Adoption of ERP system: An empirical study of factors influencing the usage of ERP and its impact on end user

doi:10.1016/J.IIMB.2015.04.008

Home
/
Papers
/
Adoption of ERP system: An empirical study of factors influencing the usage of ERP and its impact on end user

Journal Article•DOI•

Adoption of ERP system: An empirical study of factors influencing the usage of ERP and its impact on end user

Christy Angeline Rajan¹, Rupashree Baral¹•Institutions (1)

Indian Institute of Technology Madras¹

01 Jun 2015-Iimb Management Review (Elsevier)-Vol. 27, Iss: 2, pp 105-117

TL;DR: The results of the analysis suggest that computer self-efficacy, organizational support, training, and compatibility have a positive influence on ERP usage which in turn has significant influence on panoptic empowerment and individual performance.

read less

Abstract: Complex information systems like the ERP integrate the data of all business areas within the organization. The implementation of ERP is a difficult process as it involves different types of end users. Based on literature, we proposed a conceptual framework and examined it to find the effect of some of the individual, organizational, and technological factors on the usage of ERP and its impact on the end user. The results of the analysis suggest that computer self-efficacy, organizational support, training, and compatibility have a positive influence on ERP usage which in turn has significant influence on panoptic empowerment and individual performance.

...read moreread less

Citations

PDF

Open Access

More filters

Journal Article•DOI•

Understanding the Blockchain technology adoption in supply chains-Indian context

[...]

Sachin S. Kamble¹, Angappa Gunasekaran², Himanshu Arha¹•Institutions (2)

National Institute of Industrial Engineering¹, California State University, Bakersfield²

03 Apr 2019-International Journal of Production Research

TL;DR: This paper develops, and statistically validates a model for understanding the user perceptions on BT adoption, based on the integration of three adoption theories- technology acceptance model (TAM), technology readiness index (TRI), and the theory of planned behaviour (TPB).

...read moreread less

Abstract: Blockchain technology (BT) is expected to bring a revolutionary paradigm shift in the manner the transactions are carried in the supply chains. BT provides better visibility and transparency by rem...

...read moreread less

480 citations

Journal Article•DOI•

Enterprise resource planning adoption and satisfaction determinants

[...]

Carlos Costa¹, Edgar Ferreira¹, Fernando Bento¹, Manuela Aparicio•Institutions (1)

ISCTE – University Institute of Lisbon¹

01 Oct 2016-Computers in Human Behavior

TL;DR: This study enlightens theory, by contributing to a new model of ERP adoption and satisfaction, and provides relevant evidence to companies involved in the ERP implementation process.

...read moreread less

114 citations

Cites background or methods or result from "Adoption of ERP system: An empirica..."

...UN CO RR EC TE D PR OO F model for IS adoption was considered to have a good fit explaining user's adoption of IS and being a robust method for study's support (Rajan & Baral, 2015; Sternad & Bobek, 2013; Vathanophas & Stuart, 2009; Youngberg et al., 2009)....
[...]
...These results are all consistent with Sternad and Bobek (2013) and Rajan and Baral (2015) findings in ERP adoption studies about these three hypotheses....
[...]
...Consistent with Rajan and Baral (2015) findings, Pan & Jang's (2008) TOE study found a strong influence of top management support in ERP user's adoption behaviour....
[...]
...So, regardless the study objective (adoption or success), this construct is found to be one of the main influencers of user's behaviour intention and perception of the ease of use (Chien & Tsaur, 2007; Rajan & Baral, 2015; Sternad & Bobek, 2013; Tsai et al., 2012)....
[...]
...…main drivers that led users to adopt a certain ERP system (e.g., Bradley, 2008; Chien & Tsaur, 2007; Gorla, Somers, & Wong, 2010; Nwankpa & Roumani, 2014; Nwankpa, 2015; Pan & Jang, 2008; Rajan & Baral, 2015; Sternad & Bobek, 2013; Tsai, Lee, Shen, & Lin, 2012; Youngberg, Olsen, & Hauser, 2009)....
[...]

Journal Article•DOI•

Cloud computing (SaaS) adoption as a strategic technology: results of an empirical study

[...]

