Exploratory Study of the Adoption and Use of the Smartphone Technology in Emerging Regions: Case of Saudi Arabia

Aldhaban, Fahad

doi:10.15760/etd.2647

Cited by 9 publications

(31 citation statements)

References 141 publications

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“…The authors present that performance expectancy construct is the strongest predictor of intention with item loadings between .88 and .94 (Venkatesh et al, 2003). Subsequently, the construct along with UTAUT was tested by many researchers using a variety of applications including E-government services, clinical decision support system, tablet PC, internet, web-based learning environment, social media and smartphone applications (Aldhaban, 2016;Venkatesh et al, 2012;Venkatesh et al, 2016). Aldhaban (2016) reports that the performance expectancy construct shows the standard regression weight value of 0.339 to determine the intention to use the smartphone.…”

Section: Output Quality and Perceived Usefulnessmentioning

confidence: 99%

“…Subsequently, the construct along with UTAUT was tested by many researchers using a variety of applications including E-government services, clinical decision support system, tablet PC, internet, web-based learning environment, social media and smartphone applications (Aldhaban, 2016;Venkatesh et al, 2012;Venkatesh et al, 2016). Aldhaban (2016) reports that the performance expectancy construct shows the standard regression weight value of 0.339 to determine the intention to use the smartphone. The expert panel of our qualitative study selected this construct as the number 10 independent variable in order of rank to be included in the research model.…”

Section: Output Quality and Perceived Usefulnessmentioning

confidence: 99%

“…This construct was also validated from an organizational context by to test an ERP system acceptance. Aldhaban (2016) used this construct to test smartphone acceptance but it was not supported by his construct. The reason might be that smartphone use is very, very common, and does not need any technical support from the vendors.…”

Section: Moddymentioning

confidence: 99%

“…It is reported that in IS discipline, the comparison between early and later respondents is widely used (Sivo et al, 2006;Aldhaban, 2016). Originally, this strategy was proposed by Armstrong and Overton (1977).…”

Section: Non-response Error: Wave Analysismentioning

confidence: 99%

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Exploring the Factors Influencing Big Data Technology Acceptance

Rahman¹

2000

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The success of new technology depends on user acceptance. Therefore, discovering the antecedents of technology use is pivotal to overcoming the lack of user acceptance in the field of technology adoption. Factors of critical technological capability, in particular, are overlooked and largely neglected in the literature. Accordingly, the body of literature on the field of technology adoption is inconclusive as to which technological capability factors influence technology acceptance. Big Data has received great attention in academic literature and industry papers. Most of the experiments and studies focused on publishing results of big data technologies development, machine learning algorithms, and data analytics. To the best of our knowledge, there is not yet any comprehensive empirical study in the academic literature on big data technology acceptance. This research makes an attempt to identify factors influencing big data technology acceptance from an industrialorganizational context. With the help of existing technology acceptance theories, literature studies, industry technical papers, and vendor publications on data management technologies ranging from conventional data warehousing to big data storage technologies (e.g., Hadoop Distributed File System), 32 factors have been identified for use in the qualitative study of this research. By using prominent qualitative research methods including focus groups and one-on-one interviews, this research has identified 12 factors as possible antecedents of ii perceived usefulness and intention to use big data technology. These 12 factors include scalability, data storage and processing capabilities, functionality, performance expectancy, security and privacy considerations, reliability, data analytics capability, flexibility, facilitating conditions, output quality, required skills and training, and costeffectiveness. The qualitative studies were conducted using industry experts with experience in big data technologies as well as traditional data management technologies. To further validate the factors identified by the qualitative study, a quantitative model is developed. The theoretical foundation of this model is drawn from the Technology Acceptance Model (TAM) developed by Fred Davis (1993). This model allows plugins of external factors to its latent constructs of perceived usefulness (PU) and perceived ease of use (PEOU). Primary data for the quantitative study were collected from big data (Hadoop User Groups) users in the United States who work in different industries including software and internet services, financial services, healthcare, consulting and professional services, telecommunications, manufacturing, retail, marketing, and logistics. The structural equation modeling (SEM) software, AMOS, was used for empirical verification and validation of our proposed model using 349 survey responses. The statistical results of this model provide a compelling explanation of the relationships among the antecedent variables and the dependent variables. The analysis o...

