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Most research has investigated the fear of missing out (FOMO) in the context of online activities, often associated with negative personal outcomes such as fatigue and stress. However, given the increased desire to be informed and included associated with FOMO, organizations that can effectively meet these needs may develop or strengthen social and structural bonds, thereby turning short-term customers with FOMO into lifelong patrons. This study aims to examine the relationship between FOMO and favorable organizational outcomes as mediated by several constructs associated with the desire for information and inclusion.

This research was conducted within the higher education sector of the service industry. FOMO served as the IV. The mediators represented context-specific aspects of campus involvement and inclusion. Organizational outcomes related to the long-term services relationship served as the DVs. The sample consisted of 435 students recruited from research pools at two southern universities in the USA. Exploratory factor analysis, OLS regression and the Hayes–Macro were used to examine the data.

The results demonstrate that FOMO is positively associated with students’ desires for information and inclusion (informal peer interaction, campus involvement, informal faculty interaction, campus information media use and a preference for in-person course scheduling), which are associated with the desirable university outcomes of satisfaction, connection and alumni donation/activity intentions.

If a university fosters unstructured time spent with faculty and peers, and promotes campus information media involvement, students with higher levels of FOMO are more likely to be satisfied, feel connected to the university and report intentions to donate time and money as alumni.

Prior research on FOMO is generally focused on internet and social media use; this study takes a broader perspective and identifies the effect of FOMO on a desire for information and inclusion within a novel context (a service environment). It also associates FOMO with favorable long-term service relationship outcomes that fortify social and structural bonds.

The privacy calculus based on a single stakeholder failed to explain users' co-owned information disclosure owing to the uniqueness of co-owned information. Drawing on collective privacy calculus theory and impression management theory, this study attempts to explore the co-owned information disclosure of social network platform users from a collective perspective rather than an individual perspective.

Drawing on collective privacy calculus theory and impression management theory, this study explores the co-owned information disclosure of social network platform users from a collective perspective rather than an individual perspective based on a survey of 740 respondents.

This study finds that self-presentation and others presentation directly positively affect users' co-owned information disclosure. Also, self-presentation, others presentation and relationship presentation indirectly positively affect users' co-owned information disclosure via relationship support. Furthermore, personal privacy concern, others' privacy concern and relationship privacy concern indirectly negatively affect users' co-owned information disclosure via relationship risk.

The findings develop the theory of collective privacy calculus and impression management, which offer insights into the design of the collective privacy protection function of social network platform service providers.

Nanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc.

In this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021.

Among these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

This is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

This paper aims to examine the effect of contractual factors and noncontractual factors on tax avoidance (TA).

The sample comprises 400 firm-year observations of 67 companies listed on the HDAX during the period 2008–2017. The generalized least square panel regression is applied.

The study results confirm a significant effect of long-term chief executive officer (CEO) compensation incentives and CEO attributes on TA. Findings exhibit a significant impact of foreign CEO on TA, whereas an insider CEO mitigates TA. The results hold for several robustness tests, with lag effective tax rate as dependent variable and with splitting foreign CEO into European and non-European origin.

First, the sample is limited to 400 firm-year observations and to the German context. For shareholders, the study provides first evidence on relationships between the geographical and internal versus external labor market for CEOs and TA. For researchers, the findings underline the importance of integrating behavioral approaches like place attachment theory and the rooting theory in the theory of TA.

To the best of the authors’ knowledge, this is the first study to examine the impact of both contractual determinants and behavioral determinants on TA in the German context as an emerged economy with a dualistic corporate governance. This study contributes to the existing literature regarding the scientific debates about the impact of CEOs and CEO attributes on TA. It also analyses the balance between the place attachment theory and the rooting theory in the face of the compensation outcomes of agency theory.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

This study aims to examine consumer perception during COVID-19 and identifies cruise industry marketing strategies to fill a gap in crisis management and product pricing literature.

