Using organisational information processing maturity ...

Int. J. Information and Decision Sciences, Vol. 1, No. 2, 2008

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Using organisational information processing maturity as a predictor of information technology adoption James E. Yao*, Zhongxian Wang and Ruben Xing Department of Management and Information Systems, Montclair State University, 1 Normal Ave., Montclair, New Jersey 07043, USA E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] *Corresponding author

June Lu School of Business, University of Houston – Victoria, 3007 N Ben Wilson, Victoria, TX 77901, USA E-mail: [email protected]

Xiaohe Xu Department of Sociology, Anthropology, and Social Work, Mississippi State University, PO Box C, MS State, MS 39762, USA E-mail: [email protected] Abstract: Asynchronous transfer mode (ATM) is a switching and multiplexing mechanism operating over a fibre-based physical network. It can be used to provide virtual private network services to businesses. Today, ATM services represent a billion-dollar business around the world. The adoption of ATM technology will probably change the current networking systems, upgrade the quality of current networks and provide increased services. Despite the increasing deployment of ATM technology and the important role it plays in today’s information technology infrastructure, little research has been found to its study with respect to social sciences. The present study examined information processing maturity and its relationship with the adoption of ATM technology in organisations. Research results provided strong evidence that there is a statistically significant relationship between organisational information processing maturity and ATM adoption in organisations. Keywords: ATM; asynchronous transfer mode; IT; information technology; information technology adoption; information processing; information systems; organisations. Copyright © 2008 Inderscience Enterprises Ltd.

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J.E. Yao et al. Reference to this paper should be made as follows: Yao, J.E., Wang, Z., Xing, R. Lu, J. and Xu, X. (2008) ‘Using organisational information processing maturity as a predictor of information technology adoption’, Int. J. Information and Decision Sciences, Vol. 1, No. 2, pp.221–233. Biographical notes: James E. Yao is a Professor of Management Information Systems in the Department of Management and Information Systems at the Montclair State University. He received his PhD from the Mississippi State University and has taught in several universities prior to Montclair State. His research interests include information technology adoptions, e-commerce and e-business. He has been a Member of The Association of Information Systems (AIS), Decision Sciences Institute (DSI) and International Association of Computer Information Systems (IACIS). Zhongxian Wang is a Professor at the Montclair State University, New Jersey, USA. He teaches Operations Analysis, Production/Operations Management, Decision Support and Expert Systems, Business Statistics, Operations Research and Management Sciences. He is a Member of Institute for Operations Research and the Management Sciences (INFORMS), Information Resources Management Association (IRMA), The Decision Sciences Institute (DSI), The Production and Operations Management Society (POMS). Ruben Xing received his Doctor of Computer Education, Master of Science, and Master of Arts from the Columbia University, New York. Having worked for more than 12 years in the IT industry, he had held senior positions at several large financial conglomerates in metropolitan New York. He served as Vice President and Sr. Systems Engineer of the E-business group at Citicorp, Assistant Vice President and Sr. Systems Engineer of the Global Information Technology group at Merrill Lynch and Systems Manager at CS and First Boston. His current research interests include networking systems management, internet security and disaster recovery. June Lu is an Associate Professor of Management in School of Business Administration at the University of Houston–Victoria. She received her Doctorate from the University of Georgia. Her specialties are management information systems and e-commerce. Her research spans wireless mobile technology acceptance and electronic/mobile commerce in different cultural settings and effectiveness of using online learning tools in MIS education. She has published a number of articles on acceptance of wireless mobile technology in Journal of Strategic Information Systems, Information and Management, Journal of Internet Research, Journal of Computer Information Systems and other journals. Xiaohe Xu is a Professor of Sociology in the Department of Sociology, Anthropology, and Social Work at the Mississippi State University. He received his PhD from the University of Michigan. His research interest covers a broad range in sociology. He has published more than 30 articles in numerous refereed journals. He is also the recipient of many research grants at various levels.

