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Images and caption descriptions serve as important visual stimuli that influence consumer preferences; therefore, the current study focuses on property images and captions illustrated on the home pages of accommodation-sharing platforms. Specifically, this study investigates the relative importance of hue, brightness and saturation of a property image and caption description styles on potential consumers’ preferences.

A mixed-method approach was used, and a total of 293 valid responses were collected through a discrete choice experiment approach. Interviews were conducted for additional analyses to explore the detailed explanations.

The utility model demonstrated that the image’s saturation was the most critical attribute perceived by the respondents, followed by caption description style, hue and brightness.

This is one of the first studies to investigate the display of attributes on a digital accommodation platform by exploring potential customers’ stated preferences. This study focuses explicitly on images and captions illustrated on the home page of an accommodation booking platform. Detailed image investigation is also a new research area in sharing economy-related research.

This study aims to explore the opposite effects of single-category versus multi-category products information diversity on consumer decision making. Further, the authors investigate the moderating role of three categories of visitors – direct, hesitant and hedonic – in the relationship between product information diversity and consumer decision making.

The research utilizes a sample of 1,101,062 product click streams from 4,200 consumers. Visitors are clustered using the k-means algorithm. The diversity of information recommendations for single and multi-category products is characterized using granularity and dispersion, respectively. Empirical analysis is conducted to examine their influence on the two-stage decision-making process of heterogeneous online visitors.

The study reveals that the impact of recommended information diversity on consumer decision making differs significantly between single-category and multiple-category products. Specifically, information diversity in single-category products enhances consumers' click and purchase intention, while information diversity in multiple-category products reduces consumers' click and purchase intention. Moreover, based on the analysis of online visiting heterogeneity, hesitant, direct and hedonic features enhance the positive impact of granularity on consumer decision making; while direct features exacerbate the negative impact of dispersion on consumer decision making.

First, the article provides support for studies related to information cocoon. Second, the research contributes evidence to support the information overload theory. Third, the research enriches the field of precision marketing theory.

This paper aims at understanding how automotive firms integrate customer relationship management (CRM) tools and big data analytics (BDA) into their marketing strategies to enhance total quality management (TQM) after the coronavirus disease (COVID-19).

A qualitative methodology based on a multiple-case study was adopted, involving the collection of 18 interviews with eight leading automotive firms and other companies responsible for their marketing and CRM activities.

Results highlight that, through the adoption of CRM technology, automotive firms have developed best practices that positively impact business performance and TQM, thereby strengthening their digital culture. The challenges in the implementation of CRM and BDA are also discussed.

The study suffers from limitations related to the findings' generalizability due to the restricted number of firms operating in a single industry involved in the sample.

Findings suggest new relational approaches and opportunities for automotive companies deriving from the use of CRM and BDA under an overall customer-oriented approach.

This research analyzes how CRM and BDA improve the marketing and TQM processes in the automotive industry, which is undergoing deep transformation in the current context of digital transformation.

This paper aims to explore ChatGPT’s (generative pre-trained transformers) potential as a tool for retailers to improve customer experience and boost sales. While it provides benefits like personalized recommendations and 24/7 assistance, there are limitations, like difficulty in understanding unconventional language. The paper stresses careful integration to overcome these limitations and create a better customer experience. Additionally, it discusses the potential for further development and integration of ChatGPT in retail, such as generating product descriptions and virtual try-on experiences. Finally, the paper encourages retailers to embrace ChatGPT to meet their customer needs.

Case-based methodology involves using specific cases or examples to explore a broader issue or phenomenon. Researchers have analysed real-world cases to identify patterns, themes and insights that can be applied to other contexts or situations. This was useful for understanding complex and multifaceted issues as it allowed us to delve deeper into specific examples and explore the nuances of the situation.

While ChatGPT is a powerful tool for retailers, limitations such as difficulty in understanding non-standard accents and unconventional language can arise, causing customer frustration. Retail managers must integrate ChatGPT in a way that enhances customer experience. In the future, ChatGPT has the potential to generate product descriptions, provide virtual try-on experiences and integrate with augmented or virtual reality technology to offer more immersive experiences. Careful consideration and integration can help retailers overcome these limitations and offer personalized recommendations, round-the-clock assistance and an engaging customer experience that improves sales.

The case topic is very much in a novel stage of research and writing.

The purpose of this study is to present a general review that provides an overview of the concept of sustainability and the effectiveness of mathematics curricula in courses where deeper work on economic and environmental sustainability has become central.

A qualitative methodology consisting of a review based on a pre-defined systematic method was used to exhaustively search and identify the most relevant answers to the research question: What is the role of mathematics to sustainability? To facilitate answering such a broad question, several concrete questions were formulated. Answers from published and unpublished documents were analysed. The quality of the extracted data was assessed, and the results were synthesized.

It was concluded that, on the one hand, the discipline of mathematics has much to contribute to solving the problems of sustainability; on the other hand, new mathematics is appearing stimulated by new challenges.

This work presents social implications in an innovative way. It allows for an increase in educational sustainability by bringing the academic community closer to the business world and the challenges of society and, furthermore, by having a major impact on the motivation of teachers and students to develop cooperative work within university institutions.

The originality is based on an a priori analysis for the construction and implementation of didactic tools for university teacher training in the area of mathematics within the framework of sustainable development, both economically and environmentally.

