Omega 29 (2001) 143 156 www.elsevier.com/locate/dsw Resource constraints and information systems implementation in Singaporean small businesses James Y.L. Thong Department of Information and Systems Management, School of Business and Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong Received 3 April 2000; accepted 5 July 2000 Abstract While the information systems (IS) literature has identied potential factors of IS implementation success, none has investigated the relative importance of these factors in the context of small businesses. Small businesses have very dierent characteristics from large businesses; notably, small businesses suer from resource poverty. Without knowing the relative importance of key factors, small businesses may be expending their limited resources and energy on less important factors which have limited contribution to IS implementation success. This paper develops a resource-based model of IS implementation for small businesses based on Welsh and White s (Harv Bus Rev 59(4) (1981) 18 32) framework of resource constraints in small businesses and Attewell s (Organ Sci 3(1) (1992) 1 19) knowledge barrier theory. The model is then tested on a sample of 114 small businesses. The results show that small businesses with successful IS tend to have highly eective external experts, adequate IS investment, high users IS knowledge, high user involvement, and high CEO support. External expertise is the predominant key factor of IS implementation success in small businesses.? 2001 Elsevier Science Ltd. All rights reserved. Keywords: External expertise; Implementation; Information systems; Resource-based theory; Small business 1. Introduction The computer-based information systems (IS) are a major technological innovation during this century. The benets of IS to large businesses are well-documented in the popular press, trade magazines, and IS text books. At the same time, the majority of IS research has concentrated on IS implementation in large businesses. However, the literature has also argued that there is a relationship between organizational size and IS implementation success (e.g. [1 4]). Due to the inherent dierences between small and large businesses, research ndings based on large businesses cannot be generalized to small businesses (e.g. [5 8]). As small businesses constitute over 90% of operating businesses in many countries, there is a great need for more rigorous research that is relevant to this important sector of the economy [9]. Tel.: +852-2358-7631; fax: +852-2358-2421. E-mail address: jthong@ust.hk (J.Y.L. Thong). Small businesses are under increasing pressure to employ IS to maintain their competitive positions or simply to survive. At the same time, there are more barriers to IS implementation in small businesses than in large businesses due to the high capital investment and skilled manpower involved in implementing and operating IS. If the IS implementation is successful, potential benets to small businesses can include increased sales, improved protability, increased productivity, improved decision-making, and secured competitive positions (see [10 14]). On the other hand, if the IS implementation is unsuccessful, it will have severe repercussions on small businesses with their limited resources as they can rarely rely on organizational slack to act as a buer [15,16]. Hence, a key research question is identifying the determinants of successful IS implementation in small businesses. A review of the IS, small business, and management literature was conducted to assess the current state of research on small businesses computerization. As we are examining IS specically, it is highly relevant to review the IS literature. 0305-0483/01/$ - see front matter? 2001 Elsevier Science Ltd. All rights reserved. PII: S0305-0483(00)00035-9
144 J.Y.L. Thong / Omega 29 (2001) 143 156 It is also relevant to include the small business literature as we are studying the context of small businesses. Finally, the management literature is included as it also publishes studies on the management of IS in small business. It should be noted that we are interested in studies of factors that lead to successful IS implementation rather than studies of factors that lead to the decision to adopt IS or further IS adoption. Adoption, implementation, and post-implementation (including further adoption) are three dierent stages in the technology innovation cycle. The adoption stage is where a decision is made about whether to adopt a new technology. If the decision is to go ahead with adoption, the implementation stage involves implementing the technology in the business. Once the technology has been implemented successfully, the post-implementation stage is concerned with how much organizational learning takes place within the business so as to facilitate further technology adoption [17 19]. As the current study is concerned with the implementation stage, literature involving the second stage of the technology innovation cycle will be most relevant. Based on case studies and surveys, a number of potential factors have been identied in the literature as critical to IS implementation success in small businesses (e.g. [20 28]). However, there are limitations with the prior research. None of the prior literature has investigated the relative importance of the identied factors of IS implementation success. Without knowing the relative importance of these factors, small businesses may be expending their limited resources and