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Mobile health (mHealth) services are considered an important means of relieving the problems of the aging population. The efficiency of mHealth services can be enhanced by engaging more elderly users and guaranteeing their continued use. However, limited attention has been directed toward investigating elderly users' continuance intention regarding mHealth services. The purpose of this paper is to explain elderly users' continuance intention by investigating the contingent role of technology anxiety and health anxiety on affective trust and cognitive trust.

Survey data were collected from 232 elderly users to verify the research model and hypotheses based on structural equation modeling (SEM).

This study revealed that both affective and cognitive trust enhance elderly users' continuance intention regarding their use of mHealth services. Health anxiety strengthens the effect of cognitive trust but weakens the effect of affective trust with regard to continuance intention. Furthermore, technology anxiety strengthens the effect of affective trust but not the effect of cognitive trust with regard to continuance intention.

This study is one of the first to examine elderly users' continuance intention regarding mHealth services use from the perspective of affective and cognitive trust, thus enriching the extant literature on the use of mHealth services. Additionally, this study sheds light on the contingent effects of technology anxiety and health anxiety on affective and cognitive trust, which have been neglected by previous research.

Drawing on the cognitive–affective–conative framework, this study aims to develop a model of service robot acceptance in the hospitality sector by incorporating both cognitive evaluations and affective responses.

A mixed-method approach combining qualitative and quantitative methods was used to develop measurement and test research hypotheses.

The results show that five cognitive evaluations (i.e. cuteness, coolness, courtesy, utility and autonomy) significantly influence consumers’ positive affect, leading to customer acceptance intention. Four cognitive evaluations (cuteness, interactivity, courtesy and utility) significantly influence consumers’ negative affect, which in turn positively affects consumer acceptance intention.

This study provides significant implications for the design and implementation of service robots in the hospitality and tourism sector.

Different from traditional technology acceptance models, this study proposed a model based on the hierarchical relationships of cognition, affect and conation to enhance knowledge about human–robot interactions.

This chapter presents ways in which educators can use technology to enhance learning for students with autism spectrum disorder (ASD). The chapter begins with an overview of the communicative and social challenges that students with ASD typically experience. Next, it defines technology-aided instruction and intervention (TAII) and offers suggestions for educators planning to incorporate TAII into their classroom. The third section offers specific supports on using TAII to enhance communication while the final section offers specific supports on using TAII to enhance affective and social learning. Case studies and tables with clearly defined supports are included throughout the chapter. Educators may combine the supports presented because each support addresses more than one characteristic or need. In addition, educators will find that these supports benefit students with neurodiverse learning needs who may not be identified with ASD.

To date, to the best of the authors’ knowledge, the use of implicit measures in the service research domain is limited. This paper aims to introduce implicit measures and explain why, or for what purpose, they are worthwhile to consider; how these measures can be used; and when and where implicit measures merit the service researcher’s consideration.

To gain an understanding of how implicit measures could benefit service research, three promising implicit measures are discussed, namely, the implicit association test, the affect misattribution procedure and the propositional evaluation paradigm. More specifically, this paper delves into how implicit measures can support service research, focusing on three focal service topics, namely, technology, affective processes including customer experience and service employees.

This paper demonstrates how implicit measures can investigate paramount service-related subjects. Additionally, it provides essential methodological “need-to-knows” for assessing others’ work with implicit measures and/or for starting your own use of them.

This paper introduces when and why to consider integrating implicit measures in service research, along with a roadmap on how to get started.

The purpose of this paper is to discuss recent developments in neuroscientific methods and demonstrate its potential for the service field. This work is a call to action for more service researchers to adopt promising and increasingly accessible neuro-tools that allow the service field to benefit from neuroscience theories and insights.

The paper synthesizes key literature from a variety of domains (e.g. neuroscience, consumer neuroscience and organizational neuroscience) to provide an in-depth background to start applying neuro-tools. Specifically, this paper outlines the most important neuro-tools today and discusses their theoretical and empirical value.