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Introduction

Paisley (1985) stated that technological change has placed communication in the front lines of a social revolution. While some companies have the opportunities and resources to take advantage of low labour costs by moving their production facilities to low labour cost countries, other companies are forced to compete in this environment by making themselves more efficient (Ariss, Raghunathan and Kunnathar, 2000). One way to improve their efficiency is to exploit modern technology (Millen and Sohal, 1998). As we moved from the industrial age into the information age (Toffler, 1980), means of communication and the exchange of information and information resources have come to rely increasingly upon computer-based information technologies and information systems. The computer-based information systems brought a very basic change in human communication (Rogers, 1986). The theoretic framework for this study is Rogers’s theory of Diffusion of Innovation (1983, 1995). The diffusion of innovation theory is a social process in which subjectively perceived information about a new idea is communicated and rests on the premise that a new idea, practice or object has perceivable channels, time and mode of being adopted by individual or organisations (Rogers, 1983). The strength of the theory is that adopters and non-adopters of an innovation may be studied to identify the factors that influence their adoption behaviour. They include the nature of the innovation, communication channels and characteristics of social group, institutions or organisations. Rogers’s diffusion of innovation theory is considered a suitable framework because of its potential application to information technology (IT) ideas, artefacts and techniques, and has been applied as the theoretical framework and used for a number of related studies, such as the adoption of innovations in library information systems, teaching and learning and other information technologies (Minishi-Majanja and Kiplang’at, 2005), media literacy programs (Yates, 2001), telemedicine (Ibbotson, 2000), e-business adoption (Roberts and Toleman, 2007) and information technology innovation adoption research (Jeyaraj, Rottman and Lacity, 2006). Currently, personal computers and workstations are commonplace in organisations. Information technology and computers have given organisations the ability to establish effective information systems for business functional areas and even share hardware, software and data resources with business partners. The increasing power of personal computers permits multimedia, virtual reality, streaming video, instant messaging and other applications to be conducted on computer networks and over the internet, especially in organisations. To exchange these applications of high-speed digital bits a great deal more bandwidth is required on the network, even with further onset of compression (Roberts, 1994). The commonly-used Ethernet and Token Ring networking technologies cannot deliver bandwidth on demand, particularly at the switching level. As more traffic is added to the network, especially voice and video, traditional technology becomes ever more incapable of satisfying the demands of business users. A solution for solving the bandwidth problem is needed to form a unified broadband network which can deliver high bandwidth on demand. The incompatibilities between different types of local area networks (LANs) and wide area networks (WANs) have existed for a long time. Data and voice messages need to be carried via different networks. There is a definite need for a unified broadband network. To provide such a broadband network, a switching and multiplexing technology suitable for the design of high capacity switches is the core. As many technologies failed

