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Aggressive weeding in academic libraries is becoming more commonplace as colleges seek to create student-centered environments and space is at a premium. For one community college in the Southwest United States, several factors required the library to proactively weed its collection within three years. At the same time, the library sought to maintain the circulation of its physical books.

Updating the library’s collection development policy to include robust selection and weeding criteria allowed the library to embark on a revitalization project to remove thousands of outdated or unused items, resulting in a net loss of nearly 32,000 books.

The loss of more than half of the general collection had an unforeseen consequence – a 70% increase in circulation statistics during the three-year deselection project. The case study's results highlight the need for continual maintenance of academic library collections.

The case study is original and not published elsewhere.

In their study of reciprocity in investigative interviews, Matsumoto and Hwang (2018) found that offering interviewees water prior to the interview enhanced observer-rated rapport and positively affected information provision. This paper aims to examine whether tailoring the item towards an interviewee’s needs would further enhance information provision. This paper hypothesised that interviewees given a relevant item prior to the interview would disclose more information than interviewees given an irrelevant item or no item.

Participants (n = 85) ate pretzels to induce thirst, engaged in a cheating task with a confederate and were interviewed about their actions after receiving either no item, an irrelevant item to their induced thirst (pen and paper) or a relevant item (water).

This paper found that receiving a relevant item had a significant impact on information provision, with participants who received water providing the most details, and significantly more than participants that received no item.

The findings have implications for obtaining information during investigative interviews and demonstrate a need for research on the nuances of social reciprocity in investigative interviewing.

The findings have implications for obtaining information during investigative interviews and demonstrate a need for research on the nuances of social reciprocity in investigative interviewing.

To the best of the authors’ knowledge, this study is the first to experimentally test the effect of different item types upon information provision in investigative interviews.

Fashion companies have been among the first to ride the new trend and develop projects for the Metaverse, considering Generation Z (Gen Z) as a relevant target. The paper aims to investigate Gen Z consumers’ intention to use digital fashion items in the Metaverse.

The study relies on the technology acceptance model (TAM). The authors include specific aspects of the Metaverse: the user-avatar identification and the development of an alternative identity; fashion innovativeness is discussed as a moderator variable. The model is tested on Gen Z consumers, with 329 survey responses collected in 2022 and analyzed using structural equation modeling (SEM).

The paper shows that the two external and explanatory variables the authors added, i.e. user-avatar identification and alternative identity, positively and directly impact the individual attitude to use digital fashion items in the Metaverse. Moreover, according to the proposed research model, the moderating effect concerning fashion innovativeness has positive and negative consequences.

Using TAM, the authors explored consumers’ perceptions (perceived usefulness and ease of use), attitudes and intentions regarding the new technology context (digital fashion in the Metaverse). This study enriched TAM with new consumer marketing constructs (user-avatar identification and alternative identity) and their relationships with consumers’ intention to use digital fashion items in the Metaverse. This study also contributed to TAM by exploring the relevance of moderating the effects of consumer fashion innovativeness on consumers’ intentions and attitudes in the novel context of digital fashion in the Metaverse. The paper contributes to the academic debate by focusing on the individual and personal sphere of the consumer moving in the Metaverse digital environment. The marketing-focused study develops research on Gen Z consumers to provide new insights and possible opportunities for marketers in the Metaverse.

Given that knowledge hiding hampers the management of employee knowledge, it is important to measure the phenomena before applying the intervention to alleviate it. This paper aims to validate knowledge hiding measurements in South Korea.

The research collected 420 and 415 different Korean employee samples for each study, and they responded to their quality of knowledge hiding. The research conducted factor analysis using Mplus software and the Rasch model using JMetrik software based on the item response theory.

The research validated Korean versions of knowledge hiding measurements consisting of three factors and ten items. The study also found that knowledge hiding has a negative relationship with knowledge sharing and an unexpectedly positive relationship with team creativity. The study confirmed that the modified measurement yields acceptable discriminant and convergent validity.

The research relied on self-reported data and may have an issue measuring their knowledge hiding generously. Therefore, researchers are encouraged to measure it from others, including supervisors and colleagues. This research has theoretical implications for psychometrically and systematically validating the measurement.

The research includes practical implications for contributing to Human resource development practitioners could assess employee traits accurately and manage their negative knowledge behavior.

The research suggests the implications for detecting a positive relationship between knowledge hiding and team creativity. The study discussed that the specific climate could contribute to team creativity in Eastern contexts.

The research identified the importance of a psychometric validating process in the development of measurements.

In the recent few decades, there has been a rampant increase in the demand of sustainable food products around the world because of high cognizance of consumers toward environment and health. Asian countries, especially India and China, are also expecting a tremendous boost in the domestic demand for organic food products in the upcoming few years. Therefore, it becomes pertinent to explore the factors affecting the purchase intention behavior of organic food items, especially in emerging economies, i.e. India. Hence, the paper aims to explore the factors driving the purchase decision of organic consumers by collecting data set from 603 organic food item consumers in India.

