Factors Affecting Information Technology Usage: A Meta-Analysis of the Empirical Literature

Mahmood, Mo Adam; Hall, Laura; Swanberg, Daniel Leonard

doi:10.1207/s15327744joce1102_02

Cited by 130 publications

(82 citation statements)

References 53 publications

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“…Mathieson et al (2001) find that Bperceived user resources^including access to computing resources, personal knowledge, time, and availability of support are significant predictors of system usage. These findings are consistent with Mahmood and Swanberg's (2001) concept of Borganizational support^which emphasizes both enablement and encouragement by management, and with Anakwe et al's (2000) analysis of organizational support and IT usage. Training efforts are a particularly important element of user enablement.…”

Section: User Enablement and Management Support Positively Influence supporting

confidence: 89%

B2B relationship calculus: quantifying resource effects in service-dominant logic

deLeon

Chatterjee²

2015

J. of the Acad. Mark. Sci.

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Increasingly, knowledgeable business-to-business (B2B) customers and evolving customer needs are leading to seismic shifts in vendor-client interactions. Across industries, sellers are changing their business models from a simple goods orientation to a hybrid goods-services model, placing greater emphasis on delivering complete customer solutions. In such an environment, companies must find ways to prioritize investments in resource development. The servicedominant (S-D) logic framework offers significant insights into this challenge; however, these effects have not been tested quantitatively. This study addresses that gap, examining the influence of various seller resources on buyer satisfaction. An empirical analysis of buying organizations that purchased and implemented business intelligence systems finds that Baugmented^operant resources that the buyers ascribe to the software's sellers-resources that go above and beyond expectations-are the most significant predictors of both successful technology assimilation and overall customer relationship quality. In particular, an augmented operant resource reflecting a seller's ability to see value creation opportunities from the buyer's perspective (value mindset) has up to three times the effect on relationship satisfaction as Bcore^operant resources such as product-specific expertise or basic interpersonal service skills. These results can help sellers prioritize resource investments. Lusch 2004, 2008b) casts this question in terms of developing and applying Bresources^to create value. According to S-D logic, value is not intrinsic to the seller's Boperand resources^or tangible offerings such as physical products or software coupled with training or integration. Rather, sellers deliver value to buyers by applying operant resources such as skills and knowledge to the operand resources. Vargo and Lusch (2008a) maintain that the valuemaximizing application of resources-more than the specific goods-services offering mix-should be a company's primary marketing concern. To date, though, little quantitative analysis has been conducted to test the impact of these resources on value. Research has focused instead on classification systems (Madhavaram and Hunt 2008), consumer behavior (Arnould 2005), and conceptual integration into existing knowledge domains such as innovation theory (Michel et al. 2008). Recent qualitative studies have highlighted the importance of sellers customizing offerings and communicating value in customer-specific terms (Tuli et al. 2007;Ulaga and Reinartz 2011). But, again, the impact of such capabilities has yet to be assessed through quantitative analysis. Sci. (2017) 45:402-427 DOI 10.1007 Given the paradigmatic shift proposed by S-D logic, the first objective of our study is to quantitatively assess the extent to which both operant and operand resources affect value. We measure value in terms of project outcomes and customer relationship satisfaction. To do this we quantify relative effects both within operant resources, and betwe...

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Section: User Enablement and Management Support Positively Influence supporting

confidence: 89%

B2B relationship calculus: quantifying resource effects in service-dominant logic

deLeon

Chatterjee²

2015

J. of the Acad. Mark. Sci.

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“…Educational level, like other individual factors, has been studied as an antecedent of PU or PEOU (Agarwal & Prasad, 1999) and as a moderator that affects the relationship between main determinates and behavioural intention (Burton- Jones & Hubona, 2006). In particular, educational level was found to influence the relationships between PEOU, PU, SN and BI (Porter & Donthu, 2006;Rogers, 2003;Sun & Zhang, 2006;Zakaria, 2001;Mahmood, Hall, & Swanberg, 2001;Burton-Jones & Hubona, 2006). found a positive correlation between the level of education and PU, Similarly, Burton- Jones and Hubona (2006) suggested that higher education level leads to positive association with PU and those users are less sensitive to PEOU since it will reduce the computer anxiety and improve the overall attitude.…”

Section: Educational Levelmentioning

confidence: 97%

Technology, Demographic Characteristics and E-Learning Acceptance: A Conceptual Model Based on Extended Technology Acceptance Model

Tarhini¹,

Elyas²,

Akour

et al. 2016

HES

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The main aim of this paper is to develop an amalgamated conceptual model of technology acceptance that explains how individual, social, cultural and organizational factors affect the students' acceptance and usage behaviour of the Web-based learning systems. More specifically, the proposed model extends the Technology Acceptance Model (TAM) to include four constructs namely, Quality of work life; Social Norm; Facilitating Conditions and self-efficacy. In addition, Individual differences-namely, age, gender, and experience-are hypothesized to moderate the effects of these constructs on behavioral intention and technology use. A comprehensive understanding of this model will provide valuable insights into the factors that influence the acceptance or resistance of web-based learning system by intended users and offers opportunities for future research in understanding the acceptance of technology. Further, understanding these variables is helpful for instructors to design meaningful educational activities to promote student knowledge construction and make learning more effective and appealing.

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“…Numerous studies have confirmed that individual staff characteristics have a crucial role to play in the implementation of new technologies in an organisation (Venkatesh & Morris, 2000;Mahmood, Hall & Swanberg, 2001;Davenport, 2013).…”

Section: Human Capitalmentioning

confidence: 99%

An explanatory model of the organizational factors that explain the adoption of E-business

García-Moreno

Moreno

Sánchez

et al. 2016

JIEM

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Abstract:Purpose: To describe the factors that facilitate the adoption of e-business in firms. To go in deep on the factors, resources and capabilities that need to be present in those firms seeking to improve their levels of e-business adoption.Design/methodology/approach: Analysis of the literature involving the main theories on business administration, and more specifically, on those related to technology innovation (TI) and information systems (IS), as applicable to the organizational factors that explain the adoption of e-business.Findings: It identifies three main sources of influence: a first group covers the characteristics of the actual firm, which refer to the organisation's specific features: firm size, the backing of top management, expected benefit, age, the level of human capital, and international projection. A second group of factors includes technology-related characteristics. The third group contains all those aspects in the environment that may affect the firm's attitude to e-business. Research limitations/implications:The chosen variables play significant role following a review of the studies on the subject, but not all potential ones have been included. The variables have been chosen in view of the large number of studies that have reported conclusive results.-547-Journal of Industrial Engineering and Management -http://dx

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Factors Affecting Information Technology Usage: A Meta-Analysis of the Empirical Literature

Cited by 130 publications

References 53 publications

B2B relationship calculus: quantifying resource effects in service-dominant logic

B2B relationship calculus: quantifying resource effects in service-dominant logic

Technology, Demographic Characteristics and E-Learning Acceptance: A Conceptual Model Based on Extended Technology Acceptance Model

An explanatory model of the organizational factors that explain the adoption of E-business

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