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In the era of digital disruption and customer loyalty loss, it has become even more important to shape the experience journey of a firm’s stakeholders. The benefits of experience data (XD) analysis for a competitive advantage and firm performance are well proven in the business-to-customer context. Therefore, this study aims to explore the limited exploitation of XD in the business-to-business (B2B) context.

The data of 338 B2B firms is generated through computer-assisted telephone interviewing using a structured interview guideline. A Mann–Whitney U test and binary linear regression are applied to test hypotheses derived from literature.

The results suggest that XD non-collectors see XD increase efficiency, whereas XD collectors view XD strategically beyond customer data. Additionally, the successful application of XD in firms can be fostered by connecting XD with operational data through digitalised processes, strategic usage and data collection at certain defined points of time.

This study contributes to the understanding of XD perception between collectors and non-collectors and develops determinants for the successful application of XD management. Based on the results, B2B marketing executives from academics and practice can foster the implementation of XD management to improve all firm’s stakeholders’ experiences. In this way, this study contributes to the understanding of managing not only customers’ but other stakeholders’ experiences.

Amidst the turbulent tides of geopolitical uncertainty and pandemic-induced economic disruptions, the information technology industry grapples with alarming attrition and aggravating talent gaps, spurring a surge in demand for specialized digital proficiencies. Leveraging this imperative, firms seek to attract and retain top-tier talent through generous compensation packages. This study introduces a holistic, integrated theoretical framework integrating machine learning models to develop a compensation model, interrogating the multifaceted factors that shape pay determination.

Drawing upon a stratified sample of 2488 observations, this study determines whether compensation can be accurately predicted via constructs derived from the integrated theoretical framework, employing various cutting-edge machine learning models. This study culminates in discovering a random forest model, exhibiting 99.6% accuracy and 0.08° mean absolute error, following a series of comprehensive robustness checks.

The empirical findings of this study have revealed critical determinants of compensation, including but not limited to experience level, educational background, and specialized skill-set. The research also elucidates that gender does not play a role in pay disparity, while company size and type hold no consequential sway over individual compensation determination.

The research underscores the importance of equitable compensation to foster technological innovation and encourage the retention of top talent, emphasizing the significance of human capital. Furthermore, the model presented in this study empowers individuals to negotiate their compensation more effectively and supports enterprises in crafting targeted compensation strategies, thereby facilitating sustainable economic growth and helping to attain various Sustainable Development Goals.

The cardinal contribution of this research lies in the inception of an inclusive theoretical framework that persuasively explicates the intricacies of a machine learning-driven remuneration model, ennobled by the synthesis of diverse management theories to capture the complexity of compensation determination. However, the generalizability of the findings to other sectors is constrained as this study is exclusively limited to the IT sector.

Governments globally are adopting e-Government services to streamline administrative processes and meet citizens' expectations. This study investigates e-Government service quality from citizens' perspectives in 50 Greek municipalities, using the technology acceptance model (TAM) and cognitive theory.

The data from 707 respondents across 50 Greek municipalities are analyzed using structural equation modeling (SEM), ANOVA and moderation analysis. The study assesses the relationships between key factors and citizens' intentions to use e-Government services, examining the impact of demographics and the digital divide.

The study reveals that perceived attractiveness (PA), perceived usefulness (PU), perceived ease of use (PEOU) and awareness (AWA) significantly influence citizens' behavioral intentions (BINTs) toward municipal e-Government services. Interestingly, PEOU negatively impacts users' intentions, suggesting dissatisfaction with portal attractiveness and utility. The study explores the influence of demographic variables and the digital divide on citizens' BINTs, highlighting economic activity and income as crucial determinants.

The study emphasizes the significance of user-friendly design, PU, PEOU and AWA campaigns for the development of effective e-Government platforms. Strategies to address the digital divide and promote citizen engagement are essential for enhancing user experience, service utility and AWA, ultimately fostering a positive attitude toward e-Government.

Addressing demographic differences ensures inclusive e-Government systems, while bridging the digital divide promotes equitable service delivery and citizen engagement.

This research provides insights into factors influencing citizens' BINTs toward e-Government services. The study's examination of demographic attributes and the digital divide enhances understanding, contributing to the development of citizen-centric e-Government services and supporting inclusive digital transformations.

This study aims to investigate the antecedents of behavioral intention and actual usage of mobile payment (m-payment) services during the COVID-19 pandemic among Indian consumers.

