Aslib Journal of Information Management最新文献

Purpose

Developing green innovative services is critical to the restaurant industry to achieve significant benefits as well as environmental sustainability. This study aims to explore the mechanisms through which employees’ green involvement can foster green service innovation behavior.

Design/methodology/approach

The data set garnered from employees who worked in restaurants was used to test these mechanisms. A partial least square technique was conducted on this data set.

Findings

The results revealed the employees’ green involvement significantly influences their green service innovation intention, which subsequently influences their green service innovation behavior. Furthermore, information technology (IT) adoption was found to fortify the linkage of employee green involvement with green service innovation intention.

Practical implications

The results suggest to the restaurant industry that awareness of green service innovation and IT adoption practices can help restaurants to develop effective sustainability work practices and meet societal expectations.

Originality/value

This study extends the restaurant management literature by linking the green involvement of restaurant employees to green service innovation intention as well as identifying the moderating role of IT adoption underlying this link.

Purpose

This paper aims to explore the determinants of maternal and infant health knowledge (M&IHK) adoption and sharing in the short video from an empathy theory perspective. We explore how to transfer users from free health knowledge to health-related product purchase intention, which is vital for platform knowledge management and service.

Design/methodology/approach

Focusing on the M&IHK, this study proposes four processes of health knowledge adoption and sharing – knowledge quality persuasion process; source credibility persuasion process; affective empathy emotion process; and cognitive empathy emotion process – to build a framework of M&IHK adoption and sharing. Furthermore, based on adoption and sharing, we explore whether they can promote health-related product purchase intentions. A theoretical model is constructed and tested via Smart PLS in 388 samples.

Findings

In a short video context, perceived knowledge quality and perceived source credibility are still two determinants of health knowledge adoption and sharing. On the contrary, perceived affective empathy and perceived cognitive empathy are two new determinants of health knowledge adoption, but not of health knowledge sharing. Adoption of M&IHK is more driven by both rational thinking and emotional thinking than sharing-only driven by emotional thinking. Adoption and sharing both contribute to health-related product purchase intention, but the female’s intention is more related to rational adoption than the male, which is only related to emotional sharing.

Originality/value

This paper is arguably the first study to examine how short videos impact the mechanisms of M&IHK adoption, sharing and health-related products' purchase intention. It’s perhaps the first study to integrate empathy theory into health knowledge management.

Purpose

People with pre-existing mental health conditions are more likely to be affected by global crises. The Covid-19 pandemic has presented them with unique challenges, including reduced contact with the psychiatric rehabilitation and support systems. Thus, understanding the emotional experience of this population may assist mental health organizations in future global crises.

Design/methodology/approach

In this paper, researchers analyzed the discourse of the mentally ill during the Covid-19 pandemic, as reflected in Israeli Facebook groups: three private groups and one public group. Researchers explored the language, reactions, emotions and sentiments used in these groups during the year before the pandemic, outbreak periods and remission periods, as well as the period before the vaccine’s introduction and after its appearance.

Findings

Analyzing groups’ discourse using the collective emotion theory suggests that the group that expressed the most significant difficulty was the Depression group, while individuals who suffer from social phobia/anxiety and PTSD were less affected during the lockdowns and restrictions forced by the outbreak.

Originality/value

Findings may serve as a tool for service providers during crises to monitor patients’ conditions, and assist individuals who need support and help.

Purpose

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

Design/methodology/approach

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

Findings

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

Originality/value

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

Purpose

The higher-level aim of this study is to investigate the impact of health information needs satisfaction on the fear of COVID-19 for the general population. The investigation is theoretically grounded on Wilsons’ model of information seeking in the context of inquesting the reasons for seeking health information as well as the information sources the general population deploy during the COVID-19 pandemic.

Design/methodology/approach

This cross-sectional survey examines the correlations between health information seeking behavior and the COVID-19 generated fear in the general population through the application of a specially designed structured questionnaire which was distributed online. The questionnaire comprised four main distinct research dimensions (i.e. information needs, information sources, obstacles when seeking information and COVID-19 generated fear) that present significant validity levels.

Findings

Individuals were motivated to seek COVID-related health information to cope with the pandemic generated uncertainty. Information needs satisfaction as well as digital health literacy levels is associated with the COVID-19 generated fear in the general population. Finally, a conceptual framework based on Wilsons’ macro-model for information seeking behavior was developed to illustrate information needs satisfaction during the pandemic period. These results indicate the need for incentives to enhance health information needs satisfaction appropriately.