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Section: Output Quality and Perceived Usefulnessmentioning

confidence: 99%

Section: Output Quality and Perceived Usefulnessmentioning

confidence: 99%

Section: Moddymentioning

confidence: 99%

Section: Non-response Error: Wave Analysismentioning

confidence: 99%

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Exploring the Factors Influencing Big Data Technology Acceptance

Rahman¹

2000

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“…One such extension is UTAUT2 which focuses on adoption in the consumer context by adding three new concepts: hedonic motivation (enjoyment), price value, and habit [225]. Information systems [199], [221] Influenced by Aldhaban (2016) [226]…”

Section: Task-technology Fitmentioning

confidence: 99%

Determinants of Student Information Technology Adoption

VanDerSchaaf¹

2000

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Innovating service delivery in higher education is central to supporting institutional and societal goals of increasing the numbers of college graduates and for transforming higher education institutions to center on the needs of today's students. Within this context, technology plays a critical role. This research seeks to contribute to institutional, academic and educational technology sector efforts to dramatically enhance service quality, in support of improving undergraduate student outcomes (student success), by identifying the determinants of student information technology adoption and removing barriers to accessing higher education. Using a mixed-methods and empirical approach based on the Unified Theory of Acceptance and Use of Technology (UTAUT), after an extensive literature review, individual interviews and focus groups, a web-based survey was administered to undergraduate students at Portland State University in Portland, Oregon to learn about their experiences with myPSU. myPSU is a web-based digital services platform (available as a native mobile application and website) that aids students in accessing services and resources critical to maintaining their enrollment. Structural Equation Modeling (SEM), including Principal Components Analysis (PCA), was used to analyze 1,841 valid survey responses and derive statistical results.The findings suggest that effort expectancy and social influence stand out as critical influences on behavioral intention to adopt myPSU, and new to i iii

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Proposal of a guide for talent evaluation and management based on a qualitative and three-staged approach

Mohammed

Hafeez‐Baig

Gururajan

2020

JARHE

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PurposeThe key objective of the study is to understand the best processes that are currently used in managing talent in Australian higher education (AHE) and design a quantitative measurement of talent management processes (TMPs) for the higher education (HE) sector.Design/methodology/approachThe three qualitative multi-method studies that are commonly used in empirical studies, namely, brainstorming, focus group discussions and semi-structured individual interviews were considered. Twenty-three individuals from six Australian universities participated in this study.FindingsThe qualitative study explored three key themes and ten subthemes of TMPs that are used in AHE. These were: (1) talent attraction, (2) talent development and (3) talent retention.Research limitations/implicationsThis study only targeted one country (Australia) and one sector (HE).Practical implicationsThis study offers three major contributions as follows: theoretical, practical and policy aspects. Theoretically, the study provides a value-add to Talent Management (TM) theory through designing a guide (conceptual model) of TMPs for the HE sector. Practically, it collects original qualitative data regarding TM in the HE domain. From a policy point of view, this study adds more debate around adding new ideas to Australian education strategic plans for HE.Originality/valueThis study has a unique methodology because of strengthening the effect of an in-depth case study. For instance, two different techniques were used for data analysis for the same research objective as follows: (1) both manual methods and content analysis software (NVivo 11) and (2) the three-stage approach. Using these techniques for the same purpose in one study can provide greater flexibility to examine the relationship between theory and data.

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Exploratory Study of the Adoption and Use of the Smartphone Technology in Emerging Regions: Case of Saudi Arabia

Cited by 9 publications

References 141 publications

Exploring the Factors Influencing Big Data Technology Acceptance

Exploring the Factors Influencing Big Data Technology Acceptance

Determinants of Student Information Technology Adoption

Proposal of a guide for talent evaluation and management based on a qualitative and three-staged approach

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