TEMPORARY REMOVAL: Adoption of cloud based Internet of Things in India: A multiple theory perspective

Mital, Monika; Chang, Victor; Choudhary, Praveen K.; Pani, Ashis K.; Sun, Zhili

doi:10.1016/j.ijinfomgt.2016.02.011

Cited by 17 publications

(9 citation statements)

References 59 publications

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“…The positive relationship between PE and AI is consistent with the results of Chuah and Rauschnabel [33], which confirmed that PE has a direct positive effect on the AI of wearable technologies. Likewise, the positive relationship between PUS and AI was confirmed by Mital, Chang, Choudhary, Pani and Sun [41], which asserted that the concept of PU is a primary driver of users’ AI for cloud computing-based IoT services. The positive correlation between DK and AI was validated as being significant.…”

Section: Discussionmentioning

confidence: 89%

“…As the TAM-related models have demonstrated robustness and effectiveness [33,34,35,36,37,38,39], researchers often employ such models as theoretical foundations for analyzing adoptive behaviors of novel technology, including mobile banking services [10], smartphone adopting behavior [7], mobile learning [40], smart watch [33], and cloud computing technology [41]. For example, Palau-Saumell, et al [42] utilize the SEM method to examine the adoption of mobile applications for restaurant searches and reservations by different level of users’ age based on the UTAUT2 model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Theorists and behavior analysts have broadly explored user behaviors and formed a consensus opinion on adoptive intentions that can be regarded as a dominant factor in the use of IT and technology products [7,29,33,41,48,75]. Adopting intention refers to the degree to which a person has formed conscious plans to perform or not perform some specified future behavior(s) [76].…”

Section: Literature Reviewmentioning

confidence: 99%

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An Exploration and Confirmation of the Factors Influencing Adoption of IoT-Based Wearable Fitness Trackers

Kao

Nawata

Huang

2019

IJERPH

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In recent years, IoT (Internet of Things)-based smart devices have penetrated a wide range of markets, including connected health, smart home, and wearable devices. Among the IoT-based smart devices, wearable fitness trackers are the most widely diffused and adopted IoT based devices. Such devices can monitor or track the physical activity of the person wearing them. Although society has benefitted from the conveniences provided by IoT-based wearable fitness trackers, few studies have explored the factors influencing the adoption of such technology. Furthermore, one of the most prevalent issues nowadays is the large attrition rate of consumers no longer wearing their device. Consequently, this article aims to define an analytic framework that can be used to explore the factors that influence the adoption of IoT-based wearable fitness trackers. In this article, the constructs for evaluating these factors will be explored by reviewing extant studies and theories. Then, these constructs are further evaluated based on experts’ consensus using the modified Delphi method. Based on the opinions of experts, the analytic framework for deriving an influence relationship map (IRM) is derived using the decision-making trial and evaluation laboratory (DEMATEL). Finally, based on the IRM, the behaviors adopted by mass customers toward IoT-based wearable fitness trackers are confirmed using the partial least squares (PLS) structural equation model (SEM) approach. The proposed analytic framework that integrates the DEMATEL and PLS-SEM was verified as being a feasible research area by empirical validation that was based on opinions provided by both Taiwanese experts and mass customers. The proposed analytic method can be used in future studies of technology marketing and consumer behaviors.

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Section: Discussionmentioning

confidence: 89%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

An Exploration and Confirmation of the Factors Influencing Adoption of IoT-Based Wearable Fitness Trackers

Kao

Nawata

Huang

2019

IJERPH

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“…The adoption of Internet of Things (IoTs), big data, and cloud computing technologies by enterprises has led to better value creation at the customer and enterprise ends (Haile & Altmann, 2016;Mital et al, 2016). Value creation for the customer, called value to the customer (V2C), is the process of understanding customer needs and offering them products while considering the competitive advantage over rival enterprises (Cossío-Silva, Revilla-Camacho, Vega-Vázquez, & Palacios-Florencio, 2015;Verhoef, Kooge, & Walk, 2016).…”

Section: Introductionmentioning

confidence: 99%

Big data reduction framework for value creation in sustainable enterprises

Rehman

Chang

Batool

et al. 2016

International Journal of Information Management

Self Cite

221

146

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Value creation is a major sustainability factor for enterprises, in addition to profit maximization and revenue generation. Modern enterprises collect big data from various inbound and outbound data sources. The inbound data sources handle data generated from the results of business operations, such as manufacturing, supply chain management, marketing, and human resource management, among others. Outbound data sources handle customer-generated data which are acquired directly or indirectly from customers, market analysis, surveys, product reviews, and transactional histories. However, cloud service utilization costs increase because of big data analytics and value creation activities for enterprises and customers. This article presents a novel concept of big data reduction at the customer end in which early data reduction operations are performed to achieve multiple objectives, such as a) lowering the service utilization cost, b) enhancing the trust between customers and enterprises, c) preserving privacy of customers, d) enabling secure data sharing, and e) delegating data sharing control to customers. We also propose a framework for early data reduction at customer end and present a business model for end-toend data reduction in enterprise applications. The article further presents a business model canvas and maps the future application areas with its nine components. Finally, the article discusses the technology adoption challenges for value creation through big data reduction in enterprise applications.

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“…Smart Cities is another revolutionary development of IoT applications that aims to establish an efficient communication and collaboration platform exploiting various information sources to make the cities smarter [6][7][8]. Despite the recent advances towards making IoT a reality, there exist several open issues that require further research and development efforts in order to exploit its full potential [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

An energy-aware service composition algorithm for multiple cloud-based IoT applications

Baker

Asim

Tawfik

et al. 2017

Journal of Network and Computer Applications

173

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TEMPORARY REMOVAL: Adoption of cloud based Internet of Things in India: A multiple theory perspective

Cited by 17 publications

References 59 publications

An Exploration and Confirmation of the Factors Influencing Adoption of IoT-Based Wearable Fitness Trackers

An Exploration and Confirmation of the Factors Influencing Adoption of IoT-Based Wearable Fitness Trackers

Big data reduction framework for value creation in sustainable enterprises

An energy-aware service composition algorithm for multiple cloud-based IoT applications

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