energy on less important factors which have limited contribution to IS implementation success. Thus, there is a need to identify the more important factors so that implications and guidelines can be drawn for eective IS implementation in small businesses. In addition, most of the prior studies were carried out in the 1980s. Since then, computer hardware have become more powerful, software applications are more sophisticated, and prices are increasingly more aordable even for small businesses. Some of the previously identied factors are no longer applicable in today s business environment (e.g. type of computer, interactive versus batch systems, use of custom-developed applications). Thus, there is also a need to reexamine the factors of IS implementation success in small businesses in today s context. The prior literature also tends to use bivariate analysis, specically correlation analysis. Bivariate analysis which considers two variables at a time allows only a limited testing of the research question. In comparison, multivariate analysis allows us to examine the eects of multiple independent variables on a dependent variable simultaneously. Some researchers have even claimed that unless a problem is treated as a multivariate problem, it is treated supercially. Further, developments in structural equation modeling techniques, or second-generation multivariate analysis, allow even stronger tests to be brought to bear on the research question [29 31]. Thus, statistical techniques other than simple bivariate analysis need to be utilized to provide a stronger test of the research question. Finally, most prior studies used only one key informant as representative of user evaluation of IS implementation success and sometimes this key informant may not even be a user of the IS. The research design would be more rigorous if multiple respondents were included [32]. The objective of this paper is to develop an updated IS implementation model in small businesses. The model is then tested on a survey sample of small businesses with multiple respondents from each small business. The relative importance of the key factors can then be examined using a structural equation modeling technique [33]. The rest of this paper is organized as follows. The following section describes the theoretical background for this research. After that, we present the research model and develop the research propositions. We then describe the research methodology. This is followed by a presentation of the data analysis results and discussion of the ndings. Finally, we conclude the paper with implications for practice and research. 2. Theoretical background Small businesses tend to have simple and highly centralized structures with the chief executive ocers (CEOs), who are also the owners, making most of the critical decisions [34,35]. These CEOs have a great inuence on the technology adoption decision [36,37]. Small businesses, especially those with less than 100 employees, also tend to employ generalists rather than specialists [38,39]. Operating procedures are not written down or standardized. Other distinctive characteristics include reliance on short-term planning rather than long-term strategic plans, fewer bureaucratic procedures, less complex interpersonal and political relations, and less organizational inertia [34,40]. Further, few small businesses use management techniques such as nancial analysis, forecasting, and project management [6]. The decision-making process of small business managers is more intuitive and less dependent on formal decision models [41]. Due to their distinctive dierences from large businesses, there is a need to examine small businesses separately rather than as scale models of large businesses (e.g. [5,8,42,43]). The theoretical framework leading to the proposed IS implementation model for small businesses is based on the resource-based theory of the rm [44 46]. This theory has been hailed as a promising approach to the study of rms [47 49]. According to the resource-based theory, rms are characterized as collections of resources or capabilities. A rm s resources may include both tangible and intangible assets including capabilities, organizational processes, information, and knowledge, controlled by a rm that enable the rm to conceive and implement strategies that improve its eciency and eectiveness [50]. It emphasizes understanding the internal capabilities that enable rms to secure competitive positions [47]. Further, the value of a resource is likely to be partially contingent upon the presence of other
J.Y.L. Thong / Omega 29 (2001) 143 156 145 resources; i.e. a system of resources matter more than individual resources taken separately [51]. In this paper, the resource-based theory of the rm is applied to the issue of IS implementation success in small businesses. Closely related to the concepts of resource-based theory is Welsh and White s [43] framework of resource constraints in small businesses. According to them, the unique characteristics of small businesses are exemplied in the condition known as resource poverty where small businesses operate under severe time constraints, nancial constraints, and expertise constraints. Time constraints refer to the limited amount of time available for activities beyond the normal job responsibilities of individuals in the small businesses. Due to their limited time, the CEOs and their employees tend to have a short-range perspective with regard to IS implementation and are not very