to inclusion criteria. Bibliometric analysis of included papers was performed using Bibliometrix, an R package enabling network visualization, statistical tests and science mapping. This allowed the identification of significant topics, theories, methods, citations and publication trends over time.

The results clearly show that factors previously lacking attention in past tourism research, such as the interplay between online and offline experiences during travel, are emerging as important determinants of travelers' well-being. This study outlines the current state of scholarship on managing technology's impacts on travelers' psychological and social needs. Specifically, we found limited research integrating how digital detox tools shape pre-trip planning, on-site activities and post-trip sharing of travel experiences.

This is the first study to comprehensively map trends and findings in digital-free tourism and tourism digital detox research using a blended bibliometric analysis and systematic literature review methodology. It offers vital direction toward strengthening theoretical understanding and supporting balanced connectivity and fulfillment for all tourists going forward. By addressing limitations, this research approach helps develop this area of scholarship in a unified manner.

The complex geometries of human tissues are characterized by the employment of phantoms in various fields of medicine ranging from active treatment stages to educational purposes. Despite the exceptional abilities of the fused filament fabrication (FFF) technology to produce rapid and patient-specific complex anatomical models, the issue of human tissue-filament material incompatibilities persists owing to the lack of attenuation coefficients in the same range as biological tissues. The purpose of this study is to develop a novel biodegradable filament that can mimic human hard tissues by addressing the challenge mentioned above.

The current study addresses the issue through proposing a novel biodegradable radiopaque filament containing poly (lactic acid) (PLA) and antimony trioxide (Sb₂O₃) with increasing amounts (3 wt%, 5 wt% and 10 wt%) for hard tissues. Other than the thermal/flow characterization and internal structural analyses, as for evaluating the effectiveness of the produced filament under computed tomography (CT) imaging, two detailed anthropomorphic phantoms (L3 vertebra and femur bone) are produced and tested.

Results show that Sb₂O₃ disperse homogeneously and serve as a nucleating agent for PLA crystallization. Gyroid pattern gets very close isotropic structure with the highest hounsfield unit (HU) values. 5 wt% Sb₂O₃ is required to get the HU values of cortical bone. The produced model hard tissues are in very compatible with patient images in all details including cortical thickness.

The results of this study will contribute to the development of radiopaque products in medical applications using three-dimensional printing.

The current research shows that inexpensive, patient-specific, detailed medical models can be produced with a novel biodegradable radiopaque filament containing PLA/Sb₂O₃. To the best of the authors’ knowledge, no study has examined the use of Sb₂O₃ in radiopacity applications in any polymeric material.

This study explores the influence of the Top Management Team (TMT) social capital on business model innovation in business ecosystems.

This study examines the impact of internal and external TMT social capital on enterprises’ business model innovation, explores the relationship between internal and external TMT social capital, and investigates how business ecosystem health moderates the relationship between external TMT social capital and enterprises’ business model innovation. These hypotheses are proposed and tested using a hierarchical regression analysis with data from 168 Chinese firms.

First, both internal and external TMT social capital exert a significantly positive influence on an enterprise’s business model innovation. Second, internal TMT social capital positively contributes to the development of external TMT social capital, affecting business model innovation. Finally, the moderating effect of business ecosystem health on the relationship between external TMT social capital and business model innovation depends on the dimensions. Specifically, the productivity of the business ecosystem negatively moderates this relationship, whereas the niche creation capability of the business ecosystem has a positive moderating effect.

These findings enrich prior research on business model innovation within the business ecosystem, thoroughly exploring the critical role of TMT social capital. This study reveals the diverse impacts of internal and external TMT social capital on business model innovation and the intricate relationship between these elements. Furthermore, it emphasizes that the success of enterprise’s business model innovation within a business ecosystem depends on the alignment and adaptation to dynamic ecosystem conditions. By presenting these insights, this study provides valuable practical implications for enterprises aiming to cultivate social capital within business ecosystem to facilitate business model innovation.

