The evolution of data analytics through the lens of business cases

PurposeThe present study provides a comprehensive review of the evolution of data analytics using real-world cases. The purpose is to provide a distinct overview of where the phenomenon was derived from, where it currently stands and where it is heading.Design/methodology/approachThree case studies were selected to represent three different eras of data analytics: Yesterday (1950s–1990s), Today (2000s–2020s) and Tomorrow (2030s–2050s).FindingsRapid changes in information technologies more likely moving us towards a more cyber-physical society, where an increasing number of devices, people and corporations are connected. We can expect the development of a more connected cyber society, open for data exchange than ever before.Social implicationsThe analysis of technological trends through the lens of representative real-world cases helps to clarify where data analytics was derived from, where it currently stands and where it is heading towards. The presented case studies accentuate that data analytics is constantly evolving with no signs of stagnation.Originality/valueAs the field of data analytics is constantly evolving, the study of its evolution based on particular studies aims to better understand the paradigm shift in data analytics and the resulting technological advances in the IT business through the representative real-life cases.

Social network fatigue: revisiting the antecedents and consequences

PurposeThe study explores the antecedents and consequences of social network fatigue (SNF) using the stress–strain–outcome (S-S-O) model. It builds on the work of previous scholars in the area of SNS by focussing on the intervening processes that explain the study's focal constructs.Design/methodology/approachTo test the proposed framework, the study draws cross-sectional data from Indian Facebook users. Data were collected using an online survey, and the final sample of 309 valid and complete responses was analysed using SmartPLS to test the study's hypotheses.FindingsThe findings of the study report fear of missing out (FoMO) to be positively related to compulsive use (CU) of Facebook. CU had positive and significant direct and indirect effects (via information and social overload) on SNF. SNF was significantly associated with both depression (DEP) and discontinuous use intention (DUI). However, Facebook use intensity (FBI)'s moderating role on the relationship between FoMO and CU was insignificant.Originality/valueFirst, the current study proposes and empirically tests a comprehensive model on the lines of the S-S-O model to understand the antecedents and consequences of SNF. Second, the study uses an Indian sample that is not age-specific (adolescents or young adults), unlike most past studies. Third, it examines various intervening stages and processes (through mediation and moderation) suggested by previous scholars but not yet explored.

Wired to seek, comment and share? Examining the relationship between personality, news consumption and misinformation engagement

PurposeNews consumption is critical in creating informed citizenry; however, in the current context of media convergence, news consumption becomes more complex as social media becomes a primary news source rather than news media. The current study seeks to answer three questions: why the shifted pattern of news seeking only happens to some but not all of the news consumers; whether the differentiated patterns of news seeking (news media vs social media) would result in different misinformation engagement behaviors; and whether misperceptions would moderate the relationship between news consumption and misinformation engagement.Design/methodology/approachA survey consisted of questions related to personality traits, news seeking, misperceptions and misinformation engagement was distributed to 551 individuals. Multiple standard regression and PROCESS Macro model 1 were used to examine the intricate relationships between personality, news use and misinformation engagement.FindingsResults indicate that extroversion was positively associated with social media news consumption while openness was inversely related to it. Social media news consumption in turn positively predicted greater misinformation sharing and commenting. No association was found between Big Five personality traits and news media news seeking. News media news seeking predicted higher intention to reply to misinformation. Both relationships were further moderated by misperceptions that individuals with greater misperceptions were more likely to engage with misinformation.Originality/valueThe current study integrates personality traits, news consumption and misperceptions in understanding misinformation engagement behaviors. Findings suggest that news consumption via news media in the digital era merits in-depth examinations as it may associate with more complex background factors and also incur misinformation engagement. Social media news consumption deserves continuous scholarly attention. Specifically, extra attention should be devoted to extrovert and pragmatic individuals in future research and interventions. People with these characteristics are more prone to consume news on social media and at greater risk of falling prey to misinformation and becoming a driving force for misinformation distribution.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2021-0520

Exploring the social activity of open research data on ResearchGate: implications for the data literacy of researchers