involved in the IS implementation projects. If the CEOs and potential users do not participate in the IS implementation, the quality of the IS will suer. Financial constraints refer to the limited amount of nance available for activities beyond the normal operations of the small businesses. Due to their nancial constraints, small businesses have to control their cash ows carefully and do not have unlimited funds for their IS implementation projects. They tend to choose the cheapest system which may be inadequate for their purpose and underestimate the amount of time and eort required for IS implementation [52]. Expertise constraints refer to the limited amount of expertise within the small businesses to carry out activities beyond designated job responsibilities. They do not have the necessary inhouse IS expertise or a formal IS department. Small businesses tend to engage consultants and IT vendors to develop and support their information systems. Due to their lack of internal IS expertise, small businesses do not have the capability to undertake their own IS implementation projects. In summary, resources such as time, nance, and expertise, that are necessary for planning represent the most critical diculties in small businesses [7]. Inadequate resources spent on IS implementation increase the risk of IS implementation failure. While the resource-based theory emphasizes the importance of internal resources in a rm, external resources are also important in the context of small businesses. Attewell s [53] technology diusion theory emphasizes the role of external entities, such as consultants and IT vendors, as knowledge providers in lowering the knowledge barrier or knowledge deciency on the parts of potential IS adopters. Small businesses tend to delay inhouse IS implementation because they have insucient knowledge to implement IS successfully. In response to this knowledge barrier, mediating entities come into existence which progressively lower this barrier, and make it easier for small businesses to adopt and implement IS without extensive inhouse expertise. These mediating entities can capture economies of scale in learning. After developing many systems, the IT vendor would have learned from earlier attempts and developed a relatively error-free system. Similarly, the consultant would Fig. 1. Research model. have acquired a wealth of experience in IS implementation. Hence, external resources in the form of external experts are also important to small businesses in implementing IS successfully. 3. Research model The research model is developed based on the prior theoretical background discussion (see Fig. 1). By adopting the resource-based view of the rm and taking into account the distinctive characteristics of small businesses, the IS implementation environment in a small business is conceptualized in terms of three categories of resource constraints. The categories of resource constraints are time, nance, and expertise. While resources can also be viewed as facilitators, we have adopted the view of constraints to be consistent with Welsh and White s [43] concept of resource poverty. Further, according to Attewell s [53] knowledge barrier theory, small businesses with their lack of internal IS expertise will need to engage IS expertise from the external environment. 3.1. IS implementation success Two popular constructs of IS implementation success are user information satisfaction and organizational impact. Based on a literature review of prior constructs of IS implementation success, DeLone and McLean [54] developed a meta-model that included both user information satisfaction and organizational impact as appropriate constructs of the eectiveness of IS. These two constructs are elaborated on below.
146 J.Y.L. Thong / Omega 29 (2001) 143 156 3.1.1. User information satisfaction According to Seddon and Kiew [55], user information satisfaction may be considered the best omnibus construct of IS implementation success. Other researchers [32,56] have also argued that user information satisfaction provides the most useful assessment of IS implementation success. User information satisfaction may be dened as the extent to which users believe the IS meet their information requirements [57]. If the IS meet the requirements of the users, the users satisfaction with the IS will increase. Conversely, if the IS do not provide the needed information, the users will become dissatised. This denition of IS success is a popular one in the IS literature. It can also be a meaningful surrogate for the critical but unquantiable result of the IS, namely, changes in organizational eectiveness [57]. In small business research, user information satisfaction has often been used as the dependent variable (e.g. [4,24 26,58]). Hence, user information satisfaction is used as a construct of IS implementation success in this study. 3.1.2. Organizational impact Organizational impact is dened as the impact of the IS on the performance of the small business. IS implementation success only has meaning to the extent that the IS contribute to organizational eectiveness. In a small business, the impact and value of the IS are likely to be achieved through sta productivity, operations eciency, improvement in decision-making, increased sales revenue, increased prot, and increased competitive advantage [59,60]. Further, the eects of various factors on organizational impact of the IS are mediated by user information satisfaction [54]. The higher the level of user information satisfaction with the IS, the greater will be the organizational impact on the business. If the information needs of top management are met satisfactorily by the IS then their decision-making performance will improve leading to increased positive organizational impact. Hypothesis 1: Organizational impact is positively related to the level of user information satisfaction. 