This study developed and validated two-factor measurement scales (vaccine perception and protective behavior), which predicted cruise intents well. This study revealed how geo-regional factors affect consumer psychology through spatial analysis.

This study recommended pricing 7-day cruises at $1,464 (the most preferred length). The results also showed that future price hikes would not affect demand and that coastal marketing would help retain customers.

This study contributed to the business, hospitality and tourism literature by identifying two new and unique factors (vaccine perception and protective behaviors), which were found to affect consumers’ intention to travel by cruise significantly. The result provided a better understanding of cruise tourists’ pricing preferences and the methods utilized could easily be applied to other cruise markets or tourism entities.

This study aims to examine the influence of reward personalization on financing outcomes in the Industry 5.0 era, where reward-based crowdfunding meets the personalized needs of individuals.

The study utilizes a corpus of 218,822 crowdfunding projects and 1,276,786 reward options on Kickstarter to investigate the effect of reward personalization on investors’ willingness to participate in crowdfunding. The research draws on expectancy theory and employs quantitative and qualitative approaches to measure reward personalization. Quantitatively, the number of reward options is calculated by frequency; whereas text-mining techniques are implemented qualitatively to extract novelty, which serves as a proxy for innovation.

Findings indicate that reward personalization has an inverted U-shaped effect on investors’ willingness to participate, with investors in life-related projects having a stronger need for reward personalization than those interested in art-related projects. The pledge goal and reward text readability have an inverted U-shaped moderating effect on reward personalization from the perspective of reward expectations and reward instrumentality.

This study refines the application of expectancy theory to online financing, providing theoretical insight and practical guidance for crowdfunding platforms and financiers seeking to promote sustainable development through personalized innovation.

This paper examines whether the adoption of Japan’s Stewardship Code by institutional investors influences their preference for investee companies' governance quality. The Code, introduced by the Financial Services Agency in 2014, promotes constructive engagement between institutional investors and investee companies. Engagement with investees should improve institutional investors' ability to assess governance quality across their portfolios. The paper examines if this results in a positive relationship between the levels of Code-compliant institutional shareholding and investee governance quality.

The association between Code-compliant institutional shareholding levels and a governance quality score is examined for Nikkei 500 companies.

A positive association is observed between shareholdings by Code-compliant institutional investors and investee governance, with board independence playing a key role. Analysis shows that the association between institutional shareholding and governance is stronger for the Code-compliant shareholding than for overall institutional shareholdings. In addition, no significant relationship is found between the levels of shareholding by non-Code-compliant institutional investors and the governance quality score of investee companies. Taken together, the results suggest that Code adoption strengthens institutional investors' preference for high-quality investee governance.

Despite the introduction of stewardship regulation worldwide, there is a scarcity of empirical research that examines its operation. The study contributes to the existing literature by providing insights into how compliance with stewardship regulation influences institutional investor decision-making.

Driven by the Sustainable Development Goals (goals 7, 8, 12 and 13), this study investigates the moderating role of financial development in the link between energy poverty and a sustainable environment in African nations.

Panel cointegration analysis, fully modified least squares, Driscoll and Kraay least squares and method of moments quantile regression were used as estimation techniques to examine the link between financial development, energy poverty and sustainable environment for 28 African nations. Energy poverty is measured using two proxies-access to clean energy and access to electricity, while the environment is gauged using ecological footprint.

The regression outcomes show that access to clean energy and electricity negatively impacts the ecological footprint across all the quantiles; hence, energy poverty increases environmental degradation. Financial development positively influences environmental degradation in the region at the upper quantiles. Similarly, the interactive term of energy poverty and financial development has a significant positive impact on ecological footprint; thus, the financial sector adds to energy poverty and environmental degradation. The results of other variables hint that per capita income and institutions worsen environmental quality while urbanisation strengthens the environment.

This study offers fresh insights into the moderating effect of financial development in the link between energy poverty and sustainable environment in African countries.

Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.

The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.

The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.

This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.

The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.

This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.