Originality/value

The COVID-19 generated fear in the general population is studied through the information seeking behavior lenses. A well-studied theoretical model for information seeking behavior is adopted for health-related information seeking during pandemic. Finally, digital health information literacy levels are also associated with the fear of COVID-19 reported in the authors’ survey.

Purpose

This research aims to explore the research data ethics governance framework and collaborative network to optimize research data ethics governance practices, to balance the relationship between data development and utilization, open sharing, data security and to reduce the ethical risks that may arise from data sharing and utilization.

Design/methodology/approach

This study explores the framework and collaborative network of research data ethics policies by using the UK as an example. 78 policies from the UK government, university, research institution, funding agency, publisher, database, library and third-party organization are obtained. Adopting grounded theory (GT) and social network analysis (SNA), Nvivo12 is used to analyze these samples and summarize the research data ethics governance framework. Ucinet and Netdraw are used to reveal collaborative networks in policy.

Findings

Results indicate that the framework covers governance context, subject and measure. The content of governance context contains context description and data ethics issues analysis. Governance subject consists of defining subjects and facilitating their collaboration. Governance measure includes governance guidance and ethics governance initiatives in the data lifecycle. The collaborative network indicates that research institution plays a central role in ethics governance. The core of the governance content are ethics governance initiatives, governance guidance and governance context description.

Research limitations/implications

This research provides new insights for policy analysis by combining GT and SNA methods. Research data ethics and its governance are conceptualized to complete data governance and research ethics theory.

Practical implications

A research data ethics governance framework and collaborative network are revealed, and actionable guidance for addressing essential aspects of research data ethics and multiple subjects to confer their functions in collaborative governance is provided.

Originality/value

This study analyzes policy text using qualitative and quantitative methods, ensuring fine-grained content profiling and improving policy research. A typical research data ethics governance framework is revealed. Various stakeholders' roles and priorities in collaborative governance are explored. These contribute to improving governance policies and governance levels in both theory and practice.

Purpose

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Design/methodology/approach

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

Findings

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

Originality/value

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

Purpose

The landscape of search engine usage has evolved since the last known data were used to calculate click-through rate (CTR) values. The objective was to provide a replicable method for accessing data from the Google search engine using programmatic access and calculating CTR values from the retrieved data to show how the CTRs have changed since the last studies were published.

Design/methodology/approach

In this study, the authors present the estimated CTR values in organic search results based on actual clicks and impressions data, and establish a protocol for collecting this data using Google programmatic access. For this study, the authors collected data on 416,386 clicks, 31,648,226 impressions and 8,861,416 daily queries.

Findings

The results show that CTRs have decreased from previously reported values in both academic research and industry benchmarks. The estimates indicate that the top-ranked result in Google's organic search results features a CTR of 9.28%, followed by 5.82 and 3.11% for positions two and three, respectively. The authors also demonstrate that CTRs vary across various types of devices. On desktop devices, the CTR decreases steadily with each lower ranking position. On smartphones, the CTR starts high but decreases rapidly, with an unprecedented increase from position 13 onwards. Tablets have the lowest and most variable CTR values.

Practical implications

The theoretical implications include the generation of a current dataset on search engine results and user behavior, made available to the research community, creation of a unique methodology for generating new datasets and presenting the updated information on CTR trends. The managerial implications include the establishment of the need for businesses to focus on optimizing other forms of Google search results in addition to organic text results, and the possibility of application of this study's methodology to determine CTRs for their own websites.

Originality/value

This study provides a novel method to access real CTR data and estimates current CTRs for top organic Google search results, categorized by device.

Purpose

This study presents an innovative approach to analyzing user behavior when performing digital tasks by integrating eye-tracking technology. Through the measurement of user scan patterns, gaze and attention during task completion, the authors gain valuable insights into users' approaches and execution of these tasks.

Design/methodology/approach

In this research, the authors conducted an observational study that centered on assessing the digital skills of individuals with limited proficiency who enrolled in a computer introductory course. A group of 19 participants were tasked with completing various online assignments both before and after completing the course.

Findings

The study findings indicate a significant improvement in participants' skills, particularly in basic and straightforward applications. However, advancements in more sophisticated utilization, such as mastering efficient search techniques or harnessing the Internet for enhanced situational awareness, demonstrate only marginal enhancement.

Originality/value

In recent decades, extensive research has been conducted on the issue of digital inequality, given its significant societal implications. This paper introduces a novel tool designed to analyze digital inequalities and subsequently employs it to evaluate the effectiveness of “LEHAVA,” the largest government-sponsored program aimed at mitigating these disparities in Israel.