Pedro R. Palos-Sanchez¹, Francisco J. Arenas-Márquez², Mariano Aguayo-Camacho²•Institutions (2)

University of Extremadura¹, University of Seville²

19 Jun 2017-Mobile Information Systems

TL;DR: The results of this study reflect what critical factors should be considered and how they are interrelated and show the organizational demands that must be considered by those companies wishing to implement a real management model adopted to the digital economy, especially those related to cloud computing.

...read moreread less

Abstract: The present study empirically analyzes the factors that determine the adoption of cloud computing (SaaS model) in firms where this strategy is considered strategic for executing their activity. A research model has been developed to evaluate the factors that influence the intention of using cloud computing that combines the variables found in the technology acceptance model (TAM) with other external variables such as top management support, training, communication, organization size, and technological complexity. Data compiled from 150 companies in Andalusia (Spain) are used to test the formulated hypotheses. The results of this study reflect what critical factors should be considered and how they are interrelated. They also show the organizational demands that must be considered by those companies wishing to implement a real management model adopted to the digital economy, especially those related to cloud computing.

...read moreread less

53 citations

Cites result from "Adoption of ERP system: An empirica..."

...[64], Rajan and Baral [76], and Gangwar et al....
[...]
...This is in line with Bueno and Salmerón [50], Son et al. [64], Rajan and Baral [76], and Gangwar et al. [45]....
[...]
...The confirmation of hypothesis H11 corroborates prior studies on the influence of training on PU [45, 51, 52, 76], confirming that appropriate training improves organizations’ perception of the advantages that cloud systems bring about in their workers’ performance....
[...]

Journal Article•DOI•

Factors influencing the intention of managers to adopt collaborative robots (cobots) in manufacturing organizations

[...]

Ana Correia Simões¹, António Lucas Soares¹, Ana Cristina Barros¹•Institutions (1)

University of Porto¹

01 Jul 2020-Journal of Engineering and Technology Management

TL;DR: In this article, the authors identified and characterized the factors influencing managers' intentions to adopt collaborative robots (cobots) in manufacturing companies based on a conceptual framework that integrates three technology adoption theories (Diffusion of innovation, technology-organization-environment and Institutional theory).

...read moreread less

48 citations

Journal Article•DOI•

How does task-technology fit influence cloud-based e-learning continuance and impact?

[...]

Yung-Ming Cheng

08 Apr 2019-Journal of Education and Training

TL;DR: In this article, the authors examined the role of TTF in students' cloud-based e-learning continuance and evaluated whether TTF affects students' perceived impact on learning of the cloudbased elearning system within the educational institution.

...read moreread less

Abstract: According to expectation–confirmation model (ECM) and task-technology fit (TTF) model, the purpose of this paper is to examine the role of TTF in students’ cloud-based e-learning continuance and evaluate whether TTF affects students’ perceived impact on learning of the cloud-based e-learning system within the educational institution.,Sample data for this study were collected from students enrolled in a comprehensive university in Taiwan. A total of 500 questionnaires were distributed in the campus, and 391 (78.2 percent) usable questionnaires were analyzed using structural equation modeling in this study.,This study’s results verified that both task characteristics and technology characteristics affected students’ perceived TTF, which significantly contributed to their perceived usefulness, confirmation and satisfaction with the cloud-based e-learning system, and these in turn directly or indirectly led to their continuance intention of the system and perceived impact on learning; essentially, the results strongly supported the research model integrating ECM and TTF model via positioning key constructs as the drivers with all hypothesized links being significant.,This study contributes to an understanding of the TTF in explaining students’ cloud-based e-learning continuance that is difficult to explain with only their utilitarian perception of the cloud-based e-learning system, and further places considerably more emphasis upon students’ perceived impact on learning greatly driven by their TTF in the system. Thus, this study’s empirical evidence on incorporating ECM and TTF model can shed light on the outcome for cloud-based e-learning continuance and enhance better understanding of a richer post-adoption model.

...read moreread less

48 citations

1
2
3
4
…
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38

Collapse

References

PDF

Open Access

More filters

Journal Article•DOI•

Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

[...]

Claes Fornell¹, David F. Larcker²•Institutions (2)

University of Michigan¹, Northwestern University²

01 Feb 1981-Journal of Marketing Research

TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...

...read moreread less

Abstract: The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addit...