Generative conversational artificial intelligence (AI) demonstrates powerful conversational skills for general tasks but requires customization for specific tasks. The quality of a custom generative conversational AI highly depends on users’ guidance, which has not been studied by previous research. This study uses social exchange theory to examine how generative conversational AI’s cognitive and emotional conversational skills affect users’ guidance through different types of user engagement, and how these effects are moderated by users’ relationship norm orientation.

Based on data collected from 589 actual users using a two-wave survey, this study employed partial least squares structural equation modeling to analyze the proposed hypotheses. Additional analyses were performed to test the robustness of our research model and results.

The results reveal that cognitive conversational skills (i.e. tailored and creative responses) positively affected cognitive and emotional engagement. However, understanding emotion influenced cognitive engagement but not emotional engagement, and empathic concern influenced emotional engagement but not cognitive engagement. In addition, cognitive and emotional engagement positively affected users’ guidance. Further, relationship norm orientation moderated some of these effects such that the impact of user engagement on user guidance was stronger for communal-oriented users than for exchange-oriented users.

First, drawing on social exchange theory, this study empirically examined the drivers of users’ guidance in the context of generative conversational AI, which may enrich the user guidance literature. Second, this study revealed the moderating role of relationship norm orientation in influencing the effect of user engagement on users’ guidance. The findings will deepen our understanding of users’ guidance. Third, the findings provide practical guidelines for designing generative conversational AI from a general AI to a custom AI.

This study aims to examine the indirect effects of workaholism on life satisfaction (Samples 1 and 2) and work performance (Sample 2) as mediated by presenteeism. This study also examined whether these indirect effects differed at various levels of work−home segmentation.

The authors collected data from two samples of employees with jobs in administration (Sample 1) and engineering (Sample 2).

The results showed that workaholism was associated with higher levels of presenteeism, whereas work−home segmentation was negatively related to presenteeism. Presenteeism was also negatively related to life satisfaction (Samples 1 and 2) and work performance (Sample 2). Furthermore, the positive effects of workaholism on presenteeism were stronger at low levels of work−home segmentation. Finally, the indirect effects of workaholism on life satisfaction (Samples 1 and 2) and work performance (Sample 1) were significantly mediated by presenteeism at low levels of work−home segmentation, but not at high levels of work−home segmentation.

This research demonstrates that work−home segmentation buffers the detrimental effects of workaholism.

This article examines the relationships between objective overqualification, volunteering as an extra-work activity and job satisfaction.

The study draws on a vast secondary sample of 20,686 British employees across four waves covering the period 2009–2017. The bivariate ordered probit estimate was used to test the study hypotheses in the bioprobit procedure in STATA.

Our study unravels compelling insights. Overqualified employees experience lower job satisfaction and engage more in volunteering activities. The results emphasised that voluntary work allows the utilisation of skills and fulfils basic psychological needs, leading to enhanced general well-being and higher job satisfaction.

Overqualified employees, by actively engaging in volunteering, not only make valuable contributions to society but also experience positive spillover effects that significantly influence their workplace attitudes and behaviours. This underscores the potential for promoting volunteering as an effective means to mitigate the private and social overqualification.

This study provides valuable insights into the role of overqualification as well as resulting job dissatisfaction, in shaping volunteering decisions. This insight contributes to the overqualification literature and strengthens our understanding of volunteering as an important mechanism in the relationship between overqualification and job satisfaction.

The objective is to develop an empirical model estimating the relationship and interaction amongst the factors affecting and enhancing green finance (GF) in developing economies like India.

Around nine growth-accelerating enablers of green financing were found through literature and unstructured interviews and analysed using the total interpretive structural modelling (TISM) method. The hierarchical link between each factor is established using TISM, and further to evaluate the driver-dependent relationship the Matriced’ Impacts Croises Appliquee Aaun Classement (MICMAC) approach is utilised.

The findings demonstrate an interrelationship between growth-accelerating factors, where the political environment and information and communication technology (ICT), have minimal dependency but a strong driving force. Political environment and ICT are found as strategic-level factors lying at the bottom of the model driving towards the dependent variables. The government should focus on enacting effective policies such as the green credit guarantee scheme and carbon credit and establishing a regulatory framework to enhance green financing.

