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This study examines how customer loyalty among DiDi users in China is affected by two types of online social interaction (transactional and interpersonal) and how the online interaction–customer loyalty relationship is mediated by three kinds of perceived benefits (functional, social-hedonic and safety).

This study empirically examines research hypotheses based on a questionnaire survey of 428 DiDi consumers.

The results reveal that transactional interactions significantly enhance customer loyalty among DiDi users via the partial mediating effects of customers' perceived functional, social-hedonic and safety benefits. By contrast, interpersonal interactions do not directly influence customer loyalty, and only social-hedonic benefits fully mediate the positive influence of interpersonal interactions on loyalty.

This study contributes to the theoretical development of interactive marketing management by examining how two types of online social interactions contribute to customer loyalty on sharing economy platforms by influencing the perception of benefits. It also provides useful managerial insights to help ride-sharing platforms design online social interaction functions that improve customer perceptions and loyalty.

Public-private partnership (PPP) projects for construction waste recycling have become the main approach to construction waste treatment in China. Risk sharing and income distribution of PPP projects play a vital role in achieving project success. This paper is aimed at building a practical and effective risk sharing and income distribution model to achieve win–win situation among different stakeholders, thereby providing a systematic framework for governments to promote construction waste recycling.

Stakeholders of construction waste recycling PPP projects were reclassified according to the stakeholder theory. Best-worst multi–criteria decision-making method and comprehensive fuzzy evaluation method (BWM–FCE) risk assessment model was constructed to optimize the risk assessment of core stakeholders in construction waste recycling PPP projects. Based on the proposed risk evaluation model for construction waste recycling PPP projects, the Shapley value income distribution model was modified in combination with capital investment, contribution and project participation to obtain a more equitable and reasonable income distribution system.

The income distribution model showed that PPP Project Companies gained more transaction benefits, which proved that PPP Project Companies played an important role in the actual operation of PPP projects. The policy change risk, investment and financing risk and income risk were the most important risks and key factors for project success. Therefore, it is of great significance to strengthen the management of PPP Project Companies, and in the process of PPP implementation, the government should focus on preventing the risk of policy changes, investment and financing risks and income risks.

The findings from this study have advanced the application methods of risk sharing and income distribution for PPP projects and further improved PPP project-related theories. It helps to promote and rationalize fairness in construction waste recycling PPP projects and to achieve mutual benefits and win–win situation in risk sharing. It has also provided a reference for resource management of construction waste and laid a solid foundation for long-term development of construction waste resources.

PPP mode is an effective tool for construction waste recycling. How to allocate risks and distribute benefits has become the most important issue of waste recycling PPP projects, and also the key to project success. The originality of this study resides in its provision of a holistic approach of risk allocation and benefit distribution on construction waste PPP projects in China as a developing country. Accordingly, this study adds its value by promoting resource development of construction waste, extending an innovative risk allocation and benefit distribution method in PPP projects, and providing a valuable reference for policymakers and private investors who are planning to invest in PPP projects in China.

The purpose of this research study was to understand the simultaneous competitive and social gains of machine learning (ML) and artificial intelligence (AI) usage in organizations. There was a knowledge hiatus regarding the contribution of the deployment of ML and AI technologies and their effects on organizations and society.

This study was grounded on the dynamic capabilities (DC) and ML and AI automation-augmentation paradox literature. This research study examined these theoretical perspectives using the response of 239 Indian organizational chief technology officers (CTOs). Partial least square-structural equation modeling (PLS-SEM) path modeling was applied for data analysis.

The results indicated that ML and AI technologies organizational usage positively influenced DC initiatives. The findings depicted that DC fully mediated ML and AI-based technologies' effects on firm performance and social performance.

This study contributed to theoretical discourse regarding the tension between organizational and social outcomes of ML and AI technologies. The study extended the role of DC as a vital strategy in achieving social benefits from ML and AI use. Furthermore, the theoretical tension of the automation-augmentation paradox was explored.

