Examining the Role of Tie Strength in Users’ Continuance Intention of Second-Generation Mobile Instant Messaging Services

Gong, Xiuyuan; Lee, Matthew; Liu, Zhiying; Zheng, Xiabing

doi:10.1007/s10796-018-9852-9

Cited by 51 publications

(45 citation statements)

References 119 publications

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“…The decision is dependent on the perceived characteristics of the innovation, while the confirmation process reflects the degree to which technology performance produces perceptions consistent with prior beliefs (Rogers, 1995). Research in the expectation-(dis)confirmation domain has focused on confirmation (Gong et al, 2018) or the positive disconfirmation front, whereby technology outperforms initial expectations (e.g. with studies focusing on the correlation between positive disconfirmation and satisfaction) (Hsieh et al, 2010, McKinney et al, 2002.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Dissonance in Technology Adoption: A Study of Smart Home Users

2020

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This study aims to address a research gap related to the outcomes of the use of technology when the performance falls short of initial expectations, and the coping mechanisms that users may deploy in such circumstances. By adopting Cognitive Dissonance Theory, the objectives of the study are a) to examine how dissonance, caused by the negative disconfirmation of expectations, may translate into a positive outcome and b) study how negative emotions, such as anger, guilt and regret, determine the selection of the mechanism to reduce dissonance. The theorised model was tested using a cross-sectional research design and a sample of 387 smart home users. The focus on smart home users fitted the objectives of the study due to the high expectations that users form and the challenges that the utilisation of technology sometimes causes. The collected data was analysed using structural equation modelling. Findings indicate that post-disconfirmation dissonance induces feelings of anger, guilt and regret, correlating with dissonance reduction mechanisms, which in turn have a distinctive effect on satisfaction and wellbeing. The findings of the study contribute to the discussion on expectation-disconfirmation and cognitive dissonance, by illustrating the interrelationship between emotional, cognitive and behavioural factors following the evaluation of technology performance and confirming that negative disconfirmation may result in satisfaction.

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Section: Introductionmentioning

confidence: 99%

Cognitive Dissonance in Technology Adoption: A Study of Smart Home Users

2020

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“…Trust was assessed through a measure adapted from a study by Chang and Heo [45]. Social ties were measured by three items adapted from the studies of Ma, Sian Lee [46] and Gong, Lee [37]. Size of the social network was measured with one item extracted from the study by Almakrami [47].…”

Section: Methodsmentioning

confidence: 99%

“…Also, users could meet new people and maintain existing relationships. As well as, ample networks could provide a wide range of social support [5,10,37,40,41]. On this basis, it is expected: H3: The size of the social network has a direct and positive influence on Facebook use…”

Section: Size Of the Social Networkmentioning

confidence: 99%

“…Social ties are the primary motivator for the use of social networks such as Facebook [35,36]. Users achieve the maintenance and strengthening of relationships through routine and strategic behaviors, such as affective actions and search and dissemination of information through the SNS [37,38]. The strengthening and maintenance of any of these relationships will imply, therefore, the combination of time, emotional intensity, intimacy, and relational reciprocity that form all relationships [37].…”

Section: Strength Of Social Tiesmentioning

confidence: 99%

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Explaining Multidimensional Facebook Benefits: A Task-Technology Fit Approach

Bravo

Bayona

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Facebook has emerged as the most popular Social Network Site (SNS). The literature has studied extensively the factors that explain Facebook usage. Despite this, not equal attention has been devoted to explaining the benefits of this SNS. The few studies have considered impacts as one-dimensional; however, the literature shows that benefits could be conceptualized as a multidimensional construct. Besides, little is known about using the Task-Technology Fit model (TTF) to assess Facebook. In addressing this gap, this study aims to develop and empirically test a model that explains Facebook benefits in a multiple-way using a tasktechnology fit approach. Data collected from 240 Facebook users, analyzed using partial least squares technique (PLS). The results support the model empirically. This research integrates benefits, use, and task-technology fit into a single model to provide a more comprehensive perspective. Also, a multidimensional view allows us to consider both utilitarian and hedonic benefits as dimensions of value that can spawn greater continued use.

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“…With the text extraction technique, text data integrated into a semantic web. Text processing can reduce vocabulary, create new semantic features using natural language processing (NLP), or select relevant text for analysis using complex information procedures 23,24 . The text semantics is often resolved during text extraction and has an important influence on the meaning of the text.…”

Section: Introductionmentioning

confidence: 99%

Author classification using transfer learning and predicting stars in co‐author networks

et al. 2020

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Summary The vast amount of data is key challenge to mine a new scholar that is plausible to be star in the upcoming period. The enormous amount of unstructured data raise every year is infeasible for traditional learning; consequently, we need a high quality of preprocessing technique to expand the performance of traditional learning. We have persuaded a novel approach, Authors classification algorithm using Transfer Learning (ACTL) to learn new task on target area to mine the external knowledge from the source domain. Comprehensive experimental outcomes on real‐world networks showed that ACTL, Node‐based Influence Predicting Stars, Corresponding Authors Mutual Influence based on Predicting Stars, and Specific Topic Domain‐based Predicting Stars enhanced the node classification accuracy as well as predicting rising stars to compared with contemporary baseline methods.

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Examining the Role of Tie Strength in Users’ Continuance Intention of Second-Generation Mobile Instant Messaging Services

Cited by 51 publications

References 119 publications

Cognitive Dissonance in Technology Adoption: A Study of Smart Home Users

Cognitive Dissonance in Technology Adoption: A Study of Smart Home Users

Explaining Multidimensional Facebook Benefits: A Task-Technology Fit Approach

Author classification using transfer learning and predicting stars in co‐author networks

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