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their promises for the requested broadband services, asynchronous transfer mode (ATM) has stood out as one technology that fulfils its promise. ATM is a switching and multiplexing mechanism operating over a fibre-based physical network. The essential features of ATM are a fixed-length packet (called a cell, a 53-byte packet with 5 bytes for header/footer and 48 bytes for information payload), which is switched based on a virtual circuit identifier in the cell header. All information types (voice, data and video) are transported inside the cell. The most significant advantage of ATM is in its ability to do statistical multiplexing and thus can effectively handle the bursty variable bit rate (VBR) and constant bit rate (CBR) traffic types. It is primarily a connection-oriented technology using a combination of virtual circuits (VC) and virtual paths (VP) to establish an end-toend connection. End-hosts request that the network sets up a virtual circuit via a signalling control protocol that allows them to specify the desired quality of service (Chatterjee and Xiao, 1997; Kalmanek, 2002). According to McDysan and Spohn (1995), ATM takes on many forms: provides software and hardware multiplexing, switching and cross-connect functions and platforms; serves as an economical, integrated network access method; becomes the core of a network infrastructure; provides quality to the much-touted ATM service. ATM technology is one of the most important developments in internetworking in the last two decades. It has the potential to transform our network communication process. However, the debate over the merits of such a technology is still going on (Crowcroft and McAuley, 2002). Today, ATM is used to provide virtual private network (VPN) services to businesses, consisting primarily of point-to-point virtual circuits connecting customer sites. ATM services represented a 2 billion dollar business in 2001. In addition, ATM provides the underpinning of digital subscriber loop (DSL) services, which are growing rapidly. ATM is also used as the core network infrastructure for large frame relay networks and for some internet protocol (IP) networks (Kalmanek, 2002). Based on the far-reaching significant position ATM possesses in networking, it can be seen that ATM will play a more important role in the building of a new utility infrastructure for communications technologies. The adoption of ATM technology will probably change the current networking systems, upgrade the quality of current networks and provide increased services. Despite the increasing deployment of ATM technology and the important role it plays in today’s information technology infrastructure, little research has been found devoted to its study with respect to social sciences. Kimberly and Evanisko (1981) proposed that organisational variables have been clearly the best predictors of adoption of technological innovations. Lee and Shim (2007) researched on variables and categories that are related to adoption behaviour in organisational information technology adoption. Roberts and Toleman (2007) studied e-business adoption in organisations. Kamal (2006) examined factors impacting organisational information technology adoption in the private sector. Other researchers have examined organisational characteristics, such as organisational size, information systems maturity and their relationships to organisational adoption of technological innovations (DeLone, 1981; Kimberly and Evanisko, 1981; Damanpour, 1987; Lind, Zmud, and Fischer, 1989; Marcotte, 1989; Yap, 1990; Ellis, 1994; Eder and Igbaria, 2001; Kauffman and Mohtadi, 2004; Corey and Grossman, 2007; Roberts and Toleman, 2007). According to Data Bulletin (Corey and Grossman, 2007), significant variation in information technology adoption exists across specialties, based on the findings from health system change’s national physician survey, while practice setting and size are the strongest predictors of physician’s access to clinical information

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technology in their practices. Patterns of specialty adoption may reflect the relevance of particular clinical activities. However, no research has been found that studies ATM adoption in institutions of higher learning, nor has any research of this nature been found in other information technology technologies and organisational studies. Identification of such organisational variable as university information processing maturity will provide valuable information to researchers in their study of ATM adoption in universities as well as similar information technology innovation adoptions in other settings. This article examined the relationship between a university’s information processing maturity and its ATM technology adoption to investigate whether a university’s information processing maturity can serve as a predictor of information technology adoption. To examine the relationship, the following hypothesis was formulated: There is no statistically significant relationship between information processing maturity and ATM adoption in a university.

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Methodology

The research design for this study was corelational since this method permits analysis of the relationships among a number of variables in a single study (Borg and Gall, 1989). Universities chosen for the study were randomly selected from the population of four-year universities in the USA. To keep equality, the universities then listed as ATM Forum members and as Educom members were targeted for recruitment of sample subjects from the ATM adopter population. These universities are more involved in ATM technology and campus networking and are, thus, more likely to have adopted ATM. About equal number of universities which are neither listed as ATM Forum members nor as Educom members were randomly selected for the recruitment of sample subjects from the ATM non-adopter population. The sample subjects were then randomly selected from the population of university domain LAN administrators in these universities. The LAN administrators are those who are involved directly in the planning, administration, construction of university network infrastructure and LANs administration, and, most of all, adoption and implementation of state-of-the-art high-tech innovations like ATM. The preference of only university domain LAN administrators makes the selected subjects homogeneous so that more accurate data of the variables can be obtained (Borg and Gall, 1989). The list of research universities was obtained from the technical report published by The Carnegie Foundation for the Advancement of Teaching (1994). Research universities encompass research universities I and II. The classification of universities by type was based on the taxonomy developed by the Carnegie Foundation for the Advancement of Teaching. Research universities I offer a full range of baccalaureate programs. They are committed to graduate education through the doctorate and give high priority to research. They award 50 or more doctoral degrees each year. Research Universities II meet all the criteria for Research I institutions except that their annual federal support range is slightly lower than Research I institutions. Those that are not listed under Research Universities I and II were classified and listed as non-research universities. The level of computing used to support department, college and university management decision-making, data/information handling, and learning/research was defined as information processing maturity. The data were obtained from the researcher developed questionnaire survey. The questions used were an adoption of (but slightly modified by the researchers) a survey questionnaire developed by Ellis (1994) in his

