Development of application-based mobile-service trust and online trust transfer: an elaboration likelihood model perspective

Nel, Jacques; Boshoff, Christo

doi:10.1080/0144929x.2017.1296493

Cited by 31 publications

(18 citation statements)

References 63 publications

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“…The study used scales adapted from previous studies (Agarwal & Karahanna, 2000; Chen et al, 2009; Lin, 2009; Morgan‐Thomas & Veloutsou, 2013; Nel & Boshoff, 2017). The scales are slightly modified to fit in with the context of this study.…”

Section: Methodsmentioning

confidence: 99%

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Role of cognitive absorption in building user trust and experience

Balakrishnan

Dwivedi

2021

Psychology and Marketing

121

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The growth of artificial intelligence (AI) and its applications in business has proliferated in recent years. Businesses have started adopting various technology practices relevant to automation and AI, and research investigating this phenomenon is becoming increasingly important. Taking this as a cue, the present research investigates the effect of human‐to‐machine interaction and human‐to‐human interaction towards cognitive absorption and its subsequent effect on trust, experience, and continuation intention in the context of services. The study built a 3 × 3 factorial design with automated chatbots (machine interaction) and service executives (human interaction) used as a stimulus in the experiment. Data collected from 410 respondents were analyzed using structural equation modeling to test the proposed hypotheses. The findings indicated that human‐to‐machine interaction influences cognitive absorption more positively compared to human‐to‐human interactions. The study results also provide evidence for the role of the trust, experience, and technology continuation intention in a technology background rooted in human‐machine interactions. The present study adds a valuable contribution to the existing literature relevant to human‐to‐machine interaction, cognitive absorption, trust, experience, and continuation intention. The study also provides valuable inputs to technology and marketing managers.

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Section: Methodsmentioning

confidence: 99%

“…The scale for user experience is derived from the study by Morgan‐Thomas and Veloutsou (2013), which consists of six items. The scale for user trust is derived from Nel and Boshoff (2017). The scale for technology continuation intention is derived from previous studies (Bhattacherjee et al, 2008; Bhattacherjee, 2001b; Chen et al, 2009).…”

Section: Methodsmentioning

confidence: 99%

Role of cognitive absorption in building user trust and experience

Balakrishnan

Dwivedi

2021

Psychology and Marketing

121

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“…In the prior studies on the intra-channel trust transfer, Lien (2014) found that patients' trust in head hospital has a positively effect on their trust in affiliated hospitals [53]. Nel and Boshoff (2017) suggested that the online service trust positively influenced the application-based mobile services trust [40]. Sun et al (2014) noted that perceived operational consistency and similarity of the linked organizations favors the trust migrates from trusted to unfamiliar entity [52].…”

Section: Mohc Platform's Initial Trust Formation Processmentioning

confidence: 99%

“…In the travel website context, Tseng and Wang (2016) discovered the effect of perceived risk on individual information adoption behavior based on ELM [39]. Nel and Boshff (2017) employed ELM to examine application-based mobile services trust [40]. Based on ELM, Chen et al (2018) investigated the influence factors of mobile health applications' continuance using intention in the developing markets [41].…”

Section: Elaboration Likelihood Modelmentioning

confidence: 99%

Examining User’s Initial Trust Building in Mobile Online Health Community Adopting

Cao

Zhang

et al. 2020

IJERPH

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Due to the high perceived risk, it is critical to foster users’ initial trust in the promotion of mobile online health community (MOHC) adoption. The present study focused on the role of two different trust elements and examined the initial trust building process based on elaboration likelihood model and trust transfer theory. The results indicated that initial trust in MOHC context was composed of two interrelated components: health service provider (doctor) and underlying technology (MOHC platform). Especially, the initial trust in MOHC platform exerted greater effects on adopting intention. Both performance-based cue (doctors’ information quality and interaction quality) and transfer-based cue (trust in the offline doctors’ health service) positively shaped the initial trust in doctor. Meanwhile, only the performance-based cue (MOHC platform’s information quality and service quality) has significant positive association with initial trust in MOHC platform. However, interpersonal recommend is insignificantly related to the initial trust in doctor. Trust in the mobile internet service is insignificantly related to the initial trust in MOHC platform.

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“…Mobile advertising technology had transformed the principles on which marketers and practitioners communicate to their consumers (Grant & O'Donohoe, 2007;Nah, Siau, & Sheng, 2005;Nel & Boshoff, 2017). Recently, the rapid pace of smart phones and other similar technological gadgets adoption has undoubtedly created a land of opportunities for marketers and advertisers to build long term relations with their customers through this highly customized and interactive platform (Pagani & Malacarne, 2017;Sultan & Rohm, 2005).…”

Section: Introductionmentioning

confidence: 99%

Mobile Advertising Technology Acceptance Model (M-TAM): An Extension of TAM in Mobile Marketing Context

Haq¹,

Ghouri²

2018

SAJMS

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Mobile advertising technology has revolutionized the way companies interact with their customers. This has offered both marketers and consumers the advantages of convenience, flexibility, and ubiquity etc. Irrespective of the advantages of mobile advertising technology, it is not easy for marketers and researchers to form adoptable mobile advertising technology for consumers without being in a position to precisely foresee consumers' behavioural outcome. Additionally, this field appears to be scant in academic researches. The current study was initiated to empirically examine the influence of m-advertising technological factors (i.e., ease of use, usefulness, personalization and credibility) on consumers' adoption behaviour. These factors were also mediated by emotional value in order to influence end users' adoption behaviour. A quantitative approach based upon a cross-sectional administered survey was employed to collect the data from university going student as respondents. The Partial Least Square (PLS) two step path modelling was used through SmartPLS 3 to test the direct and indirect hypotheses. The results indicate that mobile advertising technology factors (i.e., ease of use, usefulness, personalization and credibility) directly and indirectly impact consumers' adoption behaviour through emotional value. Based on the findings of the study, theoretical and practical contributions along with certain limitations and future researches recommendation are discussed consequentially.

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Development of application-based mobile-service trust and online trust transfer: an elaboration likelihood model perspective

Cited by 31 publications

References 63 publications

Role of cognitive absorption in building user trust and experience

Role of cognitive absorption in building user trust and experience

Examining User’s Initial Trust Building in Mobile Online Health Community Adopting

Mobile Advertising Technology Acceptance Model (M-TAM): An Extension of TAM in Mobile Marketing Context

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