Organizations deploying ML and AI technologies could apply this study's insights to comprehend the organizational routines to pursue simultaneous competitive benefits and social gains. Furthermore, chief technology executives of organizations could devise how ML and AI technologies usage from a DC perspective could help settle the tension of the automation-augmentation paradox.

Increased ML and AI technologies usage in organizations enhanced DC. They could lead to positive social benefits such as new job creation, increased compensation to skilled employees and greater gender participation in employment. These insights could be derived based on this research study.

This study was among the first few empirical investigations to provide theoretical and practical insights regarding the organizational and societal benefits of ML and AI usage in organizations because of their DC. This study was also one of the first empirical investigations that addressed the automation-augmentation paradox at the enterprise level.

This study aims to explore the relationships between consumer data vulnerability, peer privacy concerns and consumers' continued usage intention of sharing accommodation platforms, as well as the moderating effects of the various benefits perceived by consumers.

Data were collected from 327 consumers of sharing accommodation platforms in China. Partial least squares (PLS)-structural equation modeling (SEM) was conducted to test the research hypotheses.

The results suggest that both consumer data vulnerability and peer privacy concerns have negative effects on consumer's continued usage intention of sharing accommodation platforms, which can be further mitigated by consumer perceived economic, social and emotional benefits. This study also finds that consumer data vulnerability has a positive effect on consumer's peer privacy concerns.

This study gives that managers of sharing accommodation platforms a better understanding of how consumers respond to their data vulnerability on sharing accommodation platforms. In addition, this study also highlights the measures that platforms may employ to mitigate the negative influence of consumer data vulnerability and consumers' peer privacy concerns, as well as the measures to reduce consumers' peer privacy concerns.

While previous studies mainly examined the driving forces of consumers' engagement in sharing accommodation, this study focuses on the impediment. With communication privacy management theory to explore the relationships between consumer data vulnerability, peer privacy concerns and continued usage intention of sharing accommodation platforms, as well as the moderating effects of consumers' perceived benefits, this study facilitates a more comprehensive understanding of consumers' engagement in sharing accommodation.

This study aims to investigate the psychological mediating mechanisms through which omnichannel integration affects customer word-of-mouth (WOM) behaviors in omnichannel retailing.

Guided by prior omnichannel retailing studies, the authors identify taxonomy of customer WOM behaviors with three archetypes, namely, face-to-face WOM, online store WOM, and social media WOM. Then, the authors draw on social exchange theory (SET) to explain how omnichannel integration influences customer WOM behaviors through the mediating roles of perceived personal preference fit and perceived social relatedness. The authors empirically tested the model using structural equation modeling and multiple mediation analysis with a field survey of 335 omnichannel customers.

Perceived personal preference fit positively influences face-to-face WOM and social media WOM, whereas perceived social relatedness is positively associated with face-to-face WOM, online store WOM, and social media WOM. Furthermore, transactional integration and relational integration positively affect perceived personal preference fit, whereas relational integration has a positive effect on perceived social relatedness. Finally, perceived personal preference fit mediates the effects of transactional integration and relational integration on face-to-face WOM and social media WOM. Perceived social relatedness mediates the effects of relational integration on face-to-face WOM, online store WOM, and social media WOM.

The authors' study advances the omnichannel retailing literature by proposing a taxonomy of customer WOM behaviors in omnichannel retailing and identifying the mediating mechanisms through which omnichannel integration influences customer WOM behaviors.

This study aims to assess how airline companies can frame their message strategies given the effects of the COVID-19 pandemic.

Message forms used in empirical tests included message framings, regulatory focuses and benefit types. A total of 450 participants were recruited through an online survey.

Promotion-focused behavioral intention was more effective when personal economic benefit was negatively framed. However, prevention-focused behavioral intention was more effective where social benefit was positively framed. The results were moderated by personal traits such as the level of perceived COVID-19 threat.

The findings of this study can help create new communication strategies during risky or critical situations.

