Search results

Increasing personnel costs and labour shortages have pushed retailers to give increasing attention to their intralogistics operations. We study hybrid order picking systems, in which humans and robots share work time, workspace and objectives and are in permanent contact. This necessitates a collaboration of humans and their mechanical coworkers (cobots).

Through a longitudinal case study on individual-level technology adaption, we accompanied a pilot testing of an industrial truck that automatically follows order pickers in their travel direction. Grounded on empirical field research and a unique large-scale data set comprising N = 2,086,260 storage location visits, where N = 57,239 storage location visits were performed in a hybrid setting and N = 2,029,021 in a manual setting, we applied a multilevel model to estimate the impact of this cobot settings on task performance.

We show that cobot settings can reduce the time required for picking tasks by as much as 33.57%. Furthermore, practical factors such as product weight, pick density and travel distance mitigate this effect, suggesting that cobots are especially beneficial for short-distance orders.

Given that the literature on hybrid order picking systems has primarily applied simulation approaches, the study is among the first to provide empirical evidence from a real-world setting. The results are discussed from the perspective of Industry 5.0 and can prevent managers from making investment decisions into ineffective robotic technology.

Integrating transaction costs economics and task-technology fit theory, this study distinguishes two categories of social media–enabled interactions, namely task-related interactions and tie-related interactions, and explores the match between these two and firms' use of contracts in achieving safeguarding and coordinating purposes in interfirm governance.

Two studies were conducted to test the hypotheses. In Study 1, this study collaborated with a professional market research firm and collected responses from Chinese manufacturing firms in a survey. In Study 2, this study designed a scenario-based experiment and collected 239 participants from the Credamo platform.

This study categorized social media–enabled interactions into task-related interactions and tie-related interactions and conducted two studies to reveal that the safeguarding purpose of contract specificity is amplified by tie-related interactions, whereas the coordinating purpose of contract specificity is strengthened by task-related interactions.

This study assumes that firms permit and encourage the use of social media. However, some firms might prohibit the use of social media due to risk issues, or their partners may be prohibited from using social media.

Given that social media–enabled interactions have joint effects with contracts in achieving safeguarding and coordinating purposes, a firm's employees should match their goals with an appropriate type of social media–enabled interactions.

This study enriches the interfirm governance literature by uncovering the roles of these two types of interactions in matching contract specificity to achieve safeguarding and coordinating purposes, which provides actionable insights for managers in governing interfirm relationships.

Traditional library book recommendation methods are mainly based on association rules and user profiles. They may help to learn about students' interest in different types of books, e.g., students majoring in science and engineering tend to pay more attention to computer books. Nevertheless, most of them still need to identify users' interests accurately. To solve the problem, the authors propose a novel embedding-driven model called InFo, which refers to users' intrinsic interests and academic preferences to provide personalized library book recommendations.

The authors analyze the characteristics and challenges in real library book recommendations and then propose a method considering feature interactions. Specifically, the authors leverage the attention unit to extract students' preferences for different categories of books from their borrowing history, after which we feed the unit into the Factorization Machine with other context-aware features to learn students' hybrid interests. The authors employ a convolution neural network to extract high-order correlations among feature maps which are obtained by the outer product between feature embeddings.

The authors evaluate the model by conducting experiments on a real-world dataset in one university. The results show that the model outperforms other state-of-the-art methods in terms of two metrics called Recall and NDCG.

It requires a specific data size to prevent overfitting during model training, and the proposed method may face the user/item cold-start challenge.

The embedding-driven book recommendation model could be applied in real libraries to provide valuable recommendations based on readers' preferences.

The proposed method is a practical embedding-driven model that accurately captures diverse user preferences.

This study aims to analyse the differences between professional interaction mediated by video conferencing and direct professional interaction. The research identifies diverging interests of office workers for the purpose of addressing work ethical and business ethical issues of professional collaboration, competition, and power in future hybrid work models.

Based on 28 qualitative interviews conducted between November 2020 and June 2021, and through the theoretical lens of phenomenology, the study develops explanatory hypotheses conceptualising four basic intentions of professional interaction and their corresponding preferences for video conferences and working on site.

The four intentions developed on the basis of the interviews are: the need for physical proximity; the challenge of collective creativity; the will to influence; and control of communication. This conceptual framework qualifies a moral ambivalence of professional interaction. The authors identify a connectivity paradox of professional interaction where the personal dimension remains unarticulated for the purpose of maintaining professionality. This tacit human connectivity is intertwined with latent power relations. This plasticity of both connectivity and power in direct interaction can be diminished by transferring the interaction to video conferencing.

The application of phenomenology to a collection of qualitative interviews has enabled the identification of underlying intention structures and the system in which they affect each other. This research identifies conflicts of interests between workers relative to their different self-perceived abilities to persevere in competitive professional interaction. It is therefore able to address consequences of future hybrid work models at an existential and societal level.

The purpose of the present research is to address the issue by conceptualizing artificial intelligence (AI) experience quality and its dimensions, and furthermore, to empirically test the relationships among AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention.

The data were collected from an AI community canteen in Shanghai. They were also analyzed using exploratory and confirmatory factor analyses (EFA and CFA) and structural equation modeling (SEM).

Four primary dimensions and 15 sub-dimensions of AI experience quality for community canteens were identified. The hypothesized paths between the higher-order constructs – AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention – were confirmed as well.

This is the first study to synthesize AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention in an AI restaurant setting.