To date, the use of neuro-tools in the service field is limited. This is surprising given the great potential they hold to advance service research. To stimulate the use of neuro-tools in the service area, the authors provide a roadmap to enable neuroscientific service studies and conclude with a discussion on promising areas (e.g. service experience and servicescape) ripe for neuroscientific input.

The paper offers service researchers a starting point to understand the potential benefits of adopting the neuroscientific method and shows their complementarity with traditional service research methods like surveys, experiments and qualitative research. In addition, this paper may also help reviewers and editors to better assess the quality of neuro-studies in service.

This paper discusses how educational policies have shaped the development of large-scale educational data and reviews current practices on the educational data use in selected states. Our purposes are to: (1) analyze the common practice and use of educational data in postsecondary education institutions and identify challenges as the educational crossroads; (2) propose the concept of Data Literacy (DL) for teaching (Mandinach & Gummer, 2013a) and its relevance to researchers and stakeholders in postsecondary education; and (3) provide future implications for practices and research to increase educational DL among administrators, practitioners, and faculty in postsecondary education.

We used two guiding conceptual frameworks to analyze the common practice and use of educational data in postsecondary education institutions and identify challenges as the educational crossroads. First, we used the 4Vs of Big Data by Rajan (2012) to examine the misalignment between the policy mandate and the practices. The elements of the 4Vs of Big Data – volume, velocity, variety, and veracity – help us to depict how Big Data enables educators to organize, store, manage, and manipulate vast amounts of educational data at the right moment and at the right time. Second, we used the conceptual framework for DL proposed by Gummer and Mandinach (in press). They interpret DL “as the collection, examination, analysis, and interpretation of data to inform some sort of decision in an educational setting” (p. 1, in press).

Using the guiding frameworks, we identified four educational data crossroads as follows:

Crossroad 1: Unintended Increase in Workload Volume;

Crossroad 2: Unrealistic Expectations of Data Velocity;

Crossroad 3: Data Variety in Silos; and

Crossroad 4: Data Veracity and Policy Agenda Mismatch.

In this paper, we explain each of these crossroads in more detail with some examples.

Much of the existing body of literature, exemplary practices, as well as federal and state funding has been focused on K-12 education contexts. In this paper, we identify current practices and challenges of educational data in the institutions of higher education. Additionally, this paper presents the application of the exemplary practices of data literacy development in postsecondary education and implications for future practices of data literacy development in postsecondary education.

Within the scope of the research, articles about service robots were examined by the systematic review method.

The research aims to evaluate the articles on service robots, an artificial intelligence (AI) application in restaurant businesses, using a systematic review method. In systematic reviews, the data obtained as a result of scanning databases to find an answer to a research question are synthesized and reported. The criterion sampling technique, one of the purposeful sampling methods, was used for the sample of the research. Inclusion and exclusion criteria were applied within the scope of screening.

The articles on service robots were carried out between 2018 and 2023. In terms of research methods, most of the articles are quantitative, while there are studies on mixed and qualitative methods. In studies, data were generally collected by survey technique. The keywords of the studies on service robots are examined; the most commonly used words were service robot and AI, technology, restaurant, satisfaction, revisit intention, consumer behavior, intention, preference, hospitality and foods. The objectives of the articles pertinent to service robots are mostly to determine people's attitudes and acceptance toward these services focuses.

The studies seem to focus more on customer acceptance, trust, expectations, risks, adaptation, reasons for preference, impact on creative services, emotional and cognitive effects and human–robot interaction. Despite this, it is observed that there are fewer studies on topics such as the development of service robots in restaurant businesses, their reflections on the future, future opportunities and the quality of chef service robots. Based on this, it is recommended to consider studies that will serve as a reference for revealing innovative opportunities that can meet future expectations in order to increase the quality of service robots in restaurant businesses.

The swift and unanticipated integration of telework by European companies due to COVID-19 gave rise to distinct features of telework. These attributes underscore the necessity of analysing its impact on employees’ well-being. This paper explores how telework experiences impact well-being by influencing work–life balance and job satisfaction. Additionally, it investigates whether employee preferences for telework are a contributing factor.

