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In light of the rapidly evolving digital landscape, there is an increasing need to explore digital hoarding behavior. This need is driven by concerns regarding its intricate psychological foundations and its impact on individuals within our technology-centric society. This research investigates the influence of various factors, including the fear of missing out, emotional attachment, information overload, and decision fatigue, on digital hoarding behaviors among university students in Iran. Additionally, the study examines the moderating role of maladaptive perfectionism in these relationships. The study involved 275 university students (mean age = 21.62 years; standard deviation = 2.28 years; 65.6% female) selected from four universities in Iran. The data were analyzed using partial least squares structural equation modeling (PLS-SEM). The results revealed that the fear of missing out, emotional attachment, information overload, and decision fatigue significantly predict university students’ digital hoarding behavior. Moreover, the findings highlighted the moderating effect of maladaptive perfectionism on the association between emotional attachment and digital hoarding behavior. This suggests that individuals with higher levels of maladaptive perfectionism exhibit amplified digital hoarding tendencies when emotionally attached to their digital data. This study provides a deeper understanding of the relationship between psychological factors and digital hoarding tendencies. These findings have practical implications for educational institutions and mental health professionals, as they can help in developing targeted strategies and interventions to manage digital hoarding behavior in university freshmen and promote healthier digital habits.

The significance of big data analytics (BDA) has benefited the health sector by leverage the potential insights and capabilities of big data in decision-making. However, every implementation of BDA within the healthcare field faces difficulties due to incomplete or flawed information that necessitates attention and resolution. The purpose of this systematic literature review is to accomplish two main objectives. Firstly, it aims to synthesize the various elements that contribute to imperfect information in BDA and their impact on decision-making processes within the healthcare sector. This involves identifying and analyzing the factors that can result in imperfect information in BDA applications. Secondly, the review intends to create a taxonomy specifically focused on imperfect information within the context of BDA in the health sector. The study con-ducted a systematic review of the literature, specifically focusing on studies written in English and published up until February 2023. We also screened and retrieved the titles, abstracts, and potentially relevant studies to determine if they met the criteria for inclusion. As a result, they obtained a total of 58 primary studies. The findings displayed that the presence of uncertainty, imprecision, vagueness, incompleteness, and complexity factors in BDA significantly impacts the ability to sustain effective decision-making in the healthcare sector. Additionally, the study highlighted that the taxonomy for imperfect information in BDA provides healthcare managers with the means to utilize suitable strategies essential for successful implementation when dealing with incomplete information in big data. These findings have practical implications for BDA service providers, as they can leverage the findings to attract and promote the adoption of BDA within the healthcare sector.

Big Data Analytics (BDA) has the potential to deliver a comprehensive answer with improved insights into the value of the data collected in the healthcare sector. An assessment of the quality of BDA is crucial to assure that the goal of BDA as the end-to-end solution in enhancing healthcare organizations is achieved. Thus, a holistic BDA Quality (BDAQ) model needs to be developed to achieve the goal. As part of the BDAQ model development processes, this paper will highlight the content validation study. In quantitative research, survey instrument validation is important. A group of ten experts, including two academicians, two specialists, one data scientist, two subject matter experts, a BDA expert, an industry expert, a senior ICT director in healthcare, and a content validity expert, provided the data. Based on the collected data, the scores were analyzed by incorporating the Item-Content Validity index (I-CVI) and modified kappa (K) statistics. From a collection of 62 items, the content validity process removed one item and retained 61 items, while the revised items continued for further analysis. These specific content validity findings suggest that the assessed instrument can be employed to establish a degree of content validity that is acceptable. Once the verification process and analysis are done, the pilot study on the BDAQ model will begin, followed by the validated BDAQ model through the actual study.

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needs to understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of their BDS.

Big Data Analytics implementation in healthcare can provide an end-to-end solution with better information value insights. This paper will highlight expert opinion in verifying the quality factors in big data analytics (BDA) that affect the performance of healthcare organizations and present the findings in developing the BDA quality model. This expert verification study was carried out through hybrid approach sessions either face-to-face or by virtual online meetings with two academicians, two specialists, one data scientist, two subject matter experts, and also as BDA expert, one industry expert, one senior ICT Director involve in healthcare, and one content validity expert. All the experts have a vast experience and implementation of BDA in healthcare. The results of these exercises have confirmed and verified ten BDA quality factors which consist of reliability, accuracy, completeness, timeliness, format, accessibility, usability, maintainability, and portability were commensurate with the research model. The analysis of this expert verification study has been done through descriptive analysis such as frequency, mean, and standard deviation. The pilot study will be commenced once the verification process and analysis are completed and the BDA quality model will be validated later through actual study.

Rapid technological development has led to an information explosion in the current Web environment. Recently, tourists have become reliant on the Internet as a tool to obtain information about the places they intend to visit. However, due to the overload of information, tourists face many challenges and difficulties in making the right choice. Despite the promise of Web 3.0’s revolutionary solutions to address all of Web 2.0’s shortcomings, there is still a significant gap between currently implemented systems and the useful innovation of future technologies in the tourism industry. This study proposes a theoretical model to examine the role of personal innovativeness in tourists’ intention to use Web 3.0 based on the DeLone and McLean model. Although many attempts were made in prior work to address this issue, most of those studies focused on the evolution of Web 3.0 from the technical side and did not investigate it from the theoretical perspective in different domains in general and tourism in particular. The method of this study was based on a survey questionnaire with 643 participants. SmartPLS version 3.3.3 was used to analyze the study data. The results of this study reveal that information quality, system quality, service quality, social influence, and personal innovativeness had significant effects on tourists’ intention to use Web 3.0, while awareness did not have a significant effect. This study provides further insights, expands our understanding of the study topic, and contributes to this growing research area, and the novel research framework can act as a fundamental theoretical model for future studies in different contexts.