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The effects of country-of-origin (COO) cues on product evaluations are well documented. However, research on the relative effects of COO compared to other geographical indicators, such as region-of-origin (ROO), on food purchases is still limited. This study investigates how geographical origin labels influence consumers' perceptions of product value and authenticity of foreign food, as well as subsequent purchase intention (PI) and willingness to pay premium prices (WTPPP). The moderating role of health consciousness on these relationships is also examined due to the coronavirus disease 2019 (COVID-19) pandemic.

This study uses a between-subjects experimental design conducted with 300 middle- and high-income Chinese consumers aged between 25 and 50 years. Hypotheses were tested using structural equation modelling.

Whilst under both COO and ROO cues, all five product values positively influenced consumers' WTPPP, only functional, economic and novelty values influenced PI. The ROO cue performed significantly better than the COO cue in eliciting functional, economic and novelty value perceptions, which triggered stronger PI and willingness to pay a premium price. These relationships were mediated by product authenticity (PA) and moderated by consumers' health consciousness (HC).

Because food labels provide salient product information that facilitates consumers' evaluation of products, marketers should assess which product value perceptions they wish to enhance and then choose the appropriate geographical indicators for their labelling strategies.

This study identifies the effects of COO and ROO cues on product values, authenticity, PI and WTPPP. It also provides valuable insights into the role of HC on consumers' purchase decisions, which also aids in understanding the impact of global crises on food purchases.

Responding to the continuing separation of participants and researchers in LIS participatory research, a new methodology is proposed: action partnership research design (APRD). It is asserted that APRD can mitigate or remove the hierarchical structures often inherent in the research process, thus allowing for equal contribution from all.

Building on the bonded design (BD) methodology and informed by a scoping literature review conducted by the same authors, APRD is a human-centered research approach with the goal of empowering and valuing community partnerships. APRD originates from research investigating the use of participatory design methods to foster collaboration between two potentially disparate groups, firstly with adult researchers/designers and elementary school children, and secondly with university faculty and IT professionals.

To achieve this goal, in addition to BD techniques, APRD draws inspiration from elements of indigenous and decolonization research methodologies, particularly those with an emphasis on destabilizing power hierarchies and involving research participants as full partners.

APRD, which emerged from findings from previous participatory design studies, especially those of BD, is based on the premise of partnership, recognizing that each member of a design team, whether researcher or participant/user, has unique expertise to contribute. By considering participants/users as full research partners, APRD aims to flatten the hierarchies exhibited in some LIS participatory research methodologies, where participants are treated more like research subjects than partners.

The emergence of virtual reality (VR) has the potential to revolutionize various industries, including tourism, as it delivers a simulated environment that closely emulates real-life experiences. Therefore, this study aims to explore how the factors, i.e. enjoyment, emotional involvement, flow state, perceived privacy risk, physical risk and cost, influence the customers’ intention to use VR for tourism.

This study integrates the technology acceptance model, hedonic consumption theory with other factors, including cognitive response, authenticity, perceived privacy risk, perceived physical risk, perceived cost and perceived presence. Partial least squares structural equation modelling approach was used to test the proposed research model.

The finding based on the sample of 252 respondents revealed that authenticity is the most influential factor impacting behavior intention followed by perceived cost, attitude, cognitive response and enjoyment. Also, the study supported the moderating impact of personal innovativeness between attitude and behavioral intention to use VR for tourism.

The findings of the study offers practical implications for service providers, site managers, destination marketers, tourist organizations and policymaker to develop more effective strategies for offering VR services for tourism.

This study enriches the current understanding of VR adoption in context of tourism with empirical evidences.

Designing an effective metaverse experience through a tourism digital-twin platform is crucial to the success of metaverse tourism. How such a digital-twin platform should appeal to target users, however, lacks exploration. The study aims to advance a conceptual contribution by successfully creating a metaverse experience through a well-designed digital-twin platform. It also aims to show how the design science approach in tourism can enrich our understanding of digital-twin platform design elements introduced in metaverse experience design.