3.2. Time constraint 3.2.1. CEO support The CEO of a small business has limited time to spend on IS implementation. But if the CEO can aord to be more involved in the IS implementation project, the probability of a successful implementation is much higher. The CEO has the authority to inuence other members of the business and is more likely to succeed in overcoming organizational resistance to accept the IS [61]. Jarvenpaa and Ives [62] noted that hands-on management in IS implementation may be much more important in a small business where the CEO commonly makes most key decisions and is perhaps the only one who can harness IS to business objectives. This is especially true in a small business where the CEO is the person who understands the business best. A supportive CEO is also more likely to commit scarce resources and adopt a longer-range perspective to the benets of IS implementation [63,64]. This is also believed to be the case in small businesses [21,28,34]. Hence, it is hypothesized that user information satisfaction is likely to be high when the level of CEO support is high. Hypothesis 2: User information satisfaction is positively related to the level of CEO support. 3.2.2. User involvement Employees in a small business tend to be generalists rather than specialists in a certain eld [38,39]. They have multiple job functions to perform within a limited time. Hence, if the small business encourages the ultimate users to be involved in the IS implementation by allowing them time-o from their normal responsibilities, then the IS implementation is more likely to be successful. Benets can include a better t of the IS with user requirements, ease of operating the IS due to learning experience during the design phase, feeling of ownership, and reduced resistance to change [65 67]. Hence, it is hypothesized that user information satisfaction is likely to be high when the level of user involvement is high. Hypothesis 3: User information satisfaction is positively related to the level of user involvement. 3.2.3. IS planning Similarly, if the small business can aord to spend more time on IS planning, the chances of IS implementation success will be higher. The importance of IS planning in terms of requirements analysis, system analysis and design, and resource controls has been stated in the literature [68,69]. Ginzberg [64] identied the extent of project denition and planning as a key recurrent issue in IS implementation success of large businesses. More eort spent on IS planning can lead to better t of the business requirements with the nal system. There is also some evidence of a positive relationship between user information satisfaction and the level of IS planning in small businesses [25]. Hence, user information satisfaction is expected to be high when the level of IS planning is high. Hypothesis 4: User information satisfaction is positively related to the level of IS planning. 3.3. Financial constraint 3.3.1. IS investment Research has shown that sucient nancial resources increases the likelihood of IS implementation success [70,71]. As small businesses often lack nancial resources, insucient funds may be allocated for IS implementation. Insucient nancial resources place constraints on the IS implementation eort and often lead to selection of less eective IS. Selection of such IS, although cheaper, will
J.Y.L. Thong / Omega 29 (2001) 143 156 147 result in false economy because the IS do not meet the requirements of the business and will be inadequate in the long run [52]. IS implementation involves capital investments and often has organization-wide implications. The future of the business may be jeopardized by unsuccessful investments in IS because a technical failure in the IS can have a major negative impact on the business that is heavily dependent on them. The setback has even greater ramication for a small business as it may even result in business failure [72]. Hence, it is hypothesized that increased allocation for IS investment will increase the likelihood of IS implementation success. Hypothesis 5: User information satisfaction is positively related to the level of IS investment. 3.4. Expertise constraint 3.4.1. Users IS knowledge There is generally a lack of internal IS expertise in a small business [22,39]. Employees are usually employed to work on the daily operations of the business and not for their ability to program or use software packages. Further, it is dicult to recruit and retain IS professionals in a small business due to the tight labor market for IS professionals and the absence of a career ladder for them in a small business. However, if the employees have adequate IS knowledge, they can contribute more eectively to the IS implementation through their involvement in the requirements and design phases. They will also have more realistic expectations from the IS and be more comfortable participating in the IS implementation process [66,73]. Insucient IS knowledge has been found to lead to IS selection failure and failure to use the IS [74]. Hence, it is hypothesized that a higher level of IS knowledge is likely to increase IS implementation success. Hypothesis 6: User information satisfaction is positively related to the level of users IS knowledge. 