Predicting Internet banking adoption in India: a perceived risk perspective

Roy, Sanjit Kumar; Balaji, M.S.; Kesharwani, Ankit; Sekhon, Harjit

doi:10.1080/0965254x.2016.1148771

Cited by 98 publications

(105 citation statements)

References 77 publications

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“…This line of research tried to answer to the decrease in predictive power of the model over time in specific cases. Its extensive use in different countries, contexts and technologies makes TAM a broadly accepted model to predict technology acceptance (Chuttur, 2009;Roy, Balaji, & Kesharwani, 2016) for mobile technologies as well (Kitchen, Martin, & Che-Ha, 2015). Despite this broad consensus, the information processing approach on which it is grounded received several criticisms, especially because it systematically ignores emotional and experiential influences in technology adoption (Schwarz, 1997;Zanna & Rempel, 1988).…”

Section: Cognitive Approachesmentioning

confidence: 99%

Exploring the adoption process of personal technologies: A cognitive-affective approach

Bettiga

Lamberti

2017

The Journal of High Technology Management Research

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The impact of cognitive and emotional factors on the customer's decision to adopt a new technology has long been at the core of innovation and marketing literature. Today, the proliferation of personal technologies makes the understanding of the adoption process of such innovations a vital issue. This article, moving from long-established technology adoption theories, integrates affective factors to propose a comprehensive framework to interpret and orient innovation and marketing approaches of Companies. To do this, we review a rich literature from the domains of management, information systems, marketing and cognitive psychology, identifying six possible sources of perceived value for personal technologies, hence attitude to adopt them: functional value, monetary value, social value, entertainment value, epistemic value and aesthetic value. After defining and framing them in the extant literature, we discuss how the framework may be adopted in practice to support Companies' strategies in the surprisingly under-explored industry of personal technologies.

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Section: Cognitive Approachesmentioning

confidence: 99%

Exploring the adoption process of personal technologies: A cognitive-affective approach

Bettiga

Lamberti

2017

The Journal of High Technology Management Research

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“…Fakta diatas mencerminkan bahwa penerimaan teknologi Go-Jek masih menjadi kontroversi di masyarakat. Agar aplikasi ini menguntungkan kedua belah pihak (pelanggan dan organisasi), maka perlu untuk menganalisis persepsi yang sebenarnya dan alasan utama kesediaan orang untuk mengadopsi teknologi ini (Roy et al, 2017;Youssef et al, 2017 Beberapa penelitian sebelumnya sudah membahas tentang faktor-faktor yang memengaruhi pengguna teknologi informasi, ada pekerjaan empiris terbatas yang bersamaan menangkap faktor keberhasilan (positif) dan faktor resistensi (negatif) yang mendorong pelanggan untuk mengadopsi teknologi (Roy et al, 2017). Dari premis tersebut bisa diartikan bahwa teknologi belum sepenuhnya diterima karena dalam penggunaannya masih ada faktor penolakan.…”

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Integrasi Aspek Risiko Dalam Model Unified Theory of Acceptance and Usage of Technology Untuk Menganalisis Penerimaan Teknologi Go-Ride

