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The purpose of this study is to reveal the research trends in guest experiences of service robots in the hospitality industry.

In this study, a review was carried out on the Web of Science (WoS) database with the assistance of bibliometric analysis techniques. Cluster analysis was also employed for this to group important data to determine the relationships and to visualize the areas in which the studies are concentrated. The thematic content analysis method was used to reveal on which customer experiences and on which methods the focuses were.

On the subject of experiences of service robots, the greatest number of publications was in 2021. In terms of country, China has come to the fore in the distribution of publications. As a result of thematic content analysis, it was determined that the leading factor was the main dimension of emotional experience. In terms of sub-dimensions, social interactions attracted more attention. Most of the studies discussed were not based on any theory. Apart from these, the Technology Acceptance Model (TAM), the Service Quality Model (SERVQUAL) and Perceived Value Theory (PVT) were featured more prominently among other studies.

In this study, only the WoS database was reviewed. In future studies, it would be possible to make contextual comparisons by scanning other databases. In addition to quantitative research designs, social dimensions may be examined in depth following qualitative research methods. Thus, various comparisons can be made on the subject with mixed-method research designs. Experimental research designs can also be applied to where customers have experienced human-robot interactions (HRIs).

In the hospitality industry, it is critical to uncover every dimension of guests' robot acceptance. This study, which presents the current situation on this basis, guides future projections for the development of guest experiences regarding service robots in the hospitality industry.

Addressing the significant differences between referral programs and traditional promotional marketing, this paper aims to investigate and examine the impact of how reward-related information is presented within referral programs and how it interacts with reward size and reward allocation.

This study adopts framing effect and equity theory to build the relationship between reward presentation, reward size and reward allocation. Then, two scenario-based experimental studies are designed and conducted on Amazon Mechanical Turk.

The results show that there is no direct impact of reward presentation on referral likelihood, while the effect relies on reward size. As the levels of reward size increase, the referral likelihood gradually shifts from percentage form to dollar form as perceived size mediates the interaction effect on referral likelihood. Further, adding information about reward allocation also indicate the different impacts of equity and inequity on influencing the above findings.

The study contributes to the literature by introducing reward presentation and emphasizes its impact on individual’s behavior decisions in the context of referral programs. This study extends and broadens the scope and effectiveness of the framing effect on traditional promotional marketing strategies, while also bridging the gap in the literature by examining the combined role of information about rewards.

The purpose of this paper is to propose a new grey prediction model, GOFHGM (1,1), which combines generalised fractal derivative and particle swarm optimisation algorithms. The aim is to address the limitations of traditional grey prediction models in order selection and improve prediction accuracy.

The paper introduces the concept of generalised fractal derivative and applies it to the order optimisation of grey prediction models. The particle swarm optimisation algorithm is also adopted to find the optimal combination of orders. Three cases are empirically studied to compare the performance of GOFHGM(1,1) with traditional grey prediction models.

The study finds that the GOFHGM(1,1) model outperforms traditional grey prediction models in terms of prediction accuracy. Evaluation indexes such as mean squared error (MSE) and mean absolute error (MAE) are used to evaluate the model.

The research study may have limitations in terms of the scope and generalisability of the findings. Further research is needed to explore the applicability of GOFHGM(1,1) in different fields and to improve the model’s performance.

The study contributes to the field by introducing a new grey prediction model that combines generalised fractal derivative and particle swarm optimisation algorithms. This integration enhances the accuracy and reliability of grey predictions and strengthens their applicability in various predictive applications.

The Internet of Things (IoT) cloud platforms provide end-to-end solutions that integrate various capabilities such as application development, device and connectivity management, data storage, data analysis and data visualization. The high use of these platforms results in their huge availability provided by different capabilities. Therefore, choosing the optimal IoT cloud platform to develop IoT applications successfully has become crucial. The key purpose of the present study is to implement a hybrid multi-attribute decision-making approach (MADM) to evaluate and select IoT cloud platforms.

The optimal selection of the IoT cloud platforms seems to be dependent on multiple attributes. Hence, the optimal selection of IoT cloud platforms problem is modeled as a MADM problem, and a hybrid approach named neutrosophic fuzzy set-Euclidean taxicab distance-based approach (NFS-ETDBA) is implemented to solve the same. NFS-ETDBA works on the calculation of assessment score for each alternative, i.e. IoT cloud platforms, by combining two different measures: Euclidean and taxicab distance.

A case study to illustrate the working of the proposed NFS-ETDBA for optimal selection of IoT cloud platforms is given. The results obtained on the basis of calculated assessment scores depict that “Azure IoT suite” is the most preferable IoT cloud platform, whereas “Salesman IoT cloud” is the least preferable.

The proposed NFS-ETDBA methodology for the IoT cloud platform selection is implemented for the first time in this field. ETDBA is highly capable of handling the large number of alternatives and the selection attributes involved in any decision-making process. Further, the use of fuzzy set theory (FST) makes it very easy to handle the impreciseness that may occur during the data collection through a questionnaire from a group of experts.

This study aims to provide a new perspective and path to reduce the unsafe behavior of new generation of construction workers (NGCWs) in China. The purpose of this study is to explore the influencing mechanism of work-family balance on the unsafe behavior of NGCWs and test the mediating effect of job satisfaction and the moderating effect of group safety climate.

A theoretical model on the influencing mechanism of work-family balance on unsafe behavior of NGCWs was constructed through theoretical analysis. Research data were collected from 502 NGCWs via a questionnaire survey, and research hypotheses were testified with regression analysis.