Guided by the design science approach in tourism, this research conceptualizes the role of digital-twin elements in metaverse experience design and proposes a one-factor between-subject experimental design to examine the effect.

This research conceptualizes how eight unique configurations of digital-twin design, which are embellished in two or three dimensions, shape tourists’ metaverse experience and physical travel intention.

The results offer operators clear strategic guidance on designing an effective tourism digital-twin platform.

This study not only identifies the impact of digital-twin platform design elements but also clarifies how such elements affect customers’ metaverse experiences.

通过旅游数字孪生平台设计有效的元宇宙旅游体验, 对元宇宙旅游的成功至关重要。然而, 这样一个旅游数字孪生平台应该如何吸引目标用户,还缺乏探索。该研究旨在通过精心设计的数字孪生平台成功塑造旅游者的元宇宙体验, 从而在概念上做出贡献。它还旨在展示旅游设计科学方法如何丰富我们对元宇宙体验设计中引入的数字孪生平台设计要素的理解。

在旅游设计科学方法的指导下, 本研究将概念化数字孪生平台设计元素在元宇宙体验设计中的作用, 并提出了一个单因素的受试者间实验设计来检验效果。

本研究将数字孪生平台设计的九种独特配置概念化, 这些配置在三个维度上得到了点缀, 塑造了游客的元宇宙旅游体验和现实旅游意向。

研究结果为产业者设计有效的旅游数字孪生平台提供了明确的战略指导。

现有的研究是第一个研究数字孪生平台设计元素的影响, 阐明了这些元素如何影响旅游者的元宇宙体验。

Diseñar una experiencia efectiva en el metaverso a través de una plataforma digital gemela de turismo es crucial para el éxito del turismo en el metaverso. Sin embargo, la exploración de cómo debería atraer dicha plataforma digital gemela a los usuarios objetivo carece de exploración. El estudio tiene como objetivo avanzar en una contribución conceptual al crear con éxito una experiencia en el metaverso a través de una plataforma digital gemela bien diseñada. También tiene como objetivo mostrar cómo el enfoque de la ciencia del diseño en el turismo puede enriquecer nuestra comprensión de los elementos de diseño de la plataforma digital gemela introducidos en el diseño de experiencias en el metaverso.

Guiada por el enfoque de la ciencia del diseño en el turismo, esta investigación conceptualiza el papel de los elementos gemelos digitales en el diseño de experiencias metaversales y propone un diseño experimental de un factor entre sujetos para examinar su efecto.

Esta investigación conceptualiza cómo ocho configuraciones únicas de diseño gemelo-digital, enriquecidas en dos o tres dimensiones, conforman la experiencia metaversal de los turistas y su intención de viaje físico.

Los resultados ofrecen a los operadores una orientación estratégica clara para diseñar una plataforma gemelo-digital turística eficaz.

Este estudio no sólo identifica el impacto de los elementos de diseño de las plataformas gemelas digitales, sino que también aclara cómo afectan dichos elementos a las experiencias metaversales de los clientes.

Prior research examining cultural effects on customer service expectations has primarily used more generic Western cultural theory on an aggregate scale or with only a single variable to draw conclusions on a customer’s underlying reasoning for buying a service. This study aims to focus on culturally distinct clusters within non-Western nations, specifically exploring within-cluster differences in service expectations within the Confucian Asia cluster.

This study developed a measurement model of Chinese cultural values and service expectations, consisting of a three and five-factor structure, respectively. Data from a sample of 351 diners were analysed using SmartPLS software. The data was compared with similar studies within the Confucian Asia cluster to understand the culture effect on service expectations and within-cluster variations.

The findings underscore the varying importance of cultural values in shaping customer service expectations, emphasizing their relative, rather than equal, significance. The study provides insights into potential within-group differences in customer service expectations within the same cultural cluster – without losing sight of the fundamental cultural heterogeneity of the Confucian culture.