本文实证分析中所使用的信息形式包括信息框架、监管重点和利益类型。通过线上问卷调查, 共招募了450名参与者。数据分析采用了ANOVA检验和Hayes（2017）的PROCESS检验。

当个人经济利益是负面框架时, 促进定向的行为意向更有效。然而, 当社会利益是正面框架时, 防御定向的行为意向更为有效。上述关系受到个人特质的调节, 如COVID-19感知威胁的程度。

本研究的结果是新颖且有意义的, 有助于在风险或危急情况下创造新的传播策略。

Las formas de mensaje utilizadas en las pruebas empíricas incluyeron encuadres de mensajes, enfoques normativos y tipos de beneficios. Se reclutó a un total de 450 participantes a través de una empresa en línea porque era difícil recopilar datos durante la pandemia.

Este estudio pretende evaluar cómo las compañías aéreas pueden enmarcar sus estrategias de mensajes teniendo en cuenta los efectos de la pandemia de COVID-19.

El comportamiento centrado en la promoción fue más eficaz cuando el beneficio económico personal se enmarcó negativamente. Sin embargo, el comportamiento centrado en la prevención fue más eficaz cuando el beneficio social se enmarcó positivamente. Los resultados fueron moderados por rasgos personales como el nivel de amenaza percibida de COVID-19.

Las conclusiones de este estudio son muy novedosas y significativas en la medida en que pueden ayudar a crear nuevas estrategias de comunicación durante situaciones de riesgo o críticas.

The purpose of this study is to contribute to previous research on customer relationships by quantitatively examining differences in the monetary benefits obtained by consumers using social and commercial relationships to make purchases from small and medium-sized enterprises (SMEs).

Customer transaction and relationship data from an SME in the USA is used to quantitatively assess the value of different marketplace relationships in an entrepreneurial context. Tobit regression is used to empirically model and test the impact of specific relationship characteristics on customer discounts.

Customers using social connections to make purchases obtain significantly larger discounts than customers using commercial connections; customers using direct connections attain significantly larger discounts than consumers using indirect connections (referrals). Interestingly, when examined by connection type, direct and indirect connections do not produce significant differences for social connections, yet they yield notable differences for commercial connections. The findings provide valuable insights to entrepreneurs for understanding and managing customer relationships.

This study empirically demonstrates that social relationships can be both prevalent and influential in the marketplace. The methodology used to quantitatively assess the monetary value associated with different methods of engaging with SMEs allows objective comparisons among different types of customer relationships. Quantification also allows important relationship characteristics to be empirically examined, including how the relationships compare to one another and to nonpersonal marketing activities. Ultimately, these novel contributions generate important insights to help marketers and entrepreneurs better understand customer relationships.

When retail businesses, especially small businesses with greater vulnerability, could not meet consumers in person during the recent pandemic crisis, how did they adapt to the situation? This study examined how small business practitioners (SBPs’) perceptions, trust and adoption intention levels for social media, as well as the relationships among these variables, changed before and during the crisis based on the integration of the contingency theory and the diffusion of innovation theory (DIT).

Online surveys were conducted with USA SBPs before (n = 175) and during (n = 225) the recent pandemic. The hypotheses were tested using structural equation modeling (SEM), multivariate analysis of variance (MANOVA) and multiple-group SEM analysis.

The results confirmed significant sequential positive relationships between SBPs’ perceived external pressure and perceived benefits of adopting social media, which in turn led to their trust in and then adoption intentions for social media. Further, the comparisons between the pre- and in-pandemic samples revealed that SBPs’ perceptions and adoption intentions all became significantly higher during (vs before) the pandemic, but the structural relationships among these variables weakened during the pandemic.

This study uses a novel approach to integrate the contingency theory with the DIT to propose small businesses' perceptions, trust and adoption intentions for social media during the innovation decision process under rapid contingency changes. Our findings also offer practical implications including recommendations for small businesses’ innovation management as well as training programs.