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study of LAN adoption. The validity and reliability of the questions were established in Ellis’ study. His study confirmed that organisational degree of information processing maturity was related to an organisation's ability to respond to or accept the complex elements of a LAN. All the items on the questionnaire used Likert-type 6-point scale, similar to the scale measurement used by Ellis (1994), to measure institutional information processing maturity. Variables CDATA (computers used to support data/information handling), CUNSTRDM (computers used to support unstructured decision-making) and CLRNSCH (computers used to support learning/research) are based on a scale of 1–6, with 1 = strongly disagree and 6 = strongly agree. High values represent high degree of information processing maturity. Similarly, but in the opposite direction, CUDMK (computers used to support university level decision-making), CDEPTDMK (computers used to support department/college level decision-making) and CSTRUDMK (computers used to support structured decision-making) are based on 1 = strongly disagree to 6 = strongly agree, with low values representing high degree of information processing maturity. The questionnaire used in the present research was reviewed by two experts in networking/telecommunications and ATM technology. They were university domain LAN administrators who were personally involved in university ATM adoption and implementation. Suggestions from these experts were used to modify the questionnaire. The questionnaire was posted on the World Wide Web. E-mail was used to distribute the cover letter of the questionnaire to each university domain LAN administrator. A total of 554 user addresses were actually sent through via the internet. From the 554 user addresses sent through, 208 responses were received for a response rate of 37.55%. Sixty-seven responses were received within ten days, a 32% of the total responses. The fastest response was received about five minutes after the invite e-mail was sent out to the recipient. Ten days after the initial invite e-mail, a follow-up was made, which generated 113 responses, 54% of the total responses in three weeks. A second follow-up were then made, which brought in 28 more responses. Out of the total 208 responses, nine were unusable, leaving 199 usable, yielding a usable response rate of 35.92%.

2.1 Data analysis Logistic regression was employed to study the relationship between organisational variables and the ATM technology adoption status of a university. According to Hosmer and Lemeshow (1989), regression methods have become an integral component of any data analysis concerned with describing the relationship between a dependent variable and one or more independent variables. Very often the dependent variable is discrete, taking on two or more possible values. Logistic regression, in many fields, has become the standard method of analysis in this situation. The dependent variable in this study is dichotomous (adoption and non-adoption) with an objective of describing the relationship between the dependent variable, ATM technology adoption, and the independent variable of university information processing maturity. Therefore, logistic regression was an appropriate statistical analysis method for this study. The data were analysed by using Statistical Package for the Social Sciences (SPSS).

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Findings

3.1 Asynchronous transfer mode adoption status Of the 199 responses received, 58 universities indicated that they had adopted ATM technology, which was 29.1% of the responses. Of these 58 universities which have adopted ATM, 51.7% (n = 30) were research universities and 48.3% were non-research universities. Among the non-research universities, 22.4% (n = 13) were doctorategranting universities, and 25.9% (n = 15) were neither research universities, nor doctorate-granting universities. The frequencies for ATM adoption are shown in Table 1. Table 1

Frequencies for asynchronous transfer mode adoption status

University type

Adopted

Non-adopted

Total

Frequency

Percent

Frequency

Percent

Research

30

51.7

16

11.4

Doctorate

13

22.4

23

16.3

36

Neither

15

25.9

102

72.3

117

Total

58

100.0

141

–

–

Total (%)