3.5. External expertise Due to the lack of inhouse IS expertise, small businesses are likely to be much more dependent on external expertise such as consultants and vendors [20,72]. According to Attewell s [53] theory, these external experts act as mediators to compensate for the lack of IS knowledge in the small business and lower the IS knowledge barrier to successful IS implementation. The responsibilities of a consultant are to provide consultancy service specically to help businesses implement eective IS. Consultancy service can include performing information requirements analysis, recommending suitable computer hardware and software, and managing the IS implementation [38]. The responsibilities of a vendor generally include providing the computer hardware, software packages, technical support, and training of users. It is also important to maintain a good working relationship among the various parties (i.e. the CEO, users, consultant, and vendor) in the IS implementation. In IS implementation of the small businesses, the vendor may also play the role of a consultant, and thus performs additional duties besides the usual responsibilities [58]. In view of the possibility of the consultant being the vendor, we will treat the responsibilities of the external experts as a combination of the duties of the consultant and the vendor. In the research model, external expertise is a second-order latent variable of both types of external expertise. There is some prior evidence to suggest positive correlations between user information satisfaction and external expertise [24,39]. Hence, it is hypothesized that user information satisfaction is likely to be high when the level of external expertise is high. Hypothesis 7: User information satisfaction is positively related to the eectiveness of external expertise. 4. Research methodology 4.1. Measurement of variables The measures used in this study were developed through an extensive literature review followed by iterative reviews by both practitioners and experienced IS faculty. Further, the measures had been used in prior studies and were found to demonstrate adequate reliability and validity. The questionnaires containing the measures were also pilot-tested before the main survey. Except for IS investment, the remaining variables were measured as perceptual items on seven-point Likert scales. Pre-tax prot and sales revenue were measured as seven-points Likert scales as small businesses are reluctant to reveal actual revenue or prot. In order to secure their assistance in completing the survey questionnaire, perceptual rather than objective measures were used. According to Dess and Robinson [75], subjective measures can be appropriate surrogates for organizational performance. Table 1 presents the measurement of the variables. 4.2. The sample There is no generally accepted denition of a small business. Three commonly used criteria for dening a small business are number of employees, annual sales, and xed assets [76]. In this study, the criteria for dening a small business were adopted from the Association of Small and Medium Enterprises (ASME) in Singapore. A small business must satisfy at least two of the following criteria: (1) number of employees should not exceed 100; (2) xed assets should not exceed US$7.2 million; and (3) annual sales should not exceed US$9 million. The names and addresses of small businesses that have computerized were obtained from a database maintained by the Singapore National Computer Board (NCB). The
148 J.Y.L. Thong / Omega 29 (2001) 143 156 Table 1 Measurement of variables Variable Measure Source User information satisfaction 1. Currency of reports [57,85] 2. Timeliness of reports adapted 3. Reliability of reports 4. Relevancy of reports 5. Accuracy of reports 6. Completeness of reports Organizational impact 1. Pre-tax prot [59,60] 2. Sales revenues 3. Sta productivity 4. Competitive advantage 5. Operating cost 6. Quality of decision-making CEO support 1. CEO attendance at project meetings [86] 2. CEO involvement in information requirements analysis 3. CEO involvement in reviewing consultant s recommendations 4. CEO involvement in IS decision-making 5. CEO involvement in monitoring project User involvement 1. User attendance at project meetings [86] 2. User involvement in information requirements analysis 3. User involvement in reviewing consultant s recommendations 4. User involvement in IS decision-making IS planning 1. Financial resources planning [86] 2. Human resources planning 3. Information requirements analysis 4. Implementation (software development, installation, and conversion) 5. Post-implementation (operation, maintenance, future needs) IS investment 1. Investment in computer hardware and software [4,70,87] Users IS knowledge 1. Years of computer experience [59] 2. Understanding of computers in comparison to persons in your position at other small companies 3. Number of computer courses taken Consultant eectiveness 1. Eectiveness in performing information requirements analysis [58] 2. Eectiveness in recommending suitable computer solution 3. Eectiveness in managing implementation 4. Relationship with other parties in the project (CEO, Users, Vendor) Vendor support 1. Adequacy of technical support during IS implementation [58] 2. Adequacy of technical support after IS implementation 3. Quality of technical support 4. Adequacy of training provided 5. Quality of training provided 6. Relationship with other parties in the project (CEO, Users, Consultant) NCB conducts a national IT survey on a large cross-section of business organizations every two years. Stratied random sampling was used to ensure that the sample was representative of the national prole. Hence, our sample was not a convenient sample per se. Nonprot businesses, public-listed businesses, and wholly owned subsidiaries of large businesses were excluded from the survey sample. Three hundred and four small businesses fulll the ASME criteria and were included in the survey sample. Two weeks after the questionnaires were mailed, follow-up telephone