This study examines the literature to generalise the findings and focus on the primary motivators for developing green financing. To increase green financial activity, practitioners must concentrate on aspects with significant driving forces. Furthermore, it makes organisations more profitable, efficient and competitive and promotes long-term growth.

The study is the first in the literature which identifies the growth-accelerating factors of green financing using the TISM and MICMAC-based hierarchical models.

Although prior research has employed various variables to predict player churn, the dynamic evolution of the behavioral patterns of players has received limited attention. In this study, churn prediction models are developed by incorporating the progress level, in-game purchase, social interaction, behavioral pattern and behavioral variability (BV) of players in social casino games (SCGs). The study distinguishes churn prediction between two player groups: monetizers and non-monetizers.

This study employs three machine learning techniques—logistic regression, decision trees and random forests—using real-world player data from an SCG company to construct churn prediction models. Two experiments were conducted. In Experiment 1, BV was combined with four other variable categories to effectively predict churn behaviors across all players (n = 52,246). In Experiment 2, churn prediction models were developed separately for monetizers (n = 16,628) and non-monetizers (n = 35,618).

The findings from Experiment 1 indicate that incorporating BV significantly improves the overall performance of churn prediction models. Experiment 2 demonstrates that churn prediction models achieve better performance and predictive accuracy for monetizers and non-monetizers when BV is calculated over the 3-day to 7-day and 7-day to 14-day windows, respectively.

This study introduces BV as a novel variable category for churn prediction, emphasizing within-person variability and demonstrating its effectiveness in enhancing model performance. Churn prediction models were independently constructed for monetizers and non-monetizers, utilizing different time windows for variable extraction. This approach improves predictive performance and highlights key differences in critical variables influencing churn across the two player groups. The findings provide valuable insights into churn management strategies tailored for monetizers and non-monetizers.

This research explores the impact of approach and avoidance drivers on users’ discontinuance usage intention (DUI) in social commerce (s-commerce). It also examines the mediating role of perceived enjoyment and the moderating role of autotelic personality traits.

Using a purposive sample of 465 s-commerce users in Malaysia, data analysis was conducted using partial least squares structural equation modeling (PLS-SEM).

This study demonstrates that informational support is a key approach driver, enhancing perceived enjoyment and reducing perceived deception and DUI. Although perceived deception does not directly affect perceived enjoyment, it significantly predicts DUI. Additionally, the impact of informational support on DUI is mediated by perceived enjoyment and moderated by autotelic personality.

S-commerce stakeholders, including platform developers, sellers and policymakers, can leverage these findings to foster a sustainable s-commerce environment. Implementing the proposed strategies can enhance perceived enjoyment and reduce DUI among current users.

This research advances s-commerce marketing and IS discontinuance studies by extending the approach-avoidance motivations model (AAMM) into s-commerce and incorporating autotelic personality as a moderating factor provides new insights into user DUI in this evolving field.

Social media-induced fatigue has received much attention in recent years. Although studies have discussed the association between related stress and fatigue, few studies have analyzed the media characteristics corresponding to social interaction stress and their effects on fatigue from the perspective of social relations and interaction structure. This paper aims to explore the association between network heterogeneity, social stressors and fatigue from the perspective of a person-environment misfit.

The survey data of 402 social media users were analyzed, and hierarchical regression models were used to validate the proposed theoretical model.

Data analysis showed that supplementary misfits (network heterogeneity) and complementary misfits (information overload and role conflict) positively affect fatigue. Additionally, supplementary misfits can indirectly affect fatigue through complementary misfits. The analysis results also confirmed the positive moderating effect of the relationship climate between network heterogeneity and role conflict. However, the quality of information does not reduce the impact of network heterogeneity on information overload.

This study reveals the mechanisms affecting user fatigue due to a lack of match with the environment. It also provides evidence of stress self-regulation among social media users and suggests how managers can help alleviate it.