–

29.1

70.9

46

100.0

199

–

100

3.2 Speed, bandwidth and efficiency improvement About 93% (n = 54) of the universities, which had adopted ATM, reported that their networks’ speed, bandwidth and/or efficiency had been improved since they adopted ATM. Only about 7% (n = 4) of the universities did not indicate speed, bandwidth and/or efficiency improvement on their networks since they adopted ATM. Table 2 shows the frequencies for the speed, bandwidth and/or efficiency improvement. Table 2

Frequencies for improvement

Status Improved Not Improved Total

Frequency

Percent

54

93.1

4

6.9

58

100.0

3.3 Descriptive statistics Descriptive statistics (means and standard deviations) for both the dependent and independent variables are reported in this section. Table 3 depicts the descriptive statistics for the predictor variables. Reliability test and factor analysis were performed on data of the items measuring information processing maturity. Based on the reliability test and factor analysis, two index variables were created. The first index variable, named MT1, consisted of CDATA, CUNSTRDM and CLRNSCH, indicating the concept of germane application of information systems in university settings (high value represents high degree of information processing maturity). The reliability coefficient is D = 0.9013. The second

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index variable, named MT2, comprised CUDMK, CDEPTDMK and CSTRUDMK, representing the concept of immaterial application of information systems in university settings (low value represents high degree of information processing maturity). The reliability coefficient is D = 0.5577. University information processing maturity as a whole is represented by the two index variables. Table 4 presents the correlation coefficients of variables within MT1 and MT2. Table 3

Descriptive statistics for the predictor variables and categorical variables

Variable

Variable label

Mean

ADOPT

Adoption status (Dummy, 1 = Adopted)

ADOPT

Adoption status (Dummy, 1 = Adopted)

ENROLLMT

Enrolment

UTYPE ITBUDGET NTBUDGET CDATA

0.29

SD 0.46

0.29

0.46

11722.85

9661.26

University type (Dummy, 1 = Research)

0.23

0.42

Overall information technology Budget

6.81

7.74

Budget for network/telecom

15.97

15.16

Data/Information handling

*4.32

1.49

CUDMK

University level decision-making

*3.11

1.29

CDEPTDMK

Dept/College level decision-making

*3.27

1.30

CSTRUDMK

Structured decision-making

*3.15

1.35

CUNSTRDM

Unstructured decision-making

*3.55

1.28

CLRNSCH

Learning/Research

*5.05

1.18

SPEED

Speed, bandwidth, efficiency improvement (Dummy, 1 = Improved)

0.93

0.26

Note. *Scale of 1–6 (strongly disagree to strongly agree). Table 4

Correlation coefficients within MT1 and MT2

MT1

CDATA

CLRNSCH

CUNSTRDM

CDATA

1.0000

0.2952*

0.3061*

CLRNSCH

0.2952

1.0000

0.2978*

CUNSTRDM

0.3061

0.2978

1.0000

MT2

CDEPTDMK

CSTRUDMK

CUDMK

CDEPTDMK

1.0000

0.7568*

0.7945*

CSTRUDMK

0.7568

1.0000

0.7099*

CUDMK

0.7945

0.7099

1.0000

*p < 0.001; **p < 0.01; ***p < 0.05; Listwise n = 199.

4

Logistic regression results

Nested models were used to analyse model variables. Logistic regression coefficients for the nested models are listed in Table 5. According to Norusis (1994), logistic coefficient can be interpreted as the change in the log odds associated with a one-unit change in the independent variable. Logit (the log of odds) is represented by coefficient value E.

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Since it is easier to think of odds rather than log odds, the logistic model uses Exp(E) (exponential function of coefficient) to represent odds, which can be interpreted as by increasing the value of independent variable’s coefficient from 0 to 1 the odds are increased by a factor of the value under Exp(E). If the independent variable’s coefficient value E is positive, this factor will be greater than 1, which means that the odds are increased; if the E value is negative, the factor will be less than 1, meaning that the odds are decreased. Based on this rule of thumb and the coefficient values revealed in Table 5, interpretations of these models are stated in each of the individual sections to follow.