Based on the data provided by the “living, working and COVID-19” e-survey, structural equation models (SEM) were used to test the hypotheses. Specifically, a multiple-mediation approach and path analyses were applied to measure the relationship between the variables under study. The moderating role of preference for telework was also tested.

Key findings support that telework experience has a positive impact on well-being, both directly and indirectly, particularly via work–life balance. Although preference for telework strengthens the relationship between telework experience and well-being, it does not enhance the predictive power of the mediated model.

These results have important implications from an applied perspective. Human capital departments as well as managers should design telework programmes to create a positive experience since this will ensure a positive influence on the perception of work–life balance, job satisfaction and well-being.

COVID-19 as a sudden environmental constraint forced the implementation of telework without proper planning and training. Thus, how the employees experience this major change in their working conditions has affected their well-being. The present paper contributes to clarifying how the proposed variables relate under such constraints.

Emerging technologies have been transforming most industries. A wide range of emerging technologies such as blockchain, internet of things (IoT), artificial intelligence (AI), machine learning (ML), robotics and many others have changed the way in which firm value chain activities or processes were executed traditionally. The mining industry has also witnessed the introduction of these emerging technologies in various processes from the exploration stage to the final processing of ores. The purpose of this paper is to understand the pace of adoption of emerging technologies in the Indian mining industry and identify the challenges that managers confront while adopting emerging technologies.

The authors undertook qualitative research. Data collection was done in two stages. Secondary research was conducted to arrive at a repository of use cases of the adoption of emerging technologies in the global mining industry. Primary data collection was also done. The insights on emerging technology adoption and challenges faced in the Indian mining industry were captured by in-depth interviewing of subject matter experts. The authors interviewed 21 mining subject matter experts with a semi-structured open-ended questionnaire. The responses were content analyzed by thematic content analysis. Technological-organizational-environmental (TOE) and diffusion of innovation (DOI) frameworks were applied to segregate different factors affecting the adoption of emerging technologies in the Indian mining industry.

Emerging technologies such as blockchain, IoT, AI, ML, robotics has been applied across various mining engineering value chain activities such as in drilling, blasting, excavation and ore hauling. However, emerging technologies adoption was hindered because of a lack of managerial awareness, cultural inertia, substantive upfront investments and the nature of intangible benefits in the short run.

The research applied technology adoption frameworks in the mining industry. The authors used TOE and DOI frameworks to understand the challenges faced by Indian mining firms. The research findings, thus added to the conversation of TOE and DOI frameworks in the context of the Indian mining industry.

The research finding would help mining firm managers to anticipate the challenges with respect to technology adoption. This would allow mining executives to create a proper technology adoption plan and intervene proactively. The research would also provide information about the steps taken by competing firms with respect to emerging technologies adoption. The research would help managers to decide technology implementation steps in drilling, blasting, excavation and ore hauling to be undertaken for successful adoption of emerging technologies. Technology firms could gain insights into the issues faced by mining firms in adopting emerging technologies. This research would help managers to influence organizational technology policy and endorse the addition of pro-technology policies in mining activities. Policymakers involved in the mining sector could also incorporate industry-level policy decisions so as to facilitate the adoption of emerging technologies among mining firms and remove the barriers to the adoption of emerging technologies. This would create an opportunity for technology providers to redesign product offerings, which could be a good fit for Indian mining firms.

Indian mining industry contributed significantly to the Indian economy. Despite this, limited focus has been put regarding the adoption of emerging technologies in the mining industry. Mining managers did not have any framework to understand the challenges faced in the adoption of technologies across the mining value chain that is in drilling, blasting, excavation and ore hauling. This study focused on identifying those challenges through the use of technology adoption frameworks. This research was one of the first studies to gain insights on emerging technologies adoption in the context of the mining industry through the theoretical lens of TOE and DOI frameworks.