The proposed research model of this study is based on the extended framework of the Unified Theory of Acceptance and Use of Technology (UTAUT2) by using two additional variables, namely, perceived security (PS) and perceived trust (PT). In total, 436 sample respondents are chosen from Indian consumers with experience using m-payment services through the online survey method. The data analysis and proposed hypothetical relationships were tested using confirmatory factor analysis and structural equation modeling techniques.

The results confirm that performance expectancy, effort expectancy, facilitating conditions, PS, PT, habit and price value are antecedents of consumer intention toward adopting m-payment services. Furthermore, behavioral intention significantly influences the actual usage of m-payment services during the COVID-19 pandemic.

Though the impact of COVID-19 has been observed during the research period in getting responses from m-payment service users, the constructs used in the study are confined to the UTAUT2 model, and dimensions related to COVID-19 are not directly included in the measurement scale. The study’s findings propose valuable insights for service providers and policymakers.

This study’s results offer valuable insights to the service providers and policymakers to achieve the Government of India digital India objective of “Faceless, Paperless and Cashless” transactions.

This study’s results contribute to extending the empirical research literature on m-payment as antecedents of behavioral intention toward the adoption of m-payment services during the COVID-19 pandemic. Furthermore, this study assumes important interrelationships among UTAUT2 constructs with the additional incorporation of PS and PT.

This study aims to identify the propensity for clients (legal and natural persons) to adopt peer-to-peer (P2P) short-term insurance policies as opposed to traditional and/or centralized short-term.

In this paper data was collected through a survey of 102 sampled short-term insurance clients using convenience sampling. The TAM2 questionnaire was adapted to evaluate the intention to adopt a P2P insurance policy.

The findings of this study shed light on the factors influencing the adoption and (dis)continuation of short-term insurance products, both traditional and digital, among South African consumers. The results demonstrate that perceived usefulness, ease of use, trust, risk perception and subjective norm play crucial roles in individuals' intention to use or (dis)continue the use of these insurance products.

The study's findings provide actionable insights for practitioners in the short-term insurance sector, with a focus on marketers and e-commerce professionals. These insights emphasize the need to prioritize user-friendly design and trust-building measures in the development of P2P insurance systems. Additionally, practitioners should consider harnessing the power of social influence and carefully balancing innovative features with familiarity in their marketing efforts. These strategies are poised to enhance the adoption and competitive positioning of P2P insurance solutions amidst the evolving landscape of digital transformation.

This study makes a substantial contribution by employing the technology acceptance model (TAM) in a novel and unconventional manner. It not only explicates the intricate dynamics governing the adoption and discontinuation of short-term insurance products, encompassing both conventional and digital alternatives, within the South African consumer milieu but also extends its purview to infer the reasons behind the limited widespread adoption of the digital counterpart, despite its superior value proposition compared to the traditional offering. The findings elucidate the critical determinants shaping individuals' decisions in this dynamic market segment. This research enhances the global discourse on insurance adoption with a unique South African perspective and furnishes insurers and marketers with empirically grounded insights to optimize their strategies and cultivate substantive connections with their target demographic.

The purpose of this study is to augment the perceived service quality (PSQ) dimensions as well as evaluate the effects of pandemic susceptibility and severity by appending crucial enablers of customer satisfaction (CS) in the restaurant industry (RI).

The top 10 restaurants from Mumbai and Kolkata were selected based on the Conde Nast Traveller Magazine List, 2020. The study used a cross-sectional design to collect responses from 840 respondents across the two major metropolitans of India after the second wave of COVID-19 by employing a structured questionnaire. The proffered hypotheses in this study were validated using factor analysis and structural equation modelling (SEM) techniques.

This research espies pivotal facilitators of CS and customers' perceived value (CPV). The results divulge that food quality (FQ) and tangibility dimensions markedly enhance CS while the FQ and digital technologies (DT) dimensions augment CPV in Indian restaurants. The study asserts that CPV acts as a partial mediator between FQ and DT on the one hand and CS on the other. In addition, perceived pandemic susceptibility (PPSU) and perceived pandemic severity (PPSE) moderate the association between CPV and CS in restaurants.

This study exemplifies the critical enablers of CS and CPV that may invigorate restaurant owners, managers and policymakers to prioritize the identified dimensions to aggrandize CS and CPV quotients.

The study enriches the literature by assimilating DT and CPV dimensions in a comprehensive theoretical framework. The research is unique in attempting to unfurl the moderating effects of PPSU and PPSE in the RI.