4.1 Model 4 The p-value for MT1 is less than 0.05, which suggests that, irrespective of the second index variable MT2 (universities with immaterial applications of information systems), there is a statistically significant relationship between ATM adoption and the first index variable, namely the germane applications of information systems in university settings. The p-value for MT2 is greater than 0.05. Therefore, it can be concluded that there is no significant relationship between ATM adoption and the second index variable, namely the immaterial applications of information systems in university settings. Chi-square tests the null hypothesis that the coefficient in the current model, except the constant, is 0 (Norusis, 1994). This is comparable to the overall F test for regression. If the Model F2 is statistically significant beyond p = 0.05, it indicates that the predictor variable contributes no chance to the probability of explaining the dependent variable (Menard, 1995). Model 4 yields a Model F2 of 53.953 relative to two d.f., which is statistically significant (p < 0.05). Compared to Model 3, Model 4 improves the goodness-of-fit (59.618  53.953 = 5.665) (5  3 = 2). As a result, Model 4 is better than Model 3 because variable MT1 further improves the fit by 'F2 = 5.665 relative to two d.f. Table 5

Logistic regression coefficients and goodness-of-fit for the nested models Model 1

Variable

Model 2

Model 4

Exp(E)

E

Exp(E)

ENROLLMT 0.00008* 1.0001 0.00003

1.0000

0.00002

1.0000

0.00002

1.0000

UTYPE

–

5.3297

1.6505*

5.2095

1.5204**

4.5740

NTBUDGET

–

–

1.0542

0.0526*

1.0540

MT1

–

–

MT2 2

Model F d.f.

Significance

Exp(E)

Model 3

E

E 1.6733*

0.0528*

Exp(E)

E

–

0.6980*** 2.0098 0.3479

–

–

–

23.979

37.519

53.953

59.618

1

2

3

5

0.0000

0.0000

0.0000

0.0000

*p < 0.001; **p < 0.01; ***p < 0.05

1.4161

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Conclusions and discussions

Based on the results of statistical analysis, the research hypothesis that there is no statistically significant relationship between information processing maturity and ATM adoption in a university is rejected. We can positively state that statistically there is a significant relationship between information processing maturity and ATM technology adoption in a university. Variable MT1 (universities with germane applications of information systems) showed a significant relationship between university information processing maturity and ATM adoption. Its odds ratio (Exp(E) = 2.0098 as shown in Model 4, Table 5) indicates that the odds of adopting ATM for universities with high degree of information processing maturity are about 101% greater than universities with low degree of information processing maturity. Table 2 presents that 6.9% (n = 4) of the universities did not indicate an improvement of their networks’ speed, bandwidth and/or efficiency since they adopted ATM. Does this mean that ATM adoption was not a good choice for these universities or does it mean something else? A comparison of the data reveals that three out of the four universities adopted ATM in the same year as the survey was administered, and the survey was sent out in the last two months of the year. This could signify several possibilities. It could be that they were not in the adoption evaluation phase to determine whether their network’s speed, bandwidth and efficiency had been improved or the systems implementation of the technology was still in progress by the time they were administered the survey.

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Limitations, implications and recommendations

Since the subjects of the study were randomly selected from universities throughout the USA, their individual knowledge on ATM technology and the survey questions were regarded relatively sufficient and equal. Data obtained from sources are presumably accurate, reliable and not biased. Thus, the generalisability of the results of the study may be subjected to the influence of the findings of the above-mentioned factor. Furthermore, its generalisabilty may be limited to settings with similar institutional level and technology. Within this limitation, the following implications are posited: This research sought to determine if there is a statistically significant relationship between university information processing maturity and ATM technology adoption. The research results provided strong evidence to support the postulation that university information processing maturity is significantly related to ATM technology adoption in universities. These results support, strengthen and expand the earlier findings from Ellis (1994), e.g. organisational information processing maturity can serve as a predictor when applied to the study of information technology adoption in organisations. Furthermore, previous and current theories on new technology adoption focus primarily on issues at the individual level (Venkatesh and Brown, 2001). The present study, however, has brought some constructive and meaningful contributions to the deposition of information technology adoption research based on organisations under the theory of innovation adoption and diffusion. As the statistical results indicate, universities with high degree of information processing maturity are 101% more likely to adopt ATM technology. From a managerial standpoint, administrators in the early-adopter universities or organisations