Managers should leverage the distinct cultural values of their operating country to gain insights into diverse customer groups, predict their behaviours and meet their needs and expectations.

This study offers valuable insights to both service management scholars and practitioners by focusing on culturally distinct clusters of non-Western nations and exploring their effects on variation in service expectations within these clusters.

Using social relations theory, we argue that entrepreneurship ecosystems must also include relationships such as market pricing, equality matching, authority ranking and communal sharing to be successful and thrive.

We theorize using Fiske’s typology that a successful entrepreneurial system must have certain characteristics to be successful.

In doing so, we suggest an alternative perspective of the role of exchange relationships in ecosystems which considers both the geographic context and social relationships as equally important ecosystem components. Our contributions include (1) exposing social processes as the explanatory mechanism for exchanges instead of solely market forces, (2) illustrating the role of regional cultural differences in exchanges and (3) emphasizing how entrepreneurs can better realize ecosystem benefits through understanding the methods of exchange in these ecosystems.

Social relationships include a wide variety of different types of resources and exchange mechanisms, so by their inclusion into the entrepreneurship ecosystem literature, a more complete view of ecosystems is possible.

With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.

In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.

Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.

In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.

As research emerged in terms of how narcissism, a negative or dark trait, has been found to be constructive in enhancing entrepreneurial behaviour, there are mixed results regarding the significance of narcissism in the field of entrepreneurship. Additionally, this previous research has mostly been conducted on student or nascent entrepreneur samples within developed economies. Therefore, the purpose of this paper is to explore how narcissistic traits of established entrepreneurs in an emerging economy context infuence their entrepreneurial behaviour both positively and negatively.

Gioia methodology was applied in the qualitative study by means of in-depth interviews, which allowed for the unpacking of narcissistic traits among established entrepreneurs in South Africa. Four themes emerged from the data, and included insights related to entrepreneurial experience influencing behaviour; business growth linked to personal development; opportunity identification versus loss; and identity separation in relation to authentic identity versus an entrepreneurial identity.

The findings of the paper contribute to creating an understanding of how to hone individual narcissistic traits for positive influences that develop entrepreneurs while also contributing to their business development, opportunity realization and identity. In addition, the findings highlighted a separation between established entrepreneurs’ authentic personality and the inputs that end up resulting in the entrepreneurial personality.

This paper highlights the possibility of narcissism functioning as a business process involved in entrepreneurship rather than a necessary personality trait. An interesting dynamic contributed to what seems to be a constant battle between the authentic identity and the entrepreneur identity, gaining deeper insight surrounding established entrepreneurs’ experiences to survive and, more importantly, thrive as entrepreneurs.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

This study investigates whether the underrepresentation of women in executive-level roles within the supply chain management (SCM) field is justified or the result of gender injustices. The analysis examines if there is a gender compensation gap within executive-level SCM roles and whether performance differences or other observable factors explain disparities.

Publicly reported executive compensation and financial data are merged to empirically test if gender differences exist and investigate whether the underrepresentation of women in executive-level SCM roles is unjust.

Women occupy only 6.29% of the positions in the sample of 447 SCM executives. Unlike prior studies, we find that women executives receive higher compensation. The analysis does not identify observable factors explaining the limited inclusion of women in top-level roles, suggesting that gender injustices are prevalent in SCM.

This study only considers observable factors and cannot conclusively determine if discrimination is occurring. The low level of inclusion of women in executive roles suggests that gender injustice is intrinsic within the SCM profession. These findings will hopefully motivate firms to undertake transformative actions that result in outcomes that advance gender equity, ultimately leading to social justice for female SCM executives.

The use of social justice and feminist theories, a focus on SCM roles, and an empirical methodology utilizing objective measures represents a novel approach to investigating gender discrimination in SCM organizations, complementing prior survey-based studies.