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This study examined the antecedents of technostressors as well as how customers' perceived convenience and technostressors of using a check-in/out kiosk influence their behavioral intention in a full-service hotel setting.

Using survey data collected from 630 hotel customers, hypotheses were tested via structural equation modeling and multi-group analysis.

The results showed that perceived usefulness of a check-in/out kiosk had direct effects on both technostressors (i.e. work overload and role ambiguity), and that perceived ease-of-use had indirect effects on the technostressors, via perceived usefulness. The findings showed that both role ambiguity and perceived convenience significantly influenced intention to use a check-in/out kiosk. Intention to use was positively associated with intention to revisit a hotel providing the kiosk. These findings were equivalent across the younger and older groups.

Based on the findings, hotels can implement effective strategies to reduce technostressors associated with a check-in/out kiosk and focus on enhancing the factors that influence customer acceptance of the system. This is especially important given the increased emphasis on self-service technology since the onset of the COVID-19 pandemic.

This research contributed to the relevant literature by developing a check-in/out kiosk acceptance model using a multi-theoretical approach, and empirically testing it within the full-service hotel domain. It fills the knowledge gap regarding the antecedents and outcomes of technostressors in the hospitality research literature by providing empirical evidence.

The purpose of this paper is to present an integrative approach for the problem of service capacity design for the ground crew at the airport check-in counters.

Using data collected from the airport check-in counters, several characteristics of the service demand were studied. First, many service time-dependent operating characteristics are addressed over a specified time window to evaluate the problems from the current ground crew allocation. Second, a linear programming model is developed to determine the optimal number of check-in counters to open over the specified time window based on the case study. In addition, sensitive analysis is conducted to further explore the dependency effect of the factors as well as improve the model. Third, the shift adjustment of the ground crew allocation is provided so as to satisfy the given service demand.

Numerical results indicated the effectiveness in the improvement of the human resource utilization of the proposed approach. The superiority of performance is illustrated in terms of less counters to be opened and decreased daily working hours.

The study is novel by applying the integrated approach so as to design a proper service capacity and thus the service supply and demand can be balanced.

Check-in based advertising is growing dramatically as the popularity of social media increases. The purpose of this paper is to explore which social cues are appropriate for check-in based advertising in social media based on media richness theory and how content effectiveness affects content generation intention based on achievement motivation theory.

A laboratory experiment was performed to evaluate the effects of social cue strategies on content effectiveness of attitude toward the ad and self-efficacy on recall. The influence of effectiveness on content generation intention are also measured in the experiment.

The results of a laboratory experiment indicated that a strategy of using plentiful social cues has high effectiveness as measured by the concept of attitude toward the ad. Content effectiveness measured by attitude toward the ad and self-efficacy on recall can directly affect user intentions to generating check-in based advertisements.

Although check-in based advertising is driven by the customers themselves rather than by the company, companies can encourage their customers to follow an appropriate check-in content generation strategy to improve effectiveness.

The findings of this study provide useful information for designing the content of social media designed to facilitate the promotion of products and companies in online marketing.

In theoretical contribution, this study integrates media richness theory and achievement motivation theory to explore how users intent to generate check-in advertising according to social cues effectiveness.

This paper aims to propose several characteristics of check-in services and investigates whether these characteristics influence customer perceived utilitarian and hedonic values. These perceived values mediate the influences of check-in services on customers' responses.

From the perspective of mobile check-in service, the authors proposed a framework with several hypotheses and verified the model with structural equation modeling based on questionnaire data collected from mobile device users.

The results indicate that self-disclosure, expressive support and self-congruity positively relate to utilitarian value; instrumental support positively relates to hedonic value and both hedonic and utilitarian values positively relate to continuance intention and re-patronage intention. However, sales promotion has no significant influence on both hedonic and utilitarian check-in values.

This study applies the concept of location-based social networks to provide new insights into the evolving user-generated content research in the interactive marketing field.

The purpose of this paper is to explore the emerging motives behind check-in and location sharing of consumers on social network sites (SNSs). A theoretical model for the emergent motives of check-in behavior is proposed, and implications of location sharing for consumer behavior and marketing are discussed.

Data were collected from a convenience sample of 32 respondents, comprising a representative sample of location-based SNS users. In total, 217 unique check-ins, locations, and motives were analyzed, and seven emergent motives were identified through content analysis.

This paper identified the key motives behind check-in behavior, including social-enhancement value, informational and social motivation, entertainment value, gameful experiences, utilitarian motivation, and belongingness. Social-enhancement value is the most frequently cited motive, driven mainly by the selective self-presentation efforts of respondents. Gameful experiences are a newly emerging motive contributing to the “uses and gratifications theory” (UGT).

The proposed model has not yet been formally verified in a quantitative study. A further study with a larger sample size is planned for future work for the verification and generalization of the findings.

Practitioner approaches on check-in behavior are mainly focussed on consumers giving and receiving recommendations of locations and consumers responding to the promotional efforts of marketers. However, this study suggests consumers have more creative and different motives, such as dating through location-based SNSs or gameful experiences.

Research into location-based SNSs is a relatively new area in marketing. This study and proposed model are among the first to explain check-in behavior. Additionally, gameful experiences contribute to UGT, a construct that has not been previously identified.

Provides an outline for implementing a defensive marketing strategy. Advocates a procedure which combines aggregate complaint analysis and a quality function development tool known as the House of Quality. The combination of these procedures allows marketers to identify common customer complaints and to assess the synergy between different combinations of service attributes. Then measures customer perception of service attribute combinations for changes in satisfaction level. Argues that the development of a complaint management program can positively contribute to the firm′s competitive advantage.

In the last issue of VINE we had a look at the serials control package — ISIS from Blackwell's. As a contrast we now take a look at a bespoke serials check‐in system developed at The British Library Document Supply Centre (BLDSC) at Boston Spa. The BLDSC has somewhat unique requirements from a serials check‐in system. The library currently subscribes to 56,000 journal titles and receives approximately 350,000 issues each year arriving at a rate of 1,500 issues per day. These are handled in the Accessions Department by a team of 8 who aim to have the issues checked in fast enough to be available on the shelves by 4.30pm each day. To help to achieve this aim SACHET (Serials Accessions CHEck‐in of Titles) was devised and developed in‐house by the BLDSC Computing Department.

Tourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order to predict a set of locations that a user may soon visit.

The authors proposed a novel learning-based method, the pattern-based dual learning POI recommendation system as a solution to consider users' interests and the uniformity of popular POI patterns when making recommendations. Differing from traditional long short-term memory (LSTM), a new users’ regularity–POIs’ popularity patterns long short-term memory (UP-LSTM) model was developed to concurrently combine the behaviors of a specific user and common users.

The authors introduced the concept of dual learning for POI recommendation. Several performance evaluations were conducted on real-life mobility data sets to demonstrate the effectiveness and practicability of POI recommendations. The metrics such as hit rate, precision, recall and F-measure were used to measure the capability of ranking and precise prediction of the proposed model over all baselines. The experimental results indicated that the proposed UP-LSTM model consistently outperformed the state-of-the-art models in all metrics by a large margin.

This study contributes to the existing literature by incorporating a novel pattern–based technique to analyze how the popularity of POIs affects the next move of a particular user. Also, the authors have proposed an effective fusing scheme to boost the prediction performance in the proposed UP-LSTM model. The experimental results and discussions indicate that the combination of the user's regularity and the POIs’ popularity patterns in PDLRec could significantly enhance the performance of POI recommendation.