J.Y.L. Thong / Omega 29 (2001) 143 156 149 calls were made to nonresponding businesses to encourage a higher response rate. One hundred and thirty small businesses responded, giving a response rate of 43%. Responses from 16 businesses were excluded from the nal sample because of incomplete data resulting in 114 usable sets of questionnaires. In order to assess the possibility of nonresponse bias, we compared the responses of the early returns with the late returns [77,78]. The rationale for this test is that late respondents are likely to have similar characteristics to nonrespondents. The MANOVA test did not detect any signicant dierences in the research variables (Wilks =0:958; F-sig: =0:919), and hence nonresponse bias was not a major concern. 4.3. Data collection The study was conducted in two phases: a pilot study and a questionnaire survey. Two questionnaires, the Project Manager Questionnaire and the Computer User-Manager Questionnaire, were designed for data collection. The rst questionnaire was to be completed by the inhouse person who was administratively responsible for IS implementation. It solicited data on the research variables and IS characteristics. The second questionnaire was to be completed by senior managers who were users of the IS and the computer-generated reports. It requested data on IS eectiveness and level of IS knowledge. Views from multiple respondents were solicited to provide a more representative picture of the IS implementation success in the small businesses. In the pilot study phase, ve small businesses were randomly chosen from the small business database to pretest the questionnaires. Five project managers and fteen managers who used the businesses IS completed the questionnaires. Interviews were conducted with these respondents in order to determine whether there were problems with the questionnaires. Based on their feedback, some statements were reworded and explanations were provided where necessary to clarify the questions. Responses from these ve small businesses were not included in the nal sample. In the questionnaire survey phase, a package containing four items was mailed to the CEO of each of the small business in the survey sample. The four items were: a cover letter, one Project Manager Questionnaire, three Computer User-Manager Questionnaires, and a prepaid envelope. The cover letter requested the CEO to direct the relevant questionnaires to the manager in charge of the IS implementation project and three senior managers who used the IS. The respondents were assured of the condentiality of their responses. They could also return the completed questionnaires in individually sealed envelopes. Interviews were also conducted with the project managers and computer user-managers in 67 of the small businesses. As far as possible, all managers who used the reports generated by the IS were included in the study. Responses from the managers were not revealed to each other or the CEOs of the small businesses. During the interviews, the respondents were asked to explain in greater detail their responses to the questionnaires and to qualitatively relate their experience with the IS implementation projects. The interviews helped us to interpret the questionnaire data through deeper insights into IS implementation issues faced by these small businesses. To check whether the CEOs in the noninterviewed businesses were biased in selecting managers who were more satised with the IS, a MANOVA test was conducted on the variables between the 67 interviewed businesses and the 47 noninterviewed businesses. No signicant dierence was found (Wilks = 0:905; F-sig: = 0:440). Thus, there is no evidence of selection bias. As the unit of analysis is at the organization level rather than at the individual user level, the managers responses were aggregated within each small business for purpose of statistical analysis. In this study, respondents were members of top management in the small businesses and were in a suitable position to view the success of the IS implementation. Aggregation of individual responses into an organizational response was done by averaging the scores by multiple respondents in the same business. The ANOVA tests revealed signicantly greater variance for user information satisfaction and users IS knowledge between the small businesses than within them, suggesting close agreement between respondents from the same business [4]. Hence, there was statistical support for aggregating the individual responses to the organization level. 