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need to realise that they are knowingly or unknowingly on the forefront of information technology innovation adoption because of their advantageous status in both technological and human resources. Keeping their leading positions in information technology adoption is as significant as their leading positions in the industry or academic standings because it may be one of the major factors that they can sustain their current positions. Information technology vendors, on the other hand, can benefit from the research findings by realising that organisations with better information processing maturity are often early adopters of cutting-edge information technologies, such as ATM. They ought to preserve a well-balanced targeted marketing strategy based on their in-depth understanding of their current clients and their potential customers to establish larger and long-lasting markets for new information technologies. Universities or organisations which have adopted ATM technology may be prepared for adopting new ATM related products as well as the post adoption management and maintenance. At the same time, they may need to cope with the outcome change in their organisational structure as a result of the new information technology adoption. Given the seminal and exploratory nature of the study, further studies of the determinants of ATM technology adoption and the adoption of other information technology innovations may want to investigate into additional organisational variables, such as organisational type, organisational structure, information systems structure, managerial support, etc. (Giunta and Trivieri, 2007) so as to yield more valuable and enriched information for guiding IT/IS implementation practices in organisations.

References Ariss, S.S., Raghunathan, T.S. and Kunnathar, A. (2000) ‘Factors affecting the adoption of advanced manufacturing technology in small firms’, SAM Advanced Management Journal, Vol. 65, pp.14–21. Borg, W.R. and Gall, M.D. (1989) Educational Research: An Introduction. New York, NY: Longman. Chatterjee, S. and Xiao, W. (1997) ‘Increasing throughput in ATM/B-ISDN using simple buffer management and selective discarding’, ACM SIGCOMM Computer Communication Review, Vol. 27, pp.44–60. Corey, C. and Grossman, J.M. (2007) ‘Clinical information technology adoption varies across physician specialties’, Data Bulletin, September, Vol. 34, pp.1–2. Crowcroft, J. and McAuley, D. (2002) ‘ATM: a retrospective on systems legacy or ‘A technology with a fabulous future behind it?’’, ACM SIGCOMM Computer Communications Review, Vol. 32, pp.11–12. Damanpour, F. (1987) ‘The adoption of technological, administrative, and ancillary innovations: impact of organizational factors’, Journal of Management, Vol. 13, pp.675–688. DeLone, W.H. (1981) ‘Firm size and the characteristics of computer use’, MIS Quarterly, Vol. 5, pp.65–77. Eder, L.B. and Igbaria, M. (2001) ‘Determinants of intranet diffusion and infusion’, Omega, Vol. 29, pp.233–242. Ellis, R.W. (1994) ‘Local area network adoption: an examination of selected variables as explanatory reasons for adoption’, Dissertation Abstracts International, Vol. 54, p.3247. Giunta, A. and Trivieri, F. (2007) ‘Understanding the determinants of information technology adoption: evidence from Italian manufacturing firms’, Applied Economics, Vol. 39, pp.1325–1334.

232

J.E. Yao et al.