...read moreread less

56,555 citations

"Adoption of ERP system: An empirica..." refers background in this paper

...The average variance extracted (AVE) for each factor should exceed 0.5 (Fornell & Larcker, 1981)....
[...]
...To establish discriminant validity, the square root of a construct’s AVE must be larger than the inter-construct correlations (Fornell & Larcker, 1981)....
[...]

Perceived Usefulness, Perceived Ease of Use, and User

[...]

Perceived Usefulness, Fred D. Davis

01 Jan 1989

TL;DR: Regression analyses suggest that perceived ease of use may actually be a causal antecdent to perceived usefulness, as opposed to a parallel, direct determinant of system usage.

...read moreread less

40,975 citations

"Adoption of ERP system: An empirica..." refers background or methods or result in this paper

...Technology acceptance model scales of perceived usefulness and perceived ease of use were measured using four items adapted from Davis (1989) and Davis et al. (1989)....
[...]
...These include the technology acceptance model (Davis, 1989), computer selfefficacy (Compeau & Higgins, 1995), tasketechnology fit (Goodhue and Thompson, 1995) and theory of planned behaviour (Ajzen, 1985)....
[...]
...This is consistent with the findings of Davis (1989)....
[...]
...Perceived ease of use is defined as “the degree to which a person believes that using the system will be free of effort” (Davis, 1989)....
[...]
...Davis (1989) defined perceived usefulness as “the degree to which a person believes that using a particular system would enhance his or her job performance”....
[...]

Journal Article•DOI•

Perceived usefulness, perceived ease of use, and user acceptance of information technology

[...]

Fred D. Davis¹•Institutions (1)

University of Michigan¹

01 Sep 1989-Management Information Systems Quarterly

TL;DR: In this article, the authors developed and validated new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance.

...read moreread less

Abstract: Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions of these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and streamlined, resulting in two six-item scales with reliabilities of .98 for usefulness and .94 for ease of use. The scales exhibited hgih convergent, discriminant, and factorial validity. Perceived usefulness was significnatly correlated with both self-reported current usage r = .63, Study 1) and self-predicted future usage r = .85, Study 2). Perceived ease of use was also significantly correlated with current usage r = .45, Study 1) and future usage r = .59, Study 2). In both studies, usefulness had a signficnatly greater correaltion with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecdent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.

...read moreread less

40,720 citations

Book•

Diffusion of Innovations

[...]

Everett M. Rogers

01 Jan 1962

TL;DR: A history of diffusion research can be found in this paper, where the authors present a glossary of developments in the field of Diffusion research and discuss the consequences of these developments.

...read moreread less

Abstract: Contents Preface CHAPTER 1. ELEMENTS OF DIFFUSION CHAPTER 2. A HISTORY OF DIFFUSION RESEARCH CHAPTER 3. CONTRIBUTIONS AND CRITICISMS OF DIFFUSION RESEARCH CHAPTER 4. THE GENERATION OF INNOVATIONS CHAPTER 5. THE INNOVATION-DECISION PROCESS CHAPTER 6. ATTRIBUTES OF INNOVATIONS AND THEIR RATE OF ADOPTION CHAPTER 7. INNOVATIVENESS AND ADOPTER CATEGORIES CHAPTER 8. DIFFUSION NETWORKS CHAPTER 9. THE CHANGE AGENT CHAPTER 10. INNOVATION IN ORGANIZATIONS CHAPTER 11. CONSEQUENCES OF INNOVATIONS Glossary Bibliography Name Index Subject Index

...read moreread less

38,750 citations

Journal Article•DOI•

Multivariate Data Analysis

[...]

Joseph F. Hair

01 Jan 1973

TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.

...read moreread less

Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each. Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations. Table of Contents 1. Introduction. I. PREPARING FOR A MULTIVARIATE ANALYSIS. 2. Examining Your Data. 3. Factor Analysis. II. DEPENDENCE TECHNIQUES. 4. Multiple Regression. 5. Multiple Discriminant Analysis and Logistic Regression. 6. Multivariate Analysis of Variance. 7. Conjoint Analysis. 8. Canonical Correlation Analysis. III. INTERDEPENDENCE TECHNIQUES. 9. Cluster Analysis. 10. Multidimensional Scaling. IV. ADVANCED AND EMERGING TECHNIQUES. 11. Structural Equation Modeling. 12. Emerging Techniques in Multivariate Analysis. Appendix A: Applications of Multivariate Data Analysis. Index.

...read moreread less

37,124 citations