International Journal of Information Management Data Insights Technology & behavioral changes mediation for personnel safety intentions: Crisis in theoretical framework.

With a theoretical S-O-R(stimulus-organism-response) framework, the study focused on the technology and be- havioral changes ensuring personal safety intentions in developed countries. Covid-19 Crisis made the scenario feel the difference in rich people’s society or not? Rare research focused on technology-related behavioral changes due to the 20 th century and a surge in data insights. A random sampling technique was used to analyze data from 580 individuals. At first, P.L.S. (partial least square) analysis proved that leisure, health anxiety-related informa- tion flow, and especially new social media trends had substantial effects on technology and behavioral changes. Statistical results, including time series and correlation results, focused more on personnel safety intentions in China. Individual-based historical data proved a huge data use intentions change even until 2022. Hence, the first-ever preliminary research findings will open a new aperture in information management.

[1]  S. Verma,et al.  Past, present, and future of virtual tourism-a literature review , 2022, Int. J. Inf. Manag. Data Insights.

[2]  Yaser Hasan Salem Al-Mamary Why do students adopt and use Learning Management Systems?: Insights from Saudi Arabia , 2022, Int. J. Inf. Manag. Data Insights.

[3]  Haiyan Song,et al.  Toward an accurate assessment of tourism economic impact: A systematic literature review , 2022, Annals of Tourism Research Empirical Insights.

[4]  J. Hanaysha Impact of social media marketing features on consumer's purchase decision in the fast-food industry: Brand trust as a mediator , 2022, Int. J. Inf. Manag. Data Insights.

[5]  Rajat Kumar Behera,et al.  Self-promotion and online shaming during COVID-19: A toxic combination , 2022, International Journal of Information Management Data Insights.

[6]  A. Kar,et al.  How do Fortune firms build a social presence on social media platforms? Insights from multi-modal analytics , 2022, Technological Forecasting and Social Change.

[7]  Aifeng Yang,et al.  Precautionary measures, speeding behaviour, and accidental trauma in shaping post-accidental driving behaviour and word of mouth , 2022, International Journal of Crashworthiness.

[8]  L. Bandini,et al.  Exploring leisure time use and impact on well-being among transition-age autistic youth , 2022, Research in Autism Spectrum Disorders.

[9]  Yang Wang,et al.  Feature extraction of search product based on multi-feature fusion -oriented to Chinese online reviews , 2022, Data Science and Management.

[10]  Z. Siddique,et al.  Industry 4.0 in Healthcare: A systematic review , 2022, Int. J. Inf. Manag. Data Insights.

[11]  R. Raman,et al.  Success attributes of business leaders from information technology industry: Evidence from India , 2022, Int. J. Inf. Manag. Data Insights.

[12]  Prathamesh P. Churi,et al.  The impact of Instagram on young Adult's social comparison, colourism and mental health: Indian perspective , 2022, Int. J. Inf. Manag. Data Insights.

[13]  M. Reveilhac,et al.  The framing of health technologies on social media by major actors: Prominent health issues and COVID-related public concerns , 2022, Int. J. Inf. Manag. Data Insights.

[14]  Mamta Mittal,et al.  Adoption of artificial intelligence in smart cities: A comprehensive review , 2022, Int. J. Inf. Manag. Data Insights.

[15]  Martin Henseler,et al.  Economic impacts of COVID-19 on the tourism sector in Tanzania , 2022, Annals of Tourism Research Empirical Insights.

[16]  Cem Işık,et al.  Factors Affecting Electric Bike Adoption: Seeking an Energy-Efficient Solution for the Post-COVID Era , 2022, Frontiers in Energy Research.

[17]  Tian Hewei,et al.  Factors Affecting Continuous Purchase Intention of Fashion Products on Social E-commerce: SOR Model and the Mediating Effect , 2021, Entertainment Computing.

[18]  C. Hargreaves,et al.  Leveraging Twitter data to understand public sentiment for the COVID-19 outbreak in Singapore , 2021, Int. J. Inf. Manag. Data Insights.

[19]  Ralf Plattfaut,et al.  Looking for Talent in Times of Crisis - The Impact of the Covid-19 Pandemic on Public Sector Job Openings , 2021, Int. J. Inf. Manag. Data Insights.

[20]  Darshana Sedera,et al.  Value co-creation for open innovation: An evidence-based study of the data driven paradigm of social media using machine learning , 2021, Int. J. Inf. Manag. Data Insights.