The widespread integration of artificial intelligence (AI)-enabled personalization has sparked a need for a deeper understanding of its transformative potential. To address this, this study aims to investigate the mental models held by consumers from diverse cultures regarding the impact and role of AI-enabled personalization in their lives (i.e. individual well-being) and in society (i.e. societal well-being).

This paper uses the theories-in-use approach, collecting qualitative data via the critical incident technique. This data encompasses 487 narratives from 176 consumers in two culturally distinct countries, Belgium and Pakistan. Additionally, it includes insights from a focus group of six experts in the field.

This research reveals that consumers view AI-enabled personalization as a dual-edged sword: it may both extend and restrict the self and also contribute to an affluent society as well as an ailing society. The particular aspects of the extended/restricted self and the affluent/ailing society that emerge differ across respondents from different cultural contexts.

This cross-cultural research contributes to the personalization and well-being literature by providing detailed insight into the transformative potential of AI-enabled personalization while also having important managerial and policy implications.

While the importance of human touch for maintaining a tech–touch balance has been stressed, little knowledge exists regarding how such human elements should be incorporated in service settings dominated by technologies. This study aims to examine the outcomes of human touch levels across different travel stages in a hotel stay context.

Data were collected through a survey on 900 participants. Latent class analysis was first conducted to categorize the sample into groups based on human touch levels. Hypotheses were then tested using regression-based moderation analysis with the PROCESS macro for SPSS.

Human touch level negatively predicts perceived risk, which is negatively associated with satisfaction. These effects are particularly significant at check-in and check-out stages. Informational privacy significantly moderates the effect of human touch level on perceived risk. Such interaction effects were spotted at the booking and check-out stages.

This study contributes to theory by revealing the role of human touch elements in technology-driven service scenarios and provides practical guidelines for hotels on sharpening service experience by integrating human touch and technology elements.

Through integrating the service encounter framework and concept of customer touchpoints, this study takes a different approach that integrates both “tech” and “touch” by investigating the effects of customer-owned touchpoints in each core stage of a hotel stay journey.

Online advertising becomes an essential tool to reach the target audience. One of the most widely used strategies is re-targeting. Firstly, this study explores the impact of ethics, privacy and ads' perceived benefits (ad effectiveness and ad relevance) on consumers' attitudes toward online advertising. Secondly, the study investigates the mediating effect of attitudes toward re-targeting online advertising on consumers' purchase intentions. Finally, the study investigates the moderating effect of the perceived ethicality of re-targeting online advertising on consumers' purchase intentions.

Participants (n = 307) were recruited through an online survey platform (MTurk) in the USA. The sample consisted of 65% male and 35% female respondents. The majority are aged 25–34 years, followed by 35–44 years (20%), 45–54 years (14%), 18–24 years (8%) and 55 years and older (6%).

The results show that ad effectiveness and ad relevance influenced consumers' attitudes toward re-targeting. This study shows that consumers are willing to trade their privacy for better search quality. Moreover, perceptions toward the ethicality of re-targeting ads moderated the relationship between consumers' attitudes and purchase intentions.

This study will make several contributions. First, the study will extend the consequential theory in the context of online advertising. Second, the study will assist companies in using re-targeting strategies. The results will reveal which factor is the most important factor impacting consumers' attitudes toward re-targeting strategies.

This is one of the first few papers investigating consumers' perceptions of the ethicality of re-targeting online advertising.

This study aims to inform and guide information professionals in thinking clearly about the challenges and opportunities this technology may create.

This column uses the Web browser Arc as a focal point for exploring elements that seem key to understanding how artificial intelligence (AI) may change our relationship with information. Large language model’s were used to help draft or rewrite sentences. That text was then reviewed or revised by the author.

The following elements are key to understanding the potential of informational interface software like Arc. The ability to abstract information from the original content. The ability to produce multimedia compelling user experiences. The ability to “read” multimodal forms of information and take action based on that “understanding”. This may impact the value exchange between the user and the underlying information, with implications for libraries.

Everything about AI the future of AI or any technology is speculative.

Libraries that wish to continue to be part of adding value to how users interact with information need to pay attention and find ways to adapt.

As new paradigms are created to ensure information exchange is sustainable for everyone, there may be opportunities for libraries. And even if not, libraries may leverage their expertise or relationships to build something that could not be imagined without them. Yet these are only possible if libraries engage.

The substantial expansion of technology and the efficacy of digital platforms in reaching young audiences have led to enhanced targeting and customization of promotional communications. Notwithstanding the expansion and efficacy of contemporary advertising platforms, scholarly attention has not kept pace with this domain of inquiry. This study aims to assess the antecedents of Google Shopping Ads (GSA) on intention to purchase behavior among the Generation Y and Z cohorts.

The current study used a quantitative approach and snowball sampling technique to gather primary data via a questionnaire and Google Forms, which resulted in the collection of 5,808 questionnaires among the cohort members. A principal component analysis and multigroup confirmatory multigroup structural equation modeling (between Generation Y and Z) were used to assess the research data and model.

The results show positive trust and perceived value associations with intention to purchase, particularly among Generation Y and Z consumers. The findings also show negative irritation, product risk and time risk associations with intention to purchase, especially among the Generation Y cohort, which indicates that young consumers generally do not observe perceived risk due to the usage of GSA.

GSA will continue to grow and become an increasingly important integrated marketing communications tool as the digital landscape develops. It can be concluded that young consumers show a high degree of perceived value and low levels of perceived risk due to the use of GSA. This study, therefore, promotes improved understanding among academics, marketers and businesses of search engine advertising among young cohorts of consumers (Generation Y and Z) in a developing country context.