Amrullah¹,

Priyono²

2018

MIX

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This research aims to analyse the acceptance of Go-Ride, motorbike taxi booking application, using Unified Theory of Acceptance and Usage of Technology (UTAUT) model. The uniqueness of this study is that it integrates risk into the UTAUT model. Four variables were used as independent variables including peformance expectancy, effort expectancy, social influence and facilitating condition. Meanwhile, three variables are used as moderating variables: gender, age and experience. University students were selected as respondent because young age people tends to be more risk takers in comparison to the old one. According 151 data samples collected from university students, the results demonstrated that acceptance of Go-Ride technology influenced by four factors: performance expectancy, effort expectancy, social influence and behavioral intention On the otherhand, performance risk, financial risk, time risk and facilitating condition do not support to the acceptance and usage of Go-Ride technolology.Keywords : Go-Ride, Go-Jek, Technologt Acceptance, UTAUT, Perceived Risk Abstraksi: Penelitian ini bertujuan untuk menganalisis penerimaan teknologi Go-Ride dengan menggunakan model Unified Theory of Acceptance and Usage of Technology (UTAUT) yang diintegrasikan dengan persepsi risiko (perceived risk). Model UTAUT mengidentifikasi empat faktor pendorong intention dan yaitu peformance expectancy, effort expectancy, social influence dan facilitating condition serta gender, age dan experience sebagai variabel moderasinya. Penelitian sebelumnya mengintegrasikan model UTAUT dengan perceived risk yang dibagun oleh tujuh variabel risiko. Pada penelitian ini faktor risiko yang ditambahkan hanya tiga variabel yaitu performance risk, financial risk, dan time risk. Berdasarkan 151 sampel yang diambil dari mahasiswa disimpulkan bahwa model penerimaan teknologi Go-Ride ini, terdapat 4 faktor yang memengaruhi penerimaan yaitu performance expectancy, effort expectancy, social influence dan behavioral intention. Sedangkan performance risk, financial risk, time risk dan facilitating condition tidak tebukti berpengaruh terhadap penerimaan dan penggunaan teknologi Go-Ride Kata Kunci : Go-Ride, Go-Jek, Penerimaan Teknologi, UTAUT, Perceived Risk PENDAHULUANPada beberapa tahun terakhir perkembangan Teknologi Informasi (TI) cukup membawa perubahan. Seiring perkembangan zaman, kegiatan masyarakat juga tidak terlepas dari penggunaan teknologi informasi. Teknologi informasi menjadi komponen yang penting dalam kehidupan sehari-hari. Internet, salah satu buah dari perkembangan teknologi informasi, cukup bermanfaat dalam menunjang kinerja organisasi dan bisnis.

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“…According to Lu et al (2005), internet services are those services which can be accessed through mobile technology. The widely used examples of digital services are purchasing of product or services (Hsu et al, 2014), access to bank through internet (Ndubisi and Sinti, 2006;Roy et al, 2017;Claessens et al, 2002;Calisir and Gumussoy, 2008), etc. The categorization of digital services has been done by Froehle and Roth (2004) based on the interaction among the service provider, consumer and internet.…”

Section: Digital Servicesmentioning

confidence: 99%

When the mobile app is free, the product is your personal data

Polykalas

Prezerakos

2019

DPRG

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Purpose-Mobile devices (smartphones, tables etc.) have become the de facto means of accessing the internet. While traditional Web browsing is still quite popular, significant interaction takes place via native mobile apps that can be downloaded either freely or at a cost. This has opened the door to a number of issues related to privacy protection since the smartphone stores and processes personal data. The purpose of this paper is to examine the extent of access to personal data, required by the most popular mobile apps available in Google Play store. In addition, it is examined whether the relevant procedure is in accordance with the provisions of the new EU Regulation. Design/methodology/approach-The paper examines more than a thousand mobile apps, available from the Google Play store, with respect to the extent of the requests for access to personal data. In particular, for each available category in Google Play store, the most popular mobile apps have been examined both for free and paid apps. In addition, the permissions required by free and paid mobile apps are compared. Furthermore, a correlation analysis is carried out aiming to reveal any correlation between the extent of required access to personal data and the popularity and the rating of each mobile app. Findings-The findings of this paper suggest that the majority of examined mobile apps require access to personal data to a high extent. In addition, it is found that free mobile apps request access to personal data in a higher extent compared to the relevant requests by paid apps, which indicates strongly that the business model of free mobile apps is based on personal data exploitation. The most popular types of access permissions are revealed for both free and paid apps. In addition, important questions are raised in relation to user awareness and behavior, data minimization and purpose limitation for free and paid mobile apps. Originality/value-In this study, the process and the extent of access to personal data through mobile apps are analyzed. Although several studies analyzed relevant issues in the past, the originality of this research is mainly based on the following facts: first, this work took into account the recent Regulation of the EU in relation to personal data (GDPR); second, the authors analyzed a high number of the most popular mobile apps (more than a thousand); and third, the authors compare and analyze the different approaches followed between free and paid mobile apps.

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Predicting Internet banking adoption in India: a perceived risk perspective

Abstract: This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Cited by 98 publications

References 77 publications

Exploring the adoption process of personal technologies: A cognitive-affective approach

Exploring the adoption process of personal technologies: A cognitive-affective approach

Integrasi Aspek Risiko Dalam Model Unified Theory of Acceptance and Usage of Technology Untuk Menganalisis Penerimaan Teknologi Go-Ride

When the mobile app is free, the product is your personal data

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