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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.

Unacceptable behaviour in retailing – negative customer deviance (NCD) is rising, damaging retailers financially. Current research investigates forms of NCD by addressing its impact on employees but overlooks its effects on bystander-customers and their retail channel preferences. As channel switching within retailing is increasing unprecedentedly, this research investigates its correspondence with NCD encounters.

This research uses structural equation modelling, based on data collection administered through a web-based survey of 1,008 customers of at least 16 years of age, to analyse the research model.

The findings reveal unexplored forms of NCD perceived by bystander-customers in retailing and their consequences, linking it to bystander-customers' ill-being, dissatisfaction with the shopping experience, a decrease in store commitment and an increase in their retail channel-switching intentions. Additionally, the research uncovers moderating variables.

This research tests NCD dimensions and effects on bystander-customers, which indicate the need for retailers to address shopping values, attitudes and commitment through corrective, proactive and long-term strategic actions.

As one of the first studies to investigate the impact of NCD on bystander-customers' intentions to switch from store-based to online shopping, strategies for retailers are developed to help diminish and control NCD-induced threats to bystander-customers.

An AI concierge is a technologically advanced, intelligent and personalized assistant that is designated to an individual customer, proactively taking care of that customer’s needs throughout the service journey. This article envisions the idea of AI concierges and discusses how to leverage AI concierges in the customer journey.

This article takes a conceptual approach and draws insights from literature in service management, marketing, psychology, human-computer interaction and ethics.

This article delineates the fundamental forms of AI concierges: dialog interface (no embodiment), virtual avatar (embodiment in the virtual world), holographic projection (projection in the physical world) and tangible service robot (embodiment in the physical world). Key attributes of AI concierges are the ability to exhibit semantic understanding of auditory and visual inputs, maintain an emotional connection with the customer, demonstrate proactivity in refining the customer’s experience and ensure omnipresence through continuous availability in various forms to attend to service throughout the customer journey. Furthermore, the article explores the multifaceted roles that AI concierges can play across the pre-encounter, encounter and post-encounter stages of the customer journey and explores the opportunities and challenges associated with AI concierges.

This paper provides insights for professionals in hospitality, retail, travel, and healthcare on leveraging AI concierges to enhance the customer experience. By broadening AI concierge services, organizations can deliver personalized assistance and refined services across the entire customer journey.

This article is the first to introduce the concept of the AI concierge. It offers a novel perspective by defining AI concierges’ fundamental forms, key attributes and exploring their diverse roles in the customer journey. Additionally, it lays out a research agenda aimed at further advancing this domain.

This research aims to understand the primary challenges encountered by entrepreneurs operating in emerging economies, where entrepreneurship plays a vital role. The study places a particular emphasis on entrepreneurs in Brazil.

The research methodology involved the analysis of data obtained from interviews, using both content analysis and Grey Relational Analysis techniques.

The analysis revealed several prominent difficulties that entrepreneurs face in these domains. These challenges encompassed issues such as grappling with intricate taxation systems and the associated tax burden, navigating government bureaucracy, securing access to essential financing and initial investments, contending with the absence of supportive government programs and addressing the dynamic nature of market conditions. The findings on the most critical barriers reveal potential pathways for entrepreneurs, policymakers and universities to act in developing the entrepreneurial ecosystem in emerging economies.

The insights garnered from this research have the potential to inform the formulation of robust public policies aimed at fostering entrepreneurship and innovation in emerging countries. Furthermore, these findings can serve as a valuable resource for planning initiatives designed to train engineers to become successful entrepreneurs.

This study aims to comprehensively assess how digital maturity degree (DMD) impacts sustainable supply chain management (SSCM) performance through the mediating role of SSCM practices in businesses in Vietnam.

The hypotheses were performed using partial least square-structural equation modeling (PLS-SEM) with data collected from a survey of over 234 managers having responsibility in the supply chain field in Vietnam. Qualitative data were collected through semistructured interviews with 6 experts to deepen understanding of the relationship between DMD and SSCM.

The results show the mix-results in the relationship between SSCM practices and SSCM performance dimensions while DMD strongly impacts SSCM practices. Also, this study finds the mediating role of SSCM practices on the relationship between DMD and SSCM performance.

This is the first study to investigate the role of DMD on SSCM practices and SSCM performance, using empirical evidence. Moreover, the authors integrate both qualitative and quantitative for understanding complex SSCM phenomena. The present study also helps businesses improve their SSCM performance by leveraging SSCM practices and developing their digital technologies in the long-term view.

The advantages of applying big data analytics for organizations to boost innovation performance are enormous. By collecting and analysing substantial amounts of data, firms can discern what works for their customer needs and update existing products while innovating new ones. Notwithstanding the evidence about the effects of big data analytics, the link between big data analytics and innovation performance is still underestimated. Especially in today's fast-changing and complicated environments, companies cannot simply take big data analytics as one innovative technical tool without fully understanding how to deploy it effectively.

This study tries to investigate this relationship by building on the knowledge absorptive capacity perspective. The authors conceptualized effective use of big data analytics tools as one general absorptive capacity rather than a simple technical element or skill. Specifically, effectively utilize big data analytics tools can provide values and insights for new product innovation performance in a turbulent environment. Using online survey data from 108 managers, the authors assessed their hypotheses by applying the structural equation modelling method.

The authors found that big data analytics capacity, which can be conceptualized as one absorptive capacity, can positively influence product innovation performance. The authors also found that environmental turbulence has strong moderation effects on these two main relationships.

These results establish big data analytics can be regarded as one absorptive capacity, which can positively boost an organization's innovation performance.

This study aims to apply the theory of consumption value to analyze the utilization of Metaverse technologies within hospitality and tourism while examining the factors that impact consumer intentions to use the Metaverse.

This paper aims to consider an extensive study spanning the period October 2021 to March 2023 was conducted to understand the shifts in an individual's intention to use Metaverse technologies in hospitality.

The findings of this study confirm that individual attitudes to the Metaverse and trust in Metaverse technologies significantly impact their intention to use the Metaverse.

The study aims to provide fresh insights into how individuals perceive Metaverse technologies in the context of choosing hotels and resorts, which enriches the understanding of consumer behaviors around Metaverse technology in hospitality.

This study aims to consider not only tourist intentions to use the Metaverse but also how diverse consumption values impact user attitudes, an area currently underresearched.

The study aims to study the extent of research done in luxury marketing in an emerging economy like India by conducting a bibliometric analysis. A period of 21 years has been considered to present a comprehensive picture for results and analysis. Key findings indicate the gaps and scope of further research for academics in India and abroad. The findings indicate a dearth of research by scholars and academicians in luxury, counterfeit and masstige, especially when there is a surge of the upper middle class in India. More specifically, Indian-grown luxury brands also present a massive scope for future research.

The primary objective of this research is to identify and analyze the influence of digital marketing practises on performance of business of manufacturing enterprises in Delhi-NCR. A quantitative and descriptive research design was implemented in this study. 368 participants from various enterprises were chosen. Questionnaires were used to collect data on the benefits and challenges which are perceived of digital marketing, as well as usage and business performance of these enterprises. SPSS v24.0 is utilized to carry out statistical analysis on the collected data. The study discovered that advantages and problems of digital marketing practices influenced the usage of digital marketing. Furthermore, the execution of digital marketing had a prominent influence on sales and total number of customers of these enterprises, and this relationship was moderated by type and size of businesses. It was discovered that using digital marketing strategies increased business profits by raising digital marketing awareness among consumers and various industries. It made digital marketing easier to implement in various businesses. However, research should be conducted for appropriate generalization in a larger scale.

This study aims to design a facility network for the weavers to do direct business in the Indian handloom industry by using tourists as potential customers.

Data from 4,001 weavers of the handloom industry and 82 tourist locations of Varanasi city were collected. This data was analysed using the k-mean and elbow methods to determine the locations and the optimal number of collection centres, selling stores and warehouses to provide opportunities for the weavers to do direct business.

The study's findings showed that a greater opportunity could be provided with four optimal collection centres and selling stores, along with two warehouses in the handloom industry of Varanasi city. These results provide valuable guidance for policymakers to plan the Varanasi handloom network of facilities efficiently and effectively to improve the conditions of weavers.

Determining the optimal locations is crucial for designing a facility network. The proposed network will aid the government and policymakers in comprehending and pinpointing potential sites to establish new facilities in the handloom industry in Varanasi, a city with tourism potential.