[21]  C. Kou,et al.  Leisure activity and cognitive function among Chinese old adults: The multiple mediation effect of anxiety and loneliness. , 2021, Journal of affective disorders.

[22]  Jin X. Goh,et al.  Parasites and promiscuity: Acute disease salience leads to more restricted sexual attitudes , 2021, Journal of Social and Personal Relationships.

[23]  F. Shahzad,et al.  Assessing Public Willingness to Wear Face Masks during the COVID-19 Pandemic: Fresh Insights from the Theory of Planned Behavior , 2021, International journal of environmental research and public health.

[24]  P. Vigneswara Ilavarasan,et al.  Applications of text mining in services management: A systematic literature review , 2021, Int. J. Inf. Manag. Data Insights.

[25]  A. Chamarro,et al.  Towards sustainable tourism development in a mature destination: measuring multi-group invariance between residents and visitors’ attitudes with high use of accommodation-sharing platforms , 2021, Journal of Sustainable Tourism.

[26]  Po-Ju Chen,et al.  Place attachment to pseudo establishments: An application of the stimulus-organism-response paradigm to themed hotels , 2020 .

[27]  Makarand Mody,et al.  Hapless victims or empowered citizens? Understanding residents’ attitudes towards Airbnb using Weber’s Theory of Rationality and Foucauldian concepts , 2020, Journal of Sustainable Tourism.

[28]  Nainan Wen,et al.  What motivates Chinese consumers to avoid information about the COVID-19 pandemic?: The perspective of the stimulus-organism-response model , 2020, Information Processing & Management.

[29]  Yogesh K. Dwivedi,et al.  Theory building with big data-driven research - Moving away from the "What" towards the "Why" , 2020, Int. J. Inf. Manag..

[30]  Suchi Saria,et al.  An Individualized, Data-Driven Digital Approach for Precision Behavior Change , 2020, American journal of lifestyle medicine.

[31]  J. Torous,et al.  Understanding the evolving preferences for use of health information technology among adults with self reported anxiety and depression in the U.S , 2020 .

[32]  Munir Ahmad,et al.  Modeling Impact of Word of Mouth and E-Government on Online Social Presence during COVID-19 Outbreak: A Multi-Mediation Approach , 2020, International journal of environmental research and public health.

[33]  M. Brauer,et al.  Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study , 2020, The Lancet.

[34]  Jin X. Goh,et al.  Outbreaks and Outgroups: Three Tests of the Relationship Between Disease Avoidance Motives and Xenophobia During an Emerging Pandemic , 2020, Evolutionary Psychological Science.

[35]  Feng Hsu Wang,et al.  On the relationships between behaviors and achievement in technology-mediated flipped classrooms: A two-phase online behavioral PLS-SEM model , 2019, Comput. Educ..

[36]  Silas Formunyuy Verkijika,et al.  Understanding word-of-mouth (WOM) intentions of mobile app users: The role of simplicity and emotions during the first interaction , 2019, Telematics Informatics.

[37]  Nicholas Berente,et al.  Research Commentary - Data-Driven Computationally Intensive Theory Development , 2019, Inf. Syst. Res..

[38]  Dong-xiao Gu,et al.  Heritage Image and Attitudes toward a Heritage Site: Do They Really Mediate the Relationship between User-Generated Content and Travel Intentions toward a Heritage Site? , 2018, Sustainability.

[39]  Yogesh Kumar Dwivedi,et al.  Examining branding co-creation in brand communities on social media: Applying the paradigm of Stimulus-Organism-Response , 2018, Int. J. Inf. Manag..

[40]  Jeffrey R. Edwards,et al.  Improving Our Understanding of Moderation and Mediation in Strategic Management Research , 2017 .

[41]  Alain Yee-Loong Chong,et al.  An updated and expanded assessment of PLS-SEM in information systems research , 2017, Ind. Manag. Data Syst..

[42]  Sandra Streukens,et al.  Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results , 2016 .

[43]  Joseph F. Hair,et al.  Estimation issues with PLS and CBSEM: Where the bias lies! ☆ , 2016 .

[44]  Joris H. Janssen,et al.  Technology-Based Innovations to Foster Personalized Healthy Lifestyles and Well-Being: A Targeted Review , 2016, Journal of medical Internet research.

[45]  Marko Sarstedt,et al.  Testing measurement invariance of composites using partial least squares , 2016 .

[46]  Geoffrey S. Hubona,et al.  Using PLS path modeling in new technology research: updated guidelines , 2016, Ind. Manag. Data Syst..