5. Findings 5.1. Sample characteristics Table 2 presents the characteristics of the survey sample. The responding small businesses were from the manufacturing, commerce, and service sectors. They all satised the criteria of a small business as dened earlier. They employed an average of 50 employees and the mean annual sales was US$6 million. They had a mean of four years of computer experience, and the majority had spent more than US$30,000 on their IS implementation. Their hardware platforms were distributed evenly between microcomputers, microcomputers with local area networks, and minicomputers. Most of the small businesses had implemented operational and management information systems applications such as accounting systems, inventory control, sales analysis, sales order processing, and payroll. Finally, all of them had engaged external experts to implement their information systems. The eects of ve sample characteristics (number of employees, annual sales, computer experience, type of hardware, and business sector) on IS implementation success were examined. Correlation analysis showed that there was no evidence of signicant correlations at the 10% level between the IS eectiveness measures and the rst three variables. The eects of type of hardware conguration and
150 J.Y.L. Thong / Omega 29 (2001) 143 156 Table 2 Characteristics of sample Sector Service 31 Commerce 25 Manufacturing 55 Number of employees 1 24 48 25 49 24 50 74 15 75 100 21 100 5 Annual sales (US$ million) $1:499 28 1.5 $2.999 27 3.0 $5.999 14 6.0 $9.0 14 $9:0 25 Computer experience (yr) 0 1 18 2 3 34 4 5 22 6 10 32 10 8 Computer expenditure (US$ 000) 0 30 42 31 60 25 61 120 21 120 26 Hardware Minicomputers and microcomputers 43 Microcomputers and LAN 34 Microcomputers only 33 Top 10 software applications Accounts receivable 96 General ledger 90 Accounts payable 87 Inventory control 74 Sales analysis 50 Sales order processing 47 Payroll 40 Purchasing 29 Budgeting 24 Job Costing 23 a Figures may not add up due to missing data. Frequency (n = 114) a business sector on the IS eectiveness measures were tested using one-way ANOVAs. Similarly, there was no evidence of signicant relationships (p 0:10). In summary, these sample characteristics had no eect on IS implementation success. 5.2. Statistical analysis Partial least squares (PLS), a powerful second-generation multivariate analysis technique or structural equation modeling technique, was used for hypotheses testing. PLS is an approach to assess a causal model involving multiple variables with multiple observed items by simultaneously assessing both the structural model and the corresponding measurement model in an optimal fashion [30,31]. It addresses both models at the same time; compared to factor analysis which assesses the measurement model only and path analysis which addresses the structural model alone. Hence, PLS is superior to traditional regression and factor analysis [31,79,80]. Fig. 1 presents the structural model in this study. The structural model describes the relationships among the variables. Further, for each variable in the structural model, there is a related measurement model (not shown in the gure), which links each variable in the diagram with its respective set of items. The items are listed in Table 1. 5.2.1. Testing the measurement model Testing the measurement model involves examining internal consistency, convergent validity, and discriminant validity. In PLS, internal consistency of the variables are assessed with composite reliability. The advantage of composite reliability over Cronbach s alpha is that it is not inuenced by the number of items in the scale [30]. Nunnally s [81] guideline of 0.80 for assessing internal consistency can be used to assess composite reliability. Convergent validity is the degree to which two or more items measuring the same variable agree [82]. In PLS, two tests may be used to assess convergent validity. The rst test is item reliability, which is measured by the factor loading of the item on the variable. There is no generally accepted level of what constitutes an acceptable factor loading in PLS analysis. Fornell [79] recommended a minimum loading of 0.70 which suggests that the item explains almost 50 percent of the variance in the variable; while Falk and Miller [83] recommended a loading should be at least 0.55 which explains at least 30 percent of the variance in the variable. Traditionally, researchers have used the 0.50 level [81]. The second test is average variance extracted by each variable. Fornell and Larcker s [30] criterion that the average extracted variance should be 0.50 or more is usually used to assess the average variance extracted coecients. Table 3 presents the results of tests for internal consistency and convergent validity. The composite reliabilities of all the variables were at least 0.80. All the factor loadings were greater than 0.55. While there were some loadings below the stricter limit of 0.70, the average variance extracted of the variables were high. Using the average variance extracted, which was a more conservative test of convergent validity, all the variables had gures that exceeded 0.50. Hence, the variables in the measurement model
