Laila Dahabiyeh, Ali Farooq, Farhan Ahmad, Yousra Javed
{"title":"Explaining technology migration against the change in terms of use: an fsQCA approach","authors":"Laila Dahabiyeh, Ali Farooq, Farhan Ahmad, Yousra Javed","doi":"10.1108/itp-07-2022-0498","DOIUrl":null,"url":null,"abstract":"PurposeDuring the past few years, social media has faced the challenge of maintaining its user base. Reports show that the social media giants such as Facebook and Twitter experienced a decline in their users. Taking WhatsApp's recent change of its terms of use as the case of this study and using the push-pull-mooring model and a configurational perspective, this study aims to identify pathways for switching intentions.Design/methodology/approachData were collected from 624 WhatsApp users recruited from Amazon Mechanical Turk and analyzed using fuzzy set qualitative comparative analysis (fsQCA).FindingsThe findings identify seven configurations for high switching intentions and four configurations for low intentions to switch. Firm reputation and critical mass increase intention to switch, while low firm reputation and absence of attractive alternatives hinder switching.Research limitations/implicationsThis study extends extant literature on social media migration by identifying configurations that result in high and low switching intention among messaging applications.Practical implicationsThe study identifies factors the technology service providers should consider to attract new users and retain existing users.Originality/valueThis study complements the extant literature on switching intention that explains the phenomenon based on a net-effect approach by offering an alternative view that focuses on the existence of multiple pathways to social media switching. It further advances the authors’ understanding of the relevant importance of switching factors.","PeriodicalId":168000,"journal":{"name":"Information Technology & People","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Technology & People","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/itp-07-2022-0498","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
PurposeDuring the past few years, social media has faced the challenge of maintaining its user base. Reports show that the social media giants such as Facebook and Twitter experienced a decline in their users. Taking WhatsApp's recent change of its terms of use as the case of this study and using the push-pull-mooring model and a configurational perspective, this study aims to identify pathways for switching intentions.Design/methodology/approachData were collected from 624 WhatsApp users recruited from Amazon Mechanical Turk and analyzed using fuzzy set qualitative comparative analysis (fsQCA).FindingsThe findings identify seven configurations for high switching intentions and four configurations for low intentions to switch. Firm reputation and critical mass increase intention to switch, while low firm reputation and absence of attractive alternatives hinder switching.Research limitations/implicationsThis study extends extant literature on social media migration by identifying configurations that result in high and low switching intention among messaging applications.Practical implicationsThe study identifies factors the technology service providers should consider to attract new users and retain existing users.Originality/valueThis study complements the extant literature on switching intention that explains the phenomenon based on a net-effect approach by offering an alternative view that focuses on the existence of multiple pathways to social media switching. It further advances the authors’ understanding of the relevant importance of switching factors.