Search results

As voice search has progressively become a new way of information acquisition and human–computer interaction, this paper aims to explore the users' voice search behavior in human–vehicle interaction.

This study employed mixed research methods, including questionnaires and interviews. A total of 151 Amazon MTurk volunteers were recruited to complete a questionnaire based on their most recent and impressive voice search experience. After the questionnaire, this paper conducted an online interview with the participants.

This paper studied users' voice search behavior characteristics in the context of the human–vehicle interaction and analyzed the voice search content, search need, search motivation and user satisfaction. In addition, this paper studied the barriers and suggestions for voice search in human–vehicle interaction through a content analysis of the interviews.

This paper's analysis of users' barriers and suggestions has a specific reference value for optimizing the voice search interaction system and improving the service.

This study is exploratory research that seeks to identify users' voice search needs and tasks and investigate voice search satisfaction in human–vehicle interaction context.

The study focused on online public access catalog (OPAC) users’ cross-device search behavior. The purpose of this paper is to understand the characteristics of cross-device OPAC searches, and to identify query reformulation (QR) patterns during device transitions.

The transaction log from a university library, spanning six months, was used to conduct the quantitative analysis. The query vocabulary richness, which refers to the average number of unique words each query contains in a search session, can evaluate query diversity, and contribute to the analysis of QR.

The results show that PC-PC transition is the most important pattern of device transition. The time interval of device transition was different to the time interval of transitions in web searches. Short device transitions mainly occurred in daytime, and the number of transitions that occurred in less than one minute was higher than on the web. Searches for Industry and Technology triggered the most device transitions, and the users tended to choose the same search field. In addition, the authors made a detailed analysis of the reasons for same-type device transitions and different-type device transitions. Furthermore, the authors focused on the characteristics of adjacent QR patterns. The authors not only refined the concept of cross-device to include the same-type device transition, but also summarized the characteristics of the cross-device QR patterns, which can be used to predict post-switch queries.

This study extends research into cross-device interaction and cross-device search to the domain of digital library research. The authors also introduced QR perspective on cross-device interaction on OPAC.

This paper aims to explore the users' cross-app behavior characteristics in mobile search and to predict users' cross-app behavior using multi-dimensional information.

This paper presents a longitudinal user experiment in 15 days. This paper recruited 30 participants and collected their mobile phone log data in the whole experiment. The structured diary method was also used to collect contextual information in mobile search.

This study focused on the users' cross-app behavior in mobile search and described cross-app behavior's basic characteristics. Usage of communication app and tool apps could trigger more cross-app behavior in mobile search. The method of cross-app behavior prediction in the mobile search was proposed. Collecting users' more contextual information, such as search tasks, search motivation and other environmental information, can effectively improve the prediction accuracy of cross-app behavior in mobile search.

The future research on cross-app behavior prediction should focus on context information in mobile search. Better prediction of cross-app behavior can reduce the users' interaction burden.

This paper contributes to research into cross-app behavior, especially in the mobile search research domain.

Existing literature has elicited the external behaviors of cross-device search but not much is known about users' cognition of cross-device search. The study aims to explore how users perceive the cross-device search by combining with the mental models and how motivations affect the users' understanding of cross-device search.

The research questions are addressed through semi-structured face-to-face interviews with 59 users. Prior to the interview, the user is asked to complete a cross-device search task designed with the simulation of controlled and autonomous motivations to gain a real experience. The concepts of mental models are coded according to the constructivist grounded theory method.

The study finds the users' mental models of cross-device search consist of four dimensions: Element, Quality, Function and Issue. The effect of motivation on the mental models is tested as significant in terms of the Quality and Function aspects. The controlled motivation affects the user's perception of how the device switch influence the search and the autonomous motivation influences the user's opinion of search system functions.

The contribution of this study is found to extend the existing knowledge of cross-device search and update the mental models of information search in the current multi-device environment. The findings inform the future study of cross-device search and practices of search system design.

The Internet of Things (IoT), which enables smart objects to collect and exchange data, has a variety of application domains used in everyday life including healthcare. As a set of promising next-generation technologies in the healthcare domain, Healthcare-related Internet of Things (H-IoT) promises to facilitate better healthcare by offering data-driven insights. While effective in practice at large, emerging data concerns arise because of the inscrutable black-box systems. Inspired by the notion of human data interaction, this paper seeks to understand how people engage with the H-IoT data that is about and produced by themselves and to elucidate the main data issues and challenges involved in the development of H-IoT.

This work conducted a comprehensive survey and integrated the method of content analysis by systematically review the recently published H-IoT research work in the healthcare domain.

This study thoroughly surveyed more than 300 research studies published in the last decades and classified seven H-IoT end-user groups, and three H-IoT data types that are important to H-IoT comprehension. Attention to human data interaction, our study also highlights several critical issues associated with this notion in the context of H-IoT.

This study will support H-IoT research by characterizing the data issues and challenges exist in the context of H-IoT user and data interaction. The findings will provide insights in designing for effective interactions with data in the H-IoT.

Not all apps can be favored on a smartphone. Users are bound to abandon some if they lose value. Analyzing the reasons behind this phenomenon can help develop strategies for avoiding app abandonment, which is the focus of this study.

This study conducted in-depth open interviews with 47 respondents who had abandoned or deleted mobile apps before and used the qualitative research method grounded theory to analyze the interview content, followed by open coding, axial coding and selective coding.

Seven main reasons are summarized after the relationship between every statement and concept is found and constructed, namely functional value, information expectation, emotional value, platform service, user trust, cost, and social influence.

Based on these findings, this study provided suggestions for mobile app developers to help them effectively improve the user experience and retain their customers.

The purpose of this paper is to explore users’ learning in the collaborative information search process when they conduct an academic task as a group.

This paper presents a longitudinal study for a three-month period on an actual task. The participants, who were undergraduate students, needed to write a research proposal in three months to apply for funding for a research project, including a three-hour experiment.

The results show that undergraduates’ learning in the collaborative search process for academic group work included knowledge reconstruction, tuning, and assimilation. Their understanding of the topic concepts improved through the process, and their attitudes became more optimistic. Besides, the learning in the collaborative information search process also enhanced participants’ skills in communication, research, information search, and collaboration. To improve learning outcomes, professional and appropriate academic resources are required, as well as effective division of labor, positive sharing behaviors, and use of collaborative systems.

The future development of collaborative information search systems should focus on the needs of academic research and support for elements such as instant communication and knowledge sharing.

This paper contributes to research into searching as learning by understanding undergraduates’ collaborative search behavior for writing a proposal.

This study aims to develop a cross-level research model to explore the relationship between team-level contextual ambidexterity and employees' enterprise system (ES) ambidextrous use, and the mediating role of user empowerment in and moderating effect of leader–member exchange (LMX) on the relationship.

This study conducted a sequential mixed-methods approach, which included a quantitative survey and a qualitative case study. The survey, administered to 244 employees in 59 groups from a financial institution, analyzed the relationships between contextual ambidexterity and ES ambidextrous use. Furthermore, the cross-level mediation and moderation effects were explored. The case study, involving nine members in three groups from a manufacturing firm, served to reinforce the validity of the survey results.

Team-level contextual ambidexterity can affect ES ambidextrous use directly or through the partial mediator of user empowerment. Furthermore, this study highlights the moderating role of LMX in the relationship between contextual ambidexterity and user empowerment, thereby improving ES ambidextrous use.

This study contributes to the literature by uncovering the cross-level effect of contextual ambidexterity on ES ambidextrous use through user empowerment, thereby extending the ambidexterity perspective and self-determination theory to the ES context. Additionally, this study provides nuanced insights into how to enhance ES ambidextrous use by revealing the moderating role and moderated mediation effect of LMX anchoring on social exchange theory.

Based on uncertainty reduction theory, this study explores how perceived information transparency mitigates users' perceived uncertainty, which in turn influences their actual purchase behavior. In addition, the moderating effects of cultural tightness on the relationship between perceived information transparency and perceived uncertainty are also considered.

Users with the shopping experience on Xiaohongshu are invited to participate in the survey. Finally, 355 valid longitudinal data are collected.

The results indicate that the three dimensions of perceived information transparency (i.e. perceived product transparency, perceived seller transparency and perceived transaction transparency) can reduce users' perceived uncertainty significantly. Besides, the negative impacts of perceived product and seller transparency on users' perceived uncertainty are stronger when cultural tightness is higher. However, cultural tightness does not moderate the relationship between perceived transaction transparency and users' perceived uncertainty.

First, the authors' research extends the uncertainty reduction theory to the context of social commerce. Second, the authors' research explores the boundary condition under which perceived information transparency varies by identifying cultural tightness as the moderator of the relationship between perceived information transparency and uncertainty. Third, the authors' research enriches the understanding of the cultural tightness of China.

The success of social commerce depends on the actual transactions of consumers, which will be prevented in the presence of high uncertainty. However, attention paid to the uncertainty reduction strategies in social commerce is limited, especially from a unified theoretical framework. Based on uncertainty reduction theory (URT), this paper aims to investigate how three uncertainty reduction strategies (i.e. situational normality, perceived effectiveness of social commerce institutional mechanisms (PESIM) and swift guanxi) affect perceived uncertainty in social commerce, which in turn affects buyers' purchase intention and purchase behavior. The moderating effects of PESIM on the relationships between the other two strategies and perceived uncertainty were also tested.

In this study longitudinal data from 211 buyers who have usage experience of Xiaohongshu were collected to test the proposed model and hypotheses.

Results show that the three uncertainty reduction strategies significantly reduce perceived uncertainty. PESIM negatively moderates the relationships between situational normality and perceived uncertainty, swift guanxi and perceived uncertainty. Perceived uncertainty is negatively related to purchase intention. Purchase intention positively affects purchase behavior.

This study focuses on the role of uncertainty reduction mechanisms in promoting purchase behavior through uncertainty reduction and sheds light on the relationships among situational normality, PESIM, swift guanxi and perceived uncertainty based on URT, which have not been extensively studied from a theoretical perspective in social commerce contexts. Besides, this study investigates the moderating role of PESIM, which improves the understanding of the role of swift guanxi and situational normality in reducing perceived uncertainty under the boundary condition of PESIM.