Organizational Performance and Retail Challenges: A Structural Equation Approach

Singh, Rajwinder; Sandhu, H.S.; Metri, Bhimaraya A.; Kaur, Rajinder

doi:10.4236/ib.2011.32022

Cited by 16 publications

(8 citation statements)

References 8 publications

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“…Similarly, the value of NFI = 0.825 was very close to the recommended value, and it has been suggested by many studies that a value greater than 0.8 can be considered as a good fit [75]. Gefen et al [76] and Singh et al [77] recommended that the value of AGFI, CFI and NFI should be more than 0.8. The value of RMSEA has been recommended to be less than 0.8 [78].…”

Section: Structural Model Testingsupporting

confidence: 59%

Understanding Factors Influencing Elderly Diabetic Patients’ Continuance Intention to Use Digital Health Wearables: Extending the Technology Acceptance Model (TAM)

Ahmad

Rasul

Yousaf

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

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Elderly diabetic patients in developed countries have been widely using digital health wearables for many years to manage their diabetes-related health data accurately. To encourage the increased adoption of digital health wearables among elderly diabetic patients in a developing country, Bangladesh, this study investigated the factors that influenced the existing elderly users’ continuance intention to use this technology. The Technology Acceptance Model (TAM) has been used here as a theoretical basis. A model using structural equation modelling was developed for the elderly diabetic patients’ continuance intention to use digital health wearables. Survey-based data were collected in Bangladesh from 223 diabetic patients aged sixty years and older. This study found that all six constructs, namely, perceived usefulness (β = 0.183), perceived ease of use (β = 0.165), perceived irreplaceability (β = 0.138), perceived credibility (β = 0.165), compatibility (β = 0.285) and social influence (β = 0.226) had a positive influence on elderly diabetic patients’ continuance intention to use digital health wearables. Along with the theoretical contributions, the findings of this study can be used by developers of digital health wearables, manufacturers, marketers and health practitioners in developing better strategies to increase the elderly diabetic patients’ continuance intention to use this technology.

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Section: Structural Model Testingsupporting

confidence: 59%

Understanding Factors Influencing Elderly Diabetic Patients’ Continuance Intention to Use Digital Health Wearables: Extending the Technology Acceptance Model (TAM)

Ahmad

Rasul

Yousaf

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

show abstract

“…Finally, the results indicate that the model's Comparative Fit Index (CFI) is 0.811 and RMSEA value is 0.110. These values are acceptable values for both indicators (Broyles et al 2010;Singh et al 2011;Kim and Forsythe 2010). Hence, the overall model is validated.…”

Section: Reliability and Validity Measuresmentioning

confidence: 77%

Factors Influencing the Yemeni Customers’ Intention to Adopt Takaful Products

Echchabi

Ayedh

2015

GADJAH MADA INT. J. BUS.

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The purpose of this study is to examine the Yemeni customers’ intention to adopt Takaful products, and to explore the potential factors that influence their decision. This study applies SEM and one sample t-test to analyse the collected data. The results indicate that among the factors included in this study, only compatibility positively and significantly affects the adoption intention. This is the first study that addresses the adoption of Takaful products in Yemen and the factors that influence it. Furthermore, this study extends the Innovations Diffusion Theory (IDT) by applying it to a different setting.

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“…To meet the accepted standard, the χ 2 /df value should be less than 3 (Hair et al , 2010). The goodness-of-fit index (GFI), adjusted GFI (AGFI), comparative fit index (CFI) and normed fit index (NFI) value should be greater than 0.8 (Bentler and Bonett, 1980; Gefen et al , 2000; Singh et al , 2011; Hair et al , 2010). Browne and Cudeck (1993) also noted that RMSEA should be less than 0.08, and Bagozzi and Yi (1988) noted that PNFI and PGFI should exceed 0.5.…”

Section: Discussionmentioning

confidence: 99%

Purchase intention in social commerce

Chen

Hsiao

2018

LHT

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Purpose With the advent of the digital era, the internet has gradually become an essential factor in people’s lives. Its increasing popularity has also caused e-commerce to thrive, and has increased the power of online marketing. In recent years, social commerce has emerged as a new mode of operation which can be distinguished from traditional e-commerce. The paper aims to discuss these issues. Design/methodology/approach The main purpose of this study is to determine which factors influence a consumer’s behavioral intention in the context of social commerce websites, and to explore overall purchase intentions based on the consumer’s cognitive evaluation. The examination reveals that consumers’ multidimensional perceptions influence both their perceptions of value and their purchase intentions. Other related social awareness factors explored by this study are also shown to influence customers’ purchase intentions. Findings In summary, the results show that perceived value and social awareness factors influence customers’ purchasing decisions and behavioral intentions for different gender. These findings have significant theoretical and managerial implications. Originality/value Social commerce is a subset of e-commerce brought about by the development of social networking. Activities conducted on social networking websites can be considered as a form of social commerce. In Taiwan, social commerce is still in the early stages of development.

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Organizational Performance and Retail Challenges: A Structural Equation Approach

Abstract: ABSTRACT

Cited by 16 publications

References 8 publications

Understanding Factors Influencing Elderly Diabetic Patients’ Continuance Intention to Use Digital Health Wearables: Extending the Technology Acceptance Model (TAM)

Understanding Factors Influencing Elderly Diabetic Patients’ Continuance Intention to Use Digital Health Wearables: Extending the Technology Acceptance Model (TAM)

Factors Influencing the Yemeni Customers’ Intention to Adopt Takaful Products

Purchase intention in social commerce

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