The results show that work-family balance not only directly reduces NGCWs’ unsafe behavior but also indirectly reduces it through job satisfaction, which plays a partial mediating role. In addition to positively moderating the relationship between work-family balance and NGCWs’ unsafe behavior, group safety climate can also moderate the relationship between work-family balance and job satisfaction in a positive way.

This study provides practical implications for construction companies to reduce the unsafe behaviors of NGCWs from the perspective of work-family balance. Specifically, construction companies should adopt more flexible work rules, such as flexible organization and rotation systems, to increase their work autonomy. Meanwhile, construction companies need to improve the work environment and basic conditions for NGCWs, establish a reasonable salary system and provide attractive promotion opportunities to increase their job satisfaction. In addition, construction companies should provide active safety lectures and training, and supervisors should improve safety communication and interaction levels. Co-workers should remind workers about their safety attitudes and behaviors promptly. A good group safety climate will be created through the efforts of construction companies, supervisors and co-workers.

This study clarifies the influencing mechanism of work-family balance on the NGCWs’ unsafe behavior and further tests the partial mediating role of job satisfaction and the positively moderating effect of group safety climate on the influence relationship of work-family balance on job satisfaction and NGCWs’ unsafe behavior, which defines the boundary conditions of the relationship between work-family balance and NGCWs’ unsafe behavior, and promotes the effective integration of social exchange theory and theoretical system of influencing mechanism of construction workers’ unsafe behavior.

There is currently a gap in the research regarding the effect of corporate culture on corporate innovation capability. Based on cultural hierarchy theory, in this paper, we explore the interactions between cultural factors and innovation capability in emerging market firms (EMFs). We discuss the mechanisms by which incentive, institutional, and vibrant corporate cultures influence corporate innovation capability. Furthermore, we consider the transformation of artificial general intelligence (AGI) from a tool into a colleague and how this affects the relationship between corporate culture and innovation capability.

An online questionnaire was distributed to corporate employees to explore their attitudes towards AGI and corporate culture. In total, 523 valid questionnaires were empirically analysed using partial least squares structural equation modelling and multigroup analysis (MGA).

The results showed that incentive culture, institutional culture, and vibrant culture had a positive impact on corporate innovation capability. MGA revealed significant differences between employees who considered AGI a tool and those who considered it a colleague. Employees who treated AGI as a colleague were likely to be influenced by a vibrant culture, whereas employees who treated AGI as a tool were likely to be influenced by an incentive or institutional culture.

Building on cultural hierarchy theory, our study provides a new theoretical framework to enrich current research on the relationship between corporate culture and AGI. The study can help EMF managers adjust incentive and institutional cultures before AGI shifts from being a tool to a colleague and negatively impacts innovation capacity.

This study explores the impact of green supply chain integration (GSCI) on enterprise performance (EP) from an organizational capability perspective. Additionally, this study investigated the mediating effect of ambidextrous green innovation (AMGI) and the moderating effect of green legitimacy (GL).

This study followed a five-step systematic review of the literature to ensure the auditability and repeatability of the concept development process: (1) formulation of the question, (2) research area orientation, (3) selection and evaluation of research literature, (4) data analysis and synthesis and (5) reporting and application of results.

This study clarified the concepts and dimensions of four relevant variables and, based on the organizational capability theory (OCT), ambidextrous innovation theory (AIT) and new institutional theory (NIT), explained the interactions among these variables and proposed a conceptual framework. In addition, an agenda for future research has been suggested.

This study provides a new direction for future GSCI research and practice in emerging economies. Enterprises should focus on developing GSCI capabilities to promote its positive impact on enterprise performance through AMGI adoption. Moreover, they must emphasize the acquisition of GL, which provides a certain degree of security, to realize the benefits of AMGI.

This paper aims to propose a solution for dissolving the “privacy paradox” in social networks, and explore the feasibility of adopting a synergistic mechanism of “deep-learning algorithms” and “differential privacy algorithms” to dissolve this issue.

To validate our viewpoint, this study constructs a game model with two algorithms as the core strategies.

The “deep-learning algorithms” offer a “profit guarantee” to both network users and operators. On the other hand, the “differential privacy algorithms” provide a “security guarantee” to both network users and operators. By combining these two approaches, the synergistic mechanism achieves a balance between “privacy security” and “data value”.

The findings of this paper suggest that algorithm practitioners should accelerate the innovation of algorithmic mechanisms, network operators should take responsibility for users’ privacy protection, and users should develop a correct understanding of privacy. This will provide a feasible approach to achieve the balance between “privacy security” and “data value”.

These findings offer some insights into users’ privacy protection and personal data sharing.

This study aims to evaluate the relationship between the estimated levels of diet advanced glycation end products (dAGEs) intake and obesity in university students.

This cross-sectional study included 301 university students aged 18–30 years. dAGEs was estimated using a food frequency questionnaire, for 549 routinely consumed food items and were reported by dividing total energy intake. Dietary intake and sociodemographic data were collected using validated questionnaires, and the anthropometric characteristics were measured. The relationship between anthropometric measurements and dAGEs intake was examined by binary logistic regression.

A total of 43.2% of the participants had high levels of dAGEs. A significant decreasing trend was found in the percentage of carbohydrate intake compared to the increasing trend in dAGEs consumption (p = 0.005). The percentage of fat intake and meat consumption were significantly higher in participants with the highest consumption, compared with the lowest consumption of dAGEs (p = 0.006). According to the dAGEs classification of participants, body mass index, waist circumference and energy intake were found to be significantly related in all model groups. Accordingly, the increase in body mass index, waist circumference and energy intake were determined as a risk factor in those with high dAGEs intakes.

The findings of this study emphasized that higher intake of dAGEs was associated with an increased risk of obesity parameters in college students.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.