Hosmer, D.W. and Lemeshow, S. (1989) Applied logistic regression, New York, NY: John Wiley and Sons Inc. Ibbotson, T. (2000) ‘An ethnographic study of the diffusion of telemedicine in Scotland’. Available at: http://virtualsociety.sbs.ox.ac.uk/projects/ibbotson.html. Retrieved on 30 April, 2008. Jeyaraj, A., Rottman, J.W. and Lacity, M.C. (2006) ‘A review of the predictors, linkages, and biases in IT innovation adoption research,’ Journal of Information Technology, Vol. 21, pp.1–23. Kalmanek, C. (2002) ‘A retrospective view of ATM’, ACM SIGCOMM Computer Communication Review, Vol. 32, pp.13–19. Kamal, M.M. (2006) ‘IT innovation adoption in the government sector: identifying the critical success factors’, Journal of Enterprise Information Management, Vol. 19, pp.192–222. Kauffman, R.J. and Mohtadi, H. (2004) ‘Proprietary and open systems adoption in e-procurement: a risk augmented transaction cost perspective’, Journal of Management Information Systems, summer, Vol. 21, pp.137–166. Kimberly, J.R. and Evanisko, M. (1981) ‘Organizational innovations: the influence of individual, organizational, and contextual factors on hospital adoption of technological and administrative innovations’, Academy of Management Journal, Vol. 24, pp.689–713. Lee, C. and Shim, J.P. (2007) ‘An exploratory study of radio frequency identification (RFID) adoption in the healthcare industry’, European Journal of Information Systems, Vol. 16, pp.712–724. Lind, M.R., Zmud, R.W. and Fischer, W.A. (1989) ‘Microcomputer adoption – the impact of organizational size and structure’, Information and Management, March, Vol. 16, pp.157–163. Marcotte, F.A. (1989) ‘Adoption of automation innovations in college libraries: a study of the effects of size and financial factors affecting prediction of adoption of eight technological innovations’, Dissertation Abstracts International, Vol. 49, p.2051. McDysan, D. and Spohn, D. (1995) ATM: Theory and Application. New York, NY: McGraw-Hill, Inc. Menard, S. (1995) Applied Logistic Regression Analysis. Thousand Oaks, CA: Sage Publications, Inc. Millen, R. and Sohal, A. (1998) ‘Planning process for advanced manufacturing technology by large American manufactures’, Technovation, Vol. 18, pp.741–750. Minishi-Majanja, M.K. and Kiplang’at, J. (2005) ‘The diffusion of innovations theory as a theoretical framework in library and information science research’, SA Journal of Libraries and Information Science, Vol. 71, pp.211–224. Norusis, M.J. (1994) SPSS: SPSS Advanced Statistics 6.1. Chicago, IL: SPSS Inc. Paisley, W. (1985) ‘Communication in the communication sciences’, in B Dervin and M.J. Voigt (Eds) Progress in the Communication Sciences. Norwood, NJ: Ablex, Vol. 5. Roberts, M.M. (1994) ‘Building the NII: challenges for higher education’, EDUCOM Review, Vol. 29, pp.28–31. Roberts, B. and Toleman, M. (2007) ‘One-size e-business adoption model does not fit all’, Journal of Theoretical and Applied Electronic Commerce Research, Vol. 2, pp.49–61. Rogers, E.M. (1983) Diffusion of Innovations (3rd ed.). New York, NY: Free Press. Rogers, E.M. (1986) Communication Technology: The New Media in Society, New York, NY: The Free Press. Rogers, E.M. (1995) Diffusion of Innovations (4th ed.). New York, NY: Free Press. Toffler, A. (1980) The Third Wave, New York, NY: Bantam Books. The Carnegie Foundation for the Advancement of Teaching (1994) A Classification of Institutions of Higher Education. Princeton, New Jersey: Author. Venkatesh, V. and Brown, S.A. (2001) ‘A longitudinal investigation of personal computers in homes: adoption determinants and emerging challenges’, MIS Quarterly, Vol. 25, pp.71–102.

Using organisational information processing maturity as a predictor

233

Yap, C.S. (1990) ‘Distinguishing characteristics of organizations using computers’, Information and Management, Vol. 18, pp.97–107. Yates, B.L. (2001) ‘Adoption of media literacy programs in schools,’ Paper presented in the Proceedings of the Instructional and Development Communication Division International Communication Association Conference, Washington, DC. USA, 24–28 May, 2001.