The study has applied advanced statistical tools, i.e. structural equation modeling, Harman’s single factor test and other statistical measures, to analyze the collected research data.

The results posit that consumers’ purchase intention has a favorable impact on health aspects; trustworthiness; social innovativeness; functional value; subjective norms and organic product knowledge. Moreover, the result explicates that health consciousness and trustworthiness are vital predictors of organic food purchase intention.

The findings may assist the producers, processors, marketers, policymakers and regulators in devising appropriate policies and strategies for comprehending the complex phenomenon of organic consumers’ purchase behavior.

To the best of the authors’ knowledge, this is the first study to explore the drivers of purchase intention of organic food items by collecting data from well-defined consumers of organic food items in India.

The Multi-Stakeholder Recommendation System learns consumer and producer preferences to make fair and balanced recommendations. Exclusive consumer-focused studies have improved the recommendation accuracy but lack in addressing producers' priorities for promoting their diverse items to target consumers, resulting in minimal utility gain for producers. These techniques also neglect latent and implicit stakeholders' preferences across item categories. Hence, this study proposes a personalized diversity-based optimized multi-stakeholder recommendation system by developing the deep learning-based diversity personalization model and establishing the trade-off relationship among stakeholders.

The proposed methodology develops the deep autoencoder-based diversity personalization model to investigate the producers' latent interest in diversity. Next, this work builds the personalized diversity-based objective function by evaluating the diversity distribution of producers' preferences in different item categories. Next, this work builds the multi-stakeholder, multi-objective evolutionary algorithm to establish the accuracy-diversity trade-off among stakeholders.

The experimental and evaluation results over the Movie Lens 100K and 1M datasets demonstrate that the proposed models achieve the minimum average improvement of 40.81 and 32.67% over producers' utility and maximum improvement of 7.74 and 9.75% over the consumers' utility and successfully deliver the trade-off recommendations.

The proposed algorithm for measuring and personalizing producers' diversity-based preferences improves producers' exposure and reach to various users. Additionally, the trade-off recommendation solution generated by the proposed model ensures a balanced enhancement in both consumer and producer utilities.

The research enterprise within higher education is becoming more competitive as funding agencies require more collaborative research projects, higher-level of accountability and competition for limited resources. As a result, research analytics has emerged as a field, like many other areas within higher education to act as a data-informed unit to better understand how research institutions can effectively grow their research strategy. This is a new and emerging field within higher education.

As businesses and other industries are embracing recent advances in data technologies such as cloud computing and big data analytic tools to inform decision making, research administration in higher education is seeing a potential in incorporating advanced data analytics to improve day-to-day operations and strategic advancement in institutional research. This paper documents the development of a survey measuring research administrators’ perspectives on how higher education and other research institutions perceive the use of data and analytics within the research administration functions. The survey development process started with composing a literature review on recent developments in data analytics within the research administration in the higher education domain, from which major components of data analytics in research administration were conceptualized and identified. This was followed by an item matrix mapping the evidence from literature with corresponding, newly drafted survey items. After revising the initial survey based on suggestions from a panel of subject matter experts to review, a pilot study was conducted using the revised survey instrument and validated by employing the Rasch measurement analysis.

After revising the survey based on suggestions from the subject matter experts, a pilot study was conducted using the revised survey instrument. The resultant survey instrument consists of six dimensions and 36 survey items with an establishment of reasonable item fit, item separation and reliability. This survey protocol is useful for higher educational institutions to gauge research administrators’ perceptions of the culture of data analytics use in the workplace. Suggestions for future revisions and potential use of the survey were made.

Very limited scholarly work has been published on this topic. The use of data-informed and data-driven approaches with in research strategy within higher education is an emerging field of study and practice.

The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems. However, the existing algorithms rely mostly on common ratings of items and do not consider temporal information about items or user interests. To solve this problem, this study proposes a new user-item composite filtering (UICF) recommendation framework by leveraging temporal semantics.

The UICF framework fully utilizes the time information of item ratings for measuring the similarity of items and takes into account the short-term and long-term interest decay for computing users’ latest interest degrees. For an item to be probably recommended to a user, the interest degrees of the user on all the historically rated items are weighted by their similarities with the item to be recommended and then added up to predict the recommendation degree.

Comprehensive experiments on the MovieLens and KuaiRec datasets for user movie recommendation were conducted to evaluate the performance of the proposed UICF framework. Experimental results show that the UICF outperformed three well-known recommendation algorithms Item-Based Collaborative Filtering (IBCF), User-Based Collaborative Filtering (UBCF) and User-Popularity Composite Filtering (UPCF) in the root mean square error (RMSE), mean absolute error (MAE) and F1 metrics, especially yielding an average decrease of 11.9% in MAE.

A UICF recommendation framework is proposed that combines a time-aware item similarity model and a time-wise user interest degree model. It overcomes the limitations of common rating items and utilizes temporal information in item ratings and user interests effectively, resulting in more accurate and personalized recommendations.