In this article, the authors draw-upon an extended unified theory of acceptance and use of technology (UTAUT) and propose a research model involving performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC) and competitive pressure (CP) as potential salient factors explaining the adoption of digitalization in European SMEs. The authors also postulate that there may be cross-cultural differences, thereby leading us to include the country as a moderator in the model.

The authors validate this model with a cross-cultural sample involving 188 owner-managers from the Czech Republic and Slovakia and through the partial least square structural equation modeling (PLS-SEM) techniques as well as multi-group analysis.

The results using the study’s global dataset indicate that PE, FC and CP significantly affect owner-managers intentions toward digitalization in SMEs. The authors’ application of the multi-group analysis also suggests that although the two countries differ in digitalization adoption intention, the differences are statistically insignificant. In the conclusion, the authors highlight several implications these findings have for theory and practice.

The authors recommend that the providers of emerging digital technologies should improve on the performance features of those technologies and ensure they are relevant to the SMEs. By doing so, the adoption of digitalization will grow, because owner-managers of SMEs will have the confidence that adopting such technologies will improve their operations. Second, SMEs are required to provide adequate organizational and technical infrastructure to support digitalization adoption.

Aside from being among the few attempts to extend the explanatory power of UTAUT with PE, EE, FC and CP in investigating digitalization adoption in SMEs context, this study also validates its model with rigorous methodological approach as well as three datasets (global, Czech Republic and Slovakia) thereby strengthening the validity of the results.

This study combines institutional and organizational learning perspectives to investigate the impact of institutional distance and institution-specific cross-border acquisition experience in emerging markets on cross-border acquisition performance.

The sample consists of 874 transactions involving targets across 37 emerging markets by 484 different acquirers from 45 developed and emerging markets. The authors decompose institutional distance and acquisition experience along their cultural, administrative, geographic and economic dimensions.

The authors find that cultural, administrative and geographic distance have a negative impact on acquisition performance. In contrast, economic distance does not appear detrimental to acquisition performance across markets. The study provides evidence that a company may apply learnings from previous transactions in similar cultural and economic emerging market environments to elevate the likelihood of a successful acquisition.

This study offers a more fine-grained perspective of the distance concept by decomposing the concepts of institutional distance and acquisition experience along different institutional dimensions. The research across 37 emerging markets sheds light on which of the similarities and differences between these markets are relevant concerning acquisition experience and performance.

Tourists’ irresponsible behaviours (e.g. damaging flowers, writing and painting on the walls and throwing waste material in the water and around the sea site) damage the coastal environment. The irresponsible behaviour of tourists has raised concerns about the sustainability of the coastal tourism environment. The purpose of this study is to identify and explain the behavioural patterns of tourists that can influence the environmentally responsible behaviours (ERBs) of tourists, particularly in the context of coastal tourism. The study aims to provide a theoretical and practical explanation of destination image and perceived destination value (PDV) in shaping ERB with the mediating role of destination social responsibility (DSR) among tourists at the coastal touring destination.

The study used a quantitative research design and data were gathered from the five beaches in Karachi. Structured equation model was used to analyse the direct and mediating effect while stepwise regression was used to analyse the moderating effect of DSR. The results of the direct effect showed that cognitive image has a significant effect on the affective image, while the insignificant effect on conative image and ERB. While the affective image has a significant effect on conative and ERB, and finally, results showed a significant effect of conative image on ERB. Results of the study revealed that PDV significantly mediated the relationship between cognitive, affective and conative destination image and ERB. Finally, the study’s results revealed that DSR has significantly moderated the relationship between affective, conative destination image, PDV and ERB.

The results are divided into three categories, direct effect, mediating effect and moderating effect. The results of the direct effect showed that cognitive image has a significant effect on the affective image, while the insignificant effect on conative image and ERB. While affective image has a significant effect on conative and ERB, and finally, results showed a significant effect of conative image on ERB. It is found in the results that PDV significantly mediated the relationship between cognitive, affective and conative destination image and ERB. Finally, the study’s results revealed that DSR has significantly moderated the relationship between affective, conative destination image, PDV and ERB.

First, data has been collected from a single geographic area of Pakistan. Therefore, cross-country data are required to compare the ERB of tourists. Second, only local respondents are considered in the study; future studies may include foreign tourists as well. Finally, data has been collected during one month in summer, which may have measured the experience of only summer. The respondent may have different perceived values and destination images during winter. The future study may split data collection into summer and winter to cover diverse perceptions of tourists.

It is almost impossible for coastal destinations to achieve a competitive advantage without attaining sustainable coastal environments. Clean and green beaches and responsible behaviour towards marine mammals can only be achieved through tourists’ ERB. This study has major contributions towards society by reserving the natural environment of coastal areas.

This research will significantly contribute to the existing literature by extending the ERB knowledge through the theoretical lens of cognitive-affective-conative models and social expectancy theory. Moreover, PDV as a mediator and DSR as a moderator will enhance the understanding of ERB and extend the existing literature. Further research has provided a strong understanding of how cognitive, effective and conative image helps in influencing the ERB of tourists. Moreover, research will benefit destination managers and policymakers to enhance the image and perceived value of touring destinations. Finally, this study is a unique attempt to present a comprehensive model which could be applicable to diverse situations and areas.

This study aims to investigate tourists’ attitudes and intentions regarding the usage of Chat Generative Pre-trained Transformer (ChatGPT) for accessing tourism information. Furthermore, by integrating the perceived risks associated with ChatGPT and the theory of planned behavior (TPB), this research examines the impact of three types of perceived risks, such as privacy risk, accuracy risk and overreliance risk, on tourists’ behavioral intention.

Data were gathered for this study by using two online survey platforms, thus resulting in a sample of 536 respondents. The online survey questionnaire assessed tourists’ perceived risks, attitude, subjective norm, perceived behavioral control, behavioral intention and demographic information related to their usage of ChatGPT.

The structural equation modeling analysis revealed that tourists express concerns about the associated risks of using ChatGPT to search for tourism information, specifically privacy risk, accuracy risk and overreliance risk. It was found that perceived risks significantly influence tourists’ attitude and intention toward the usage of ChatGPT, which is consistent with the hypotheses proposed in previous literature regarding tourists’ perceived risks of ChatGPT.

This work is a preliminary empirical study that assesses tourists’ behavioral intention toward the use of ChatGPT in the field of tourism. Previous research has remained at the hypothetical level, speculating about the impact of ChatGPT on the tourism industry. This study investigates the behavioral intention of tourists who have used ChatGPT to search for travel information. Furthermore, this study provides evidence based on the outcome of this research and offers theoretical foundations for the sustainable development of generative AI in the tourism domain. This study has limitations in that it primarily focused on exploring the risks associated with ChatGPT and did not extensively investigate its range of benefits.

First, to address privacy concerns that pose significant challenges for chatbots various measures, such as data encryption, secure storage and obtaining user consent, are crucial. Second, despite concerns and uncertainties, the introduction of ChatGPT holds promising prospects for the tourism industry. By offering personalized recommendations and enhancing operational efficiency, ChatGPT has the potential to revolutionize travel experiences. Finally, recognizing the potential of ChatGPT in enhancing customer service and operational efficiency is crucial for tourism enterprises.

Recognizing the potential of ChatGPT in enhancing customer service and operational efficiency is crucial for tourism enterprises. As their interest in adopting ChatGPT grows, increased investments and resources will be dedicated to developing and implementing ChatGPT solutions. This enhancement may involve creating customized ChatGPT solutions and actively engaging in training and development programs to empower employees in effectively using ChatGPT’s capabilities. Such initiatives can contribute to improved customer service and overall operations within the tourism industry.

This study integrates TPB with perceived risks in ChatGPT, thus providing empirical evidence. It highlights the importance of considering perceived risks in tourists’ intentions and contributes to the sustainable development of generative AI in tourism. As such, it provides valuable insights for practitioners and policymakers.

本研究旨在调查游客对使用ChatGPT获取旅游信息的态度和意向。此外, 通过将与ChatGPT相关的感知风险与计划行为理论（TPB）相结合, 本研究探讨了三种感知风险（隐私风险、准确性风险和过度依赖风险）对游客行为意向的影响。

本研究通过两个在线调查平台收集了536名受访者的数据。在线调查问卷评估了游客对ChatGPT使用的感知风险、态度、主观规范、感知行为控制、行为意向以及与其使用ChatGPT相关的人口统计信息。

结构方程建模分析显示, 游客对使用ChatGPT搜索旅游信息的相关风险表示关切, 特别是隐私风险、准确性风险和过度依赖风险。发现感知风险显著影响游客对使用ChatGPT的态度和意向, 与先前有关游客对ChatGPT感知风险的文献中提出的假设一致。

本研究将TPB与ChatGPT中的感知风险相结合, 提供了实证证据。它强调了在考虑游客意向时考虑感知风险的重要性, 并为旅游中生成AI的可持续发展提供了贡献。因此, 它为从业者和政策制定者提供了宝贵的见解。