People's socializing behavior in social networking services (SNS) presents dramatically different features, forming differentiated online social interaction patterns (DOSIP) in SNS. This study aims to explore the relationships between users' multidimensional psychological needs and multiple social interaction patterns in SNS.

Based on Maslow's hierarchy of needs and use and gratifications (U&G) theory, the authors develop the research model to examine the effects of psychological needs on DOSIP. A survey is used to collect the data of SNS users' social interaction. The authors adopt structural equation modeling–neural network (SEM-NN) integrated method to examine the research model.

Need to belong, need for self-esteem, need for social contact, need for emotional expression, need for cognition, and need for external-esteem have significant influences on both active and passive social interactions respectively.

Based on the categorization of DOSIP into six types in terms of the level of activity and disclosure of social interaction, the authors construct an integrated research model of multidimensional psychological needs to multiple social interaction patterns, and validate the antecedents of DOSIP from the perspective of psychological needs.

The main purpose of the study is to reveal the hotel managers' perspectives strategies, and predictions on smart technologies, and their expectations for current staff and potential staff to be employed in the future.

A qualitative research approach was used to reveal the internal perspectives of managers to determine their Smart Tourism (ST) perspectives. With the snowball sampling method, face-to-face interviews were conducted with 16 senior hotel managers working in a 5-star hotel in Antalya. The data obtained were carried out with inductive content analysis and descriptive analysis from qualitative research analysis methods.

In this study, hotel managers' broad perspectives on ST were discussed in depth and presented comprehensively. Managers' perceptions of smart technologies are classified in terms of aggregate dimensions and themes. Among the most important findings, rapidity/quickness, managing/holistic assessment, standardization, harmony/integration-coordination and experience-memory, defined as the crossroads of total dimensions or the heart of smart technologies, were identified as the most important themes. In addition, Stylos et al. (2021), another important finding is the classification of technologies used in top-quality hotels, based on the conceptual framework for smart technologies presented in the literature.

The effective and efficient use of technology, its internalization and openness to technology provides important advantages in hotels such as increasing revisits and satisfaction, providing loyalty and reducing costs. Thus, the perceptions, practices, strategies and prediction of senior managers working in high-level hotels about ST provide valuable data to other hotels that want to survive in the competition.

From a theoretical point of view, this study is valuable in that it deals with the ST perceptions of senior managers with an exploratory research approach. Managerially, the research findings offer valuable contribution about the attitudes, trends, forecasts and expectations of senior managers working in high-level hotels towards ST.

Product discounts have been widely applied in digital commerce as a method to attract and retain customers to purchase in China. Given that digital channels differ in their attributes, customers may behave differently when they respond to the same discount across channels. However, little attention has been paid to explore the heterogeneity of customer responses to discounts across channels. This study aims to fill in the gap by exploring how customers’ purchase responses to price discounts differ across digital channels.

This study applies a Poisson regression to an unbalanced data set of purchasing history from Chinese footwear brands with 3,510 customers in a two-year time window across the three digital channels (i.e. personal computer [PC], app and mobile website), with a correction for endogeneity by using the Gaussian copula method.

This paper finds that price discounts have the strongest positive effect on consumers’ purchase volumes on the PC channel, followed by the app channel, while discounts show the weakest impact in the mobile website channel. By so, this paper demonstrates that customers respond differently to online and mobile channels, and they also respond differently within mobile channels when they purchase products with price discounts.

This study is original in analyzing the difference in customers’ discount responses across digital channels, offering valuable contributions to existing research on multichannel marketing as well as mobile marketing and providing helpful insights for multichannel merchants to design digital discount strategies.

Consumers are increasingly spending more time on social media platforms to cope with anxiety and loneliness resulting from the recent pandemic. The extraordinary times, characterised by isolation and lockdown, has placed increasing dependence on online technology as a coping mechanism in which social media influencers (SMIs) served as the human face of brands, helping both consumers and brands maintain human connection. How should practitioners navigate their social media campaigns in this changing environment?

To answer this question, the Source Connectedness Pyramid is proposed to help explain and compare the interactions between consumers and SMIs in ordinary and extraordinary times.

In their interactions with influencers during ordinary times, consumers are satiated with influencer source characteristics of attractiveness, trustworthiness and expertise. However, during extraordinary times, consumers substitute their usual preference to focus on connectedness, characterised by relatedness, belongingness and attachment.

The empirical study within this paper lends support to this proposition and offers additional insights. The proposed Source Connectedness Pyramid contributes to influencer communication theoretically and has strategic implications for practitioners when navigating their social media campaigns in these extraordinary times.

The mega railway infrastructure projects are faced with complex environments and multi-level management challenges. Thus, the mega railway infrastructure project management system not only needs to focus on its composition, but also needs to consider changes and impacts of internal and external environment.

This study attempts to introduce the concept of dissipative structure from the perspective of complexity theory and constructs a positive entropy and negentropy flow index system for mega railway infrastructure project management system in order to analyze the factors of management system more deeply. The Brusselator model is used to construct the structure of the mega railway infrastructure project management system, and the entropy method is used to calculate the positive entropy and negentropy values to verify whether the management system is a dissipative structure.

A plateau railway project in China was used as an example for an empirical study, not only its own characteristics are analyzed, but also the role of constraints and facilitation of the internal and external environment. Based on the research results, several effective suggestions are put forward to improve the stability and work efficiency of mega railway infrastructure project management system.

This study demonstrates that mega railway infrastructure project management system has the characteristics of dissipative structure. It can provide theoretical support for the development of mega railway infrastructure project management system from disorderly state to orderly state.