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With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

The study determined the role of personal values in doctor of philosophy (Ph.D.) students’ academic success in Tanzania. Specifically, it looked into the influence of openness to change values, self-enhancement values and conservation values on Ph.D. students’ academic success.

The study employed a cross-sectional survey design, in which 200 Ph.D. students from Tanzanian universities were involved by responding to a questionnaire. The relationship between the variables was determined by using structural equation modeling, and testing of the measurement model was done by confirmatory factor analysis (CFA).

The results indicate that personal values influence Ph.D. students’ academic success. Particularly, openness to change values have an ß value of 0.209 and p value of < 0.001, self-enhancement values have an ß of 0.173 and p-value of < 0.001 and conservation values have ß of 0.339 and p-value of < 0.001.

In the quest to improve Ph.D. students’ academic success, universities and Ph.D. students should foster openness to change values, self-enhancement values and conservation values in Ph.D. students.

The results of this study extend the use of the Schwartz theory of basic human values in explaining the academic success of Ph.D. students in Tanzanian universities. Past studies that applied this theory were based on secondary school and college students. Moreover, based on the author’s knowledge, this study is one of the early studies to systematically look into the role of personal values on Ph.D. students’ academic success. Thus, the study contributes to the existing literature on personal values and academic success because previous studies on this subject could not examine Ph.D. students’ success in isolation.

This study aims to examine the perceived values of the metaverse when adopting it in the luxury hospitality business. Based on the cost–benefit perspective, this research provides solid theoretical contributions and actionable managerial recommendations.

An exploratory sequential mixed-method design was used. For the qualitative phase, 21 hotel managers and 24 hotel guests (who often stay in four-star and five-star hotels and resorts) were interviewed after showing them a series of videos about using the metaverse in the hotel business. Based on the results of the qualitative phase, the analytic hierarchy process method was used, and 476 valid questionnaires were analyzed.

The results highlight the perceived benefits (personalized services, immersive experience and positive brand image) and costs (lack of human touch, time and effort and security and privacy) of metaverse adoption for hotel managers and their guests. In addition, the study determines the weight of each value attribute of metaverse adoption for each travel stage (pre-travel, during travel and post-travel).

Regarding metaverse adoption, the research offers practical suggestions for luxury hotels. For instance, the cost of equipment and the time and effort required are perceived costs of metaverse adoption. To address these challenges, hotels may offer free equipment (e.g. VR headsets) and training to their guests to stimulate the use of the metaverse.

This study addresses a gap in the literature by presenting a conceptual framework for examining metaverse adoption in the luxury hotel scenario. Unlike using conventional models like the technology acceptance model or the unified theory of acceptance and use of technology to investigate a technology’s adoption, this study stands out by unraveling the topic through the lens of value proposition. The latter often comes from an efficient value co-creation process, which is indeed shaped by an adequate appreciation of the congruence of perceived values (i.e. perceived benefits and costs) of metaverse from hotel manager and guest perspectives.

This research draws on drive reduction theory and mental accounting theory to understand how the prospect of reselling used items can influence consumer feelings of consumption guilt and impact their willingness to purchase new products.

We conducted two studies with between-subjects designs to explore this relationship. In Study 1, we examined the correlation between consumers' perceived guilt and their willingness to buy a new product, considering their awareness of the product’s resale potential. Study 2 delved into the aspect of reselling a similar old product already owned by the consumer.

The findings suggest three key insights. First, consumers' awareness of resale potential significantly affects their guilt perception and purchasing decisions. Second, the resale reference price (RRP) can decrease guilt perception but increase the intention to buy a new product. Lastly, when consumers are aware of the resale value of a previously owned product that is similar to the desired new product, the effect of the RRP on their purchasing intent is mediated by consumer guilt.

This research fills a theoretical gap by empirically exploring the emotional motivations behind consumer resale behavior. It presents a novel perspective on how resale activities can shape feelings of guilt and impact purchasing decisions. This offers important implications for understanding the dynamics of consumer behavior in the second-hand market.

This paper aims to investigate the literacy corpus regarding the potential of big data to improve public decision-making processes and direct these processes toward the creation of public value. This paper presents a map of current knowledge in a sample of selected articles and explores the intersecting points between data from the private sector and the public dimension in relation to benefits for society.

A bibliometric analysis was performed to provide a retrospective review of published content in the past decade in the field of big data for the public interest. This paper describes citation patterns, key topics and publication trends.

The findings indicate a propensity in the current literature to deal with the issue of data value creation in the private dimension (data as input to improve business performance or customer relations). Research on data for the public good has so far been underestimated. Evidence shows that big data value creation is closely associated with a collective process in which multiple levels of interaction and data sharing develop between both private and public actors in data ecosystems that pose new challenges for accountability and legitimation processes.

The bibliometric method focuses on academic papers. This paper does not include conference proceedings, books or book chapters. Consequently, a part of the existing literature was excluded from the investigation and further empirical research is required to validate some of the proposed theoretical assumptions.

Although this paper presents the main contents of previous studies, it highlights the need to systematize data-driven private practices for public purposes. This paper offers insights to better understand these processes from a public management perspective.