Purpose Although current research has investigated how open research data (ORD) are published, researchers' behaviour of ORD sharing on academic social networks (ASNs) remains insufficiently explored. The purpose of this study is to investigate the connections between ORDs publication and social activity to uncover data literacy gaps.Design/methodology/approach This work investigates whether the ORDs publication leads to social activity around the ORDs and their linked published articles to uncover data literacy needs. The social activity was characterised as reads and citations, over the basis of a non-invasive approach supporting this preliminary study. The eventual associations between the social activity and the researchers' profile (scientific domain, gender, region, professional position, reputation) and the quality of the ORD published were investigated to complete this picture. A random sample of ORD items extracted from ResearchGate (752 ORDs) was analysed using quantitative techniques, including descriptive statistics, logistic regression and K-means cluster analysis.Findings The results highlight three main phenomena: (1) Globally, there is still an underdeveloped social activity around self-archived ORDs in ResearchGate, in terms of reads and citations, regardless of the published ORDs quality; (2) disentangling the moderating effects over social activity around ORD spots traditional dynamics within the “innovative” practice of engaging with data practices; (3) a somewhat similar situation of ResearchGate as ASN to other data platforms and repositories, in terms of social activity around ORD, was detected.Research limitations/implications Although the data were collected within a narrow period, the random data collection ensures a representative picture of researchers' practices.Practical implications As per the implications, the study sheds light on data literacy requirements to promote social activity around ORD in the context of open science as a desirable frontier of practice.Originality/value Researchers data literacy across digital systems is still little understood. Although there are many policies and technological infrastructure providing support, the researchers do not make an in-depth use of them.Peer reviewThe peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2021-0255.

Applying the time continuum model of motivation to explain how major factors affect mobile learning motivation: a comparison of SEM and fsQCA

PurposeStudying mobile learning – the use of electronic devices (i.e. cellphone and tablets) to engage in learning across multiple contexts via connection to peers, media, experts and the larger world is a relatively new academic enterprise. This study analyzes the influencing factors of mobile learning (M-learning) motivation based on the time continuum model of motivation (TCMM).Design/methodology/approachThe study uses structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to verify relationships between mobile learning motivation, attitude, need, stimulation, emotion, ability and reinforcement. Justification for the use of both methods lies in the complementarity relationships that existed between the variables and research methodologies. The sample contains 560 mobile learners' feedback.FindingsResults show that attitude, need, emotion, ability and reinforcement are important factors to enhance mobile learning motivation, while stimulation is not.Practical implicationsThis work highlights the importance of training for app designers on how to design an M-learning App with high learning motivation by paying prior attention to learning content, teaching team and online learning communities.Originality/valueThis study proposes three precise solutions (scholars, managers and practitioners) to improve learning motivation based on the categorization of mobile learners.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0226.

Artificial intelligence research in agriculture: a review

PurposeThe purpose of this paper is to provide a comprehensive review on the academic journey of artificial intelligence (AI) in agriculture and to highlight the challenges and opportunities in adopting AI-based advancement in agricultural systems and processes.Design/methodology/approachThe authors conducted a bibliometric analysis of the extant literature on AI in agriculture to understand the status of development in this domain. Further, the authors proposed a framework based on two popular theories, namely, diffusion of innovation (DOI) and the unified theory of acceptance and use of technology (UTAUT), to identify the factors influencing the adoption of AI in agriculture.FindingsFour factors were identified, i.e. institutional factors, market factors, technology factors and stakeholder perception, which influence adopting AI in agriculture. Further, the authors indicated challenges under environmental, operational, technological, economical and social categories with opportunities in this area of research and business.Research limitations/implicationsThe proposed conceptual model needs empirical validation across countries or states to understand the effectiveness and relevance.Practical implicationsPractitioners and researchers can use these inputs to develop technology and business solutions with specific design elements to gain benefit of this technology at larger scale for increasing agriculture production.Social implicationsThis paper brings new developed methods and practices in agriculture for betterment of society.Originality/valueThis paper provides a comprehensive review of extant literature and presents a theoretical framework for researchers to further examine the interaction of independent variables responsible for adoption of AI in agriculture.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2020-0448

Visual analysis of global research trends in social bots based on bibliometrics

PurposeIn order to further advance the research of social bots, based on the latest research trends and in line with international research frontiers, it is necessary to understand the global research situation in social bots.Design/methodology/approachChoosing Web of Science™ Core Collections as the data sources for searching social bots research literature, this paper visually analyzes the processed items and explores the overall research progress and trends of social bots from multiple perspectives of the characteristics of publication output, major academic communities and active research topics of social bots by the method of bibliometrics.FindingsThe findings offer insights into research trends pertaining to social bots and some of the gaps are also identified. It is recommended to further expand the research objects of social bots in the future, not only focus on Twitter platform and strengthen the research of social bot real-time detection methods and the discussion of the legal and ethical issues of social bots.Originality/valueMost of the existing reviews are all for the detection methods and techniques of social bots. Unlike the above reviews, this study is a systematic literature review, through the method of quantitative analysis, comprehensively sort out the research output in social bots and shows the latest research trends in this area and suggests some research indirections that need to be focused in the future. The findings will provide references for subsequent scholars to research on social bots.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2021-0336.

Statistical analysis used in LIS research produced by Pakistani authors

PurposeStatistical methods are important for meaningful analysis, critique and interpretation of results. The current study aims to investigate the use of statistical methods used in LIS research articles produced by Pakistani authors during 2001–2016.Design/methodology/approachContent analysis method with both the qualitative and quantitative components was used. LIS articles published by Pakistani authors in national and international journals from 2001 to 2016 were selected. The descriptive and inferential statistics were used to analyze the usage of statistical techniques.FindingsThe findings show that use of descriptive statistics remained higher as compared to inferential statistics in the LIS research produced by Pakistani authors. However, a visible growth trend in the use of inferential statistical techniques is found. Males are two times more likely to use inferential statistics as compared to female authors. Articles published in foreign journals and impact factor journals used more inferential statistics as compared to local and nonimpact factor journals. Parametric inferential statistics is more popular among Pakistani authors as compared to nonparametric. Faculty was more inclined toward using parametric statistic. The percentage of collaboration was higher in the papers using parametric statistics. Few articles reported the tests to fulfill the assumptions of parametric and nonparametric statistics.Originality/valueThis study can be used to better understand the trends of statistical techniques used in LIS research and authors' orientation in this regard. It will be helpful for future researchers in the selection of appropriate statistical techniques to be used.

Trespassing the gates of research: identifying algorithmic mechanisms that can cause distortions and biases in academic social media

PurposeThe use of recommender systems is increasing on academic social media (ASM). However, distinguishing the elements that may be influenced and/or exert influence over content that is read and disseminated by researchers is difficult due to the opacity of the algorithms that filter information on ASM. In this article, the purpose of this paper is to investigate how algorithmic mediation through recommender systems in ResearchGate may uphold biases in scholarly communication.Design/methodology/approachThe authors used a multi-method walkthrough approach including a patent analysis, an interface analysis and an inspection of the web page code.FindingsThe findings reveal how audience influences on the recommendations and demonstrate in practice the mutual shaping of the different elements interplaying within the platform (artefact, practices and arrangements). The authors show evidence of the mechanisms of selection, prioritization, datafication and profiling. The authors also substantiate how the algorithm reinforces the reputation of eminent researchers (a phenomenon called the Matthew effect). As part of defining a future agenda, we discuss the need for serendipity and algorithmic transparency.Research limitations/implicationsAlgorithms change constantly and are protected by commercial secrecy. Hence, this study was limited to the information that was accessible within a particular period. At the time of publication, the platform, its logic and its effects on the interface may have changed. Future studies might investigate other ASM using the same approach to distinguish potential patterns among platforms.Originality/valueContributes to reflect on algorithmic mediation and biases in scholarly communication potentially afforded by recommender algorithms. To the best of our knowledge, this is the first empirical study on automated mediation and biases in ASM.

Social media usage and employee creativity: is relational energy a missing link?

PurposePrevious studies overemphasize the negative effects of social media usage (SMU) within organizations and underestimate its positive influences on employees' behavior. This study attempts to link employees' social media use at work to their creativity performance.Design/methodology/approachBased on the bounded generalized reciprocity theory and unbounded indirect reciprocity (UIR) theory, the authors developed a research model. To test the model, the authors collected a set of 172 paired data of organizations and employees from 31 knowledge-intensive enterprises in China to test the hypothesis.FindingsThis research found that the social, cognitive and hedonic uses of social media all directly affect employee creativity. Relational energy fully mediates the effects of the cognitive and hedonic usages on creativity. Moreover, job autonomy moderates the effects of the relationships among the social, cognitive and hedonic uses on employee creativity.Originality/valueThe conclusions not only enriched authors’ understanding of the effectiveness of interpersonal interaction but also extended the research boundary of the relationship between SMU and employee creativity.