[47]  D. Hunt,et al.  Expectations in the field of the Internet and health: an analysis of claims about social networking sites in clinical literature , 2015, Sociology of health & illness.

[48]  Isabell Büschel,et al.  Protecting Human Health and Security in Digital Europe: How to Deal with the “Privacy Paradox”? , 2014, Sci. Eng. Ethics.

[49]  Corneel Vandelanotte,et al.  Effects of a Web-Based Tailored Multiple-Lifestyle Intervention for Adults: A Two-Year Randomized Controlled Trial Comparing Sequential and Simultaneous Delivery Modes , 2014, Journal of medical Internet research.

[50]  Marko Sarstedt,et al.  Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research , 2014 .

[51]  Marko Sarstedt,et al.  The Use of Partial Least Squares Structural Equation Modeling in Strategic Management Research: A Review of Past Practices and Recommendations for Future Applications , 2012 .

[52]  Edward E. Rigdon,et al.  Rethinking Partial Least Squares Path Modeling: In Praise of Simple Methods , 2012 .

[53]  D. Lupton M-health and health promotion: The digital cyborg and surveillance society , 2012, Social Theory & Health.

[54]  Donald Hedeker,et al.  A Practical Guide to Calculating Cohen’s f2, a Measure of Local Effect Size, from PROC MIXED , 2012, Front. Psychology.

[55]  E. Walker,et al.  Diagnostic and Statistical Manual of Mental Disorders , 2013 .

[56]  Marko Sarstedt,et al.  PLS-SEM: Indeed a Silver Bullet , 2011 .

[57]  Genevieve Fridlund Dunton,et al.  Using Ecological Momentary Assessment to Examine Antecedents and Correlates of Physical Activity Bouts in Adults Age 50+ Years: A Pilot Study , 2009, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[58]  K. Scherer What are emotions? And how can they be measured? , 2005 .

[59]  B. Byrne Structural equation modeling with EQS : basic concepts, applications, and programming , 2000 .

[60]  B. Byrne Structural Equation Modeling with LISREL, PRELIS, and SIMPLIS: Basic Concepts, Applications, and Programming , 1998 .

[61]  N. Heather,et al.  Development of a short 'readiness to change' questionnaire for use in brief, opportunistic interventions among excessive drinkers. , 1992, British journal of addiction.

[62]  R. Niaura,et al.  Self-efficacy and the stages of exercise behavior change. , 1992, Research quarterly for exercise and sport.

[63]  Jacob Cohen,et al.  The statistical power of abnormal-social psychological research: a review. , 1962, Journal of abnormal and social psychology.

[64]  Richard J. Arend How uncertainty levels and types matter, to likely entrepreneurs and others , 2022, Journal of Business Venturing Insights.

[65]  Youngsook Lee,et al.  Factors affecting continuous purchase intention of fashion products on social E-commerce: SOR model and the mediating effect , 2022, Entertain. Comput..

[66]  Punyashlok Dwibedy Informal competition and product innovation decisions of new ventures and incumbents across developing and transitioning countries , 2022, Journal of Business Venturing Insights.

[67]  Yogesh K. Dwivedi,et al.  Sentiment analysis and classification of Indian farmers' protest using twitter data , 2021, Int. J. Inf. Manag. Data Insights.

[68]  Yongqiang Sun,et al.  Technological environment, virtual experience, and MOOC continuance: A stimulus-organism-response perspective , 2020, Comput. Educ..

[69]  Yau Seng Mah,et al.  Factors Affecting Satisfaction and Loyalty in Public Transport using Partial Least Squares Structural Equation Modeling (PLS-SEM) , 2019, International Journal of Innovative Technology and Exploring Engineering.

[70]  Christian Nitzl,et al.  Mediation Analyses in Partial Least Squares Structural Equation Modeling, Helping Researchers Discuss More Sophisticated Models: An Abstract , 2017 .

[71]  M. Sarstedt,et al.  A new criterion for assessing discriminant validity in variance-based structural equation modeling , 2015 .

[72]  Steven L. Neuberg,et al.  Infection Breeds Reticence: The Effects of Disease Salience on Self-Perceptions of Personality and Behavioral Avoidance Tendencies. , 2012 .

[73]  Wynne W. Chin How to Write Up and Report PLS Analyses , 2010 .

[74]  E. Deci,et al.  Self-Determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness , 2017 .

[75]  Michael R. Mullen,et al.  Structural equation modelling: guidelines for determining model fit , 2008 .

[76]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .