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1 – 8 of 8Malathi Sivasankara Pillai and Kannan Balakrishnan
This paper aims to prove the following hypothesis Problem Statement: HYPOTHESIS (1) User Experience collection of mobile applications can be done using the Crowdsourcing…
Abstract
Purpose
This paper aims to prove the following hypothesis Problem Statement: HYPOTHESIS (1) User Experience collection of mobile applications can be done using the Crowdsourcing mechanism; (2) User Experience collection of mobile applications are influenced by the mindset of Crowdmembers, culture/ethnicity/social background, ease of interface use and rewards, among other factors.
Design/methodology/approach
The authors of this paper, did a literature review first to find if Crowdsourcing was applicable and a used method to solve problems in Software Engineering. This helped us to narrow down the application of Crowdsourcing to the Requirements Engineering-Usability (User Experience) collection. User experience collection of two Malayalam language-based mobile applications, AarogyaSetu and BevQ was done as the next step. Incorporating findings from Study I, another study using AarogyaSetu and Manglish was launched as Study II. The results from both cases were consolidated and analyzed. Significant concerns relating to expectations of Crowd members with User Experience collection were unraveled and the purpose of Study was accomplished.
Findings
(1) Crowdsourcing is and can be used in Software Engineering activities. (2) Crowd members have expectations (motivating factors) of User Interface and other elements that enable them to be an effective contributor. (3) An individual’s environment and mindset (character) are influential in him becoming a contributor in Crowdsourcing. (4) Culture and social practices of a region strongly affects the crowd-participating decision of an individual.
Originality/value
This is purely self-done work. The value of this research work is two-fold. Crowdsourcing is endorsed significant in Software Engineering tasks, especially in User Experience collection of mobile applications. Two, the Crowd service requesters can be careful about designing the questionnaire for Crowdsourcing. They have to be aware and prepared to meet the expectations of the Crowd. This can ensure the active participation of potential contributors. Future researchers can use the results of this work to base their research on similar purposes.
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HaeJung Maria Kim and Swagata Chakraborty
The study aims to explore the digital fashion trend within the Metaverse, characterized by non-fungible tokens (NFTs), across Twitter networks. Integrating theories of diffusion…
Abstract
Purpose
The study aims to explore the digital fashion trend within the Metaverse, characterized by non-fungible tokens (NFTs), across Twitter networks. Integrating theories of diffusion of innovation, two-step flow of communication and self-efficacy, the authors aimed to uncover the diffusion structure and the influencer's social roles undertaken by social entities in fostering communication and collaboration for the advancement of Metaverse fashion.
Design/methodology/approach
Social network analysis examined the critical graph metrics to profile, visualize, and cluster the unstructured network data. The authors used the NodeXL program to analyze two hashtag keyword networks, “#metaverse fashion” and “#metawear,” using Twitter API data. Cluster, semantic, and time series analyses were performed to visualize the contents and contexts of communication and collaboration in the diffusion of Metaverse fashion.
Findings
The results unraveled the “broadcast network” structure and the influencers' social roles of opinion leaders and market mavens within Twitter's “#metaverse fashion” diffusion. The roles of innovators and early adopters among influencers were comparable in collaborating within the competition venues, promoting awareness and participation in digital fashion diffusion during specific “fad” periods, particularly when digital fashion NFTs and cryptocurrencies became intertwined with the competition in the Metaverse.
Originality/value
The study contributed to theory building by integrating three theories, emphasizing effective communication and collaboration among influencers, organizations, and competition venues in broadcasting digital fashion within shared networks. The validation of multi-faceted Social Network Analysis was crucial for timely insights, highlighting the critical digital fashion equity in capturing consumers' attention and driving engagement and ownership of Metaverse fashion.
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Bülent Doğan, Yavuz Selim Balcioglu and Meral Elçi
This study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to…
Abstract
Purpose
This study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.
Design/methodology/approach
A mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.
Findings
The results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.
Research limitations/implications
The primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.
Practical implications
Understanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.
Social implications
The study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.
Originality/value
This research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.
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Nava Rothschild, Jonathan Schler, David Sarne and Noa Aharony
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…
Abstract
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.
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This study aims to predict artificial intelligence (AI) technology development and the impact of AI utilization activity on companies, to identify AI strategies dealing with the…
Abstract
Purpose
This study aims to predict artificial intelligence (AI) technology development and the impact of AI utilization activity on companies, to identify AI strategies dealing with the broad innovation activity of AI, and to construct the strategic decision-making framework of AI strategies for a small- and medium-sized enterprise (hereafter SME), to improve strategic decision-making practices of AI strategy in SMEs.
Design/methodology/approach
This study used the multiple methods on the design of two data collection stages. The first stage is an expertise-based approach. It organized the three groups of expert panels and conducted the Delphi survey on them in combination with the brainstorming of technology, innovation and strategy in the fourth industrial revolution. The second stage is in the complement approach of expertise-based results. It used the literature review to involve the analysis of academic and practical papers, reports and audio materials relating to technology development, innovation types and strategies of AI. Additionally, it organized the four semi-structured interviews. Finally, this study used the mind-map and decision tree to conduct each analysis and synthesize each analytical result.
Findings
This study identifies the precondition and four paths of AI technological development classifying into specialized AI, AI convergence with other technologies, general AI and AI control methods. It captures the impact of non- and technological innovation through AI on companies. Second, it identifies and classifies the six types of AI strategy: the bystander, capability-building, capability-holding, management-enhancing, market-enhancing and new-market-creating strategy. By using the decision tree, it constructs the strategic decision-making framework containing six AI strategies. Actionable points, strategic priorities and relevant instruments are suggested.
Research limitations/implications
The strategic decision-making framework covering from AI technology development to utilization in a SME can help understand the strategic behaviours in SMEs. The typology of six AI strategies implies the broad innovation behaviours in SMEs. It can lead to further research to understand the pattern of strategic and innovation behaviour on AI.
Practical implications
This practical study can help executives, managers and engineers in SMEs to develop their strategic practices through the strategic decision framework and six AI strategies.
Originality/value
This practical study elicits the six types of AI strategy and constructs the strategic decision-making framework of six AI strategies from AI technology development to utilization. It can contribute to improving the practices of strategic decision-making in SMEs.
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This study aims to explain the relationship between employer branding, social media, online reviews and intention to apply for a job vacancy (IAJV), which organizations should…
Abstract
Purpose
This study aims to explain the relationship between employer branding, social media, online reviews and intention to apply for a job vacancy (IAJV), which organizations should ponder upon while designing branding campaigns.
Design/methodology/approach
The sample belongs to 385 final-year management graduates and postgraduates enrolled in central universities in the state of Uttar Pradesh, India. The dual mediation model is tested by regression and PROCESS macro.
Findings
Out of five employer branding dimensions, three (corporate social responsibility, healthy work atmosphere and training and development) were found to be significant predictors of IAJV. On the other hand, the dimensions of compensation and benefits and work-life balance did not influence candidates’ intention to apply for a job. The findings indicate that social recruiting could act as an effective tool for leveraging an organization’s image as an employer and could communicate unique brand values to the target market. Moreover, review whether positive, negative or neutral attributes could help job seekers affirm and reaffirm employer branding attributes before applying for a job.
Originality/value
Studies in social media and employer branding areas lag far behind in practice, and the present research attempts to fill this research gap. A further contribution of this research work will be to assess the role of reviews for a meaningful analysis of potential employees’ intentions to apply in an organization.
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Eyyub Can Odacioglu, Lihong Zhang, Richard Allmendinger and Azar Shahgholian
There is a growing need for methodological plurality in advancing operations management (OM), especially with the emergence of machine learning (ML) techniques for analysing…
Abstract
Purpose
There is a growing need for methodological plurality in advancing operations management (OM), especially with the emergence of machine learning (ML) techniques for analysing extensive textual data. To bridge this knowledge gap, this paper introduces a new methodology that combines ML techniques with traditional qualitative approaches, aiming to reconstruct knowledge from existing publications.
Design/methodology/approach
In this pragmatist-rooted abductive method where human-machine interactions analyse big data, the authors employ topic modelling (TM), an ML technique, to enable constructivist grounded theory (CGT). A four-step coding process (Raw coding, expert coding, focused coding and theory building) is deployed to strive for procedural and interpretive rigour. To demonstrate the approach, the authors collected data from an open-source professional project management (PM) website and illustrated their research design and data analysis leading to theory development.
Findings
The results show that TM significantly improves the ability of researchers to systematically investigate and interpret codes generated from large textual data, thus contributing to theory building.
Originality/value
This paper presents a novel approach that integrates an ML-based technique with human hermeneutic methods for empirical studies in OM. Using grounded theory, this method reconstructs latent knowledge from massive textual data and uncovers management phenomena hidden from published data, offering a new way for academics to develop potential theories for business and management studies.
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Yanqing Lin, Shaoxiong Fu and Xun Zhou
As the number of social media users continues to rise globally, a heated debate emerges on whether social media use improves or harms mental health, as well as the bidirectional…
Abstract
Purpose
As the number of social media users continues to rise globally, a heated debate emerges on whether social media use improves or harms mental health, as well as the bidirectional relation between social media use and mental health. Motivated by this, the authors’ study adopts the stressor–strain–outcome model and social compensation hypothesis to disentangle the effect mechanism between social media use and psychological well-being. The purpose of this paper is to address this issue.
Design/methodology/approach
To empirically validate the proposed research model, a large-scale two-year longitudinal questionnaire survey on social media use was administered to a valid sample of 6,093 respondents recruited from a university in China. Structural equation modeling was employed for data analysis.
Findings
A longitudinal analysis reveals that social media use positively (negatively) impacts psychological well-being through the mediator of nomophobia (perceived social support) in a short period. However, social media use triggers more psychological unease, as well as more life satisfaction from a longitudinal perspective.
Originality/value
This study addresses the bidirectional relation between social media use and psychological unease. The current study also draws both theoretical and practical implications by unmasking the bright–dark duality of social media use on psychological well-being.
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