Table 3 Assessment of the measurement model in PLS J.Y.L. Thong / Omega 29 (2001) 143 156 151 Variable Mean Standard Factor Composite Average variance deviation loading reliability extracted User information satisfaction (1 7 scale) 0.93 0.65 Satisf1 5.55 0.95 0.82 Satisf2 5.55 0.87 0.81 Satisf3 5.52 0.99 0.86 Satisf4 5.73 0.83 0.85 Satisf5 5.73 0.93 0.86 Satisf6 5.38 0.82 0.82 Organizational impact (1 7 scale) 0.89 0.58 OrgImp1 4.47 0.96 0.79 OrgImp2 4.47 0.93 0.83 OrgImp3 5.33 1.04 0.79 OrgImp4 4.93 1.01 0.82 OrgImp5 4.18 1.20 0.65 OrgImp6 5.16 0.88 0.69 CEO support (1 7 scale) 0.92 0.70 CEO1 4.89 1.66 0.79 CEO2 4.89 1.48 0.83 CEO3 5.17 1.57 0.91 CEO4 5.60 1.42 0.79 CEO5 4.99 1.55 0.86 User involvement (1 7 scale) 0.87 0.64 Involve1 5.33 1.43 0.96 Involve2 5.34 1.36 0.92 Involve3 4.71 1.49 0.58 Involve4 5.13 1.39 0.66 IS planning (1 7 scale) 0.85 0.53 Plan1 4.67 1.36 0.65 Plan2 4.51 1.25 0.72 Plan3 5.09 1.24 0.83 Plan4 4.97 1.16 0.76 Plan5 4.84 1.12 0.68 IS investment (US$ 000) 114 37 1.00 1.00 1.00 Users IS knowledge 0.80 0.58 CKnow1 (yr) 5.19 3.42 0.71 CKnow2 (1 7 scale) 4.48 1.14 0.96 CKnow3 (number) 2.99 3.99 0.56 Consultant eectiveness (1 7 scale) 0.90 0.70 Consult1 5.03 1.13 0.81 Consult2 4.53 1.28 0.78 Consult3 4.76 1.24 0.88 Consult4 5.13 1.28 0.88 Vendor support (1 7 scale) 0.95 0.75 Vendor1 4.76 1.46 0.87 Vendor2 4.48 1.71 0.83 Vendor3 4.67 1.51 0.86 Vendor4 4.29 1.60 0.90 Vendor5 4.28 1.60 0.90 Vendor6 4.79 1.31 0.84
152 J.Y.L. Thong / Omega 29 (2001) 143 156 Table 4 Discriminant validity of measurement model in PLS Variable 1 2 3 4 5 6 7 8 9 1. User information satisfaction 0.650 a 2. Organizational impact 0.200 0.580 3. CEO support 0.037 0.031 0.700 4. User involvement 0.063 0.108 0.178 0.640 5. IS planning 0.056 0.122 0.147 0.073 0.530 6. IS investment 0.020 0.002 0.000 0.000 0.000 1.000 7. Users IS knowledge 0.066 0.029 0.008 0.025 0.050 0.068 0.580 8. Consultant eectiveness 0.062 0.042 0.061 0.072 0.154 0.044 0.056 0.700 9. Vendor support 0.191 0.020 0.015 0.022 0.070 0.002 0.011 0.304 0.750 a Diagonals represent the average variance extracted; other entries represent the shared variances. Shared variances = 0:000 means below 0.001. demonstrated adequate internal consistency and convergent validity. Discriminant validity is the degree to which items differentiate between variables or measure dierent variables [82]. Discriminant validity can be assessed by examining the correlations between variables. Each item should correlate more highly with other items of the same variable than with items of other variables. To assess this, the squared correlation (shared variance) between two variables should be less than the average variances extracted by the items measuring the variables [30]. Table 4 presents the results of the test for discriminant validity. In all cases, the shared variance between two variables was less than the average variances extracted by the items measuring the variables. Hence, the requirement for the test of discriminant validity was satis- ed, indicating that the measurement model discriminated adequately between the variables. 5.2.2. Testing the structural model Following conrmation of adequate psychometric properties in the measurement model, we proceeded to examine the structural model. This evaluation consisted of an assessment of the explanatory power of the independent variables, and an examination of the size and signicance of the path coecients. Jackkning, a nonparametric technique, was used to test the signicance of the paths. Fig. 2 presents the test of the structural model. The model accounted for 26% of the variance in user information satisfaction and 20% of the variance in organizational impact. The percentage of variance explained was greater than 10%, implying a satisfactory and substantive model [83]. All the standardized path coecients were signicant at the 5% level. However, a path coecient may be statistically signicant but not meaningful. Pedhazur [84] recommended using the 0.05 level as the threshold point. Following this guideline, hypotheses 1, 2, 3, 5, 6, and 7 were supported; only hypothesis 4 was not supported. The results show that user information satisfaction is a key mediator between the antecedent factors and eventual organizational impact on the small businesses. Among the Fig. 2. Assessment of structural model. key factors, external expertise is the variable most closely related to user information satisfaction. 6. Discussion The data analysis shows that external expertise is more important than the other key factors in IS implementation success of small businesses. In general, small businesses do not have the resources to hire internal IS expertise and face diculties in recruiting and retaining IS professionals. But this lack of internal IS expertise may be compensated for by engaging experienced external expertise, in the forms of consultants and IT vendors, when undertaking IS implementation. Attewell [53] has argued that external institutions
J.Y.L. Thong / Omega 29 (2001) 143 156 153 play critical roles in lowering the knowledge barriers to IS diusion, making it easier for businesses to adopt and use the IS without extensive inhouse expertise. In the case of small businesses, consultants and IT vendors perform the role of external institutions that lower the knowledge barriers and make it easier for small businesses to implement IS successfully. In a small business with its simple organizational structure and limited inter-personal and departmental politics, IS implementation is essentially a technical matter. Under such circumstances, it is imperative to engage external IS experts who are experienced, understand the requirements of small businesses, and able to maintain good working relationships with all concerned parties. This was borne in upon us in the interviews where the quality of the external expertise was heavily stressed. The level of users IS knowledge is another important factor of IS implementation success. If the managers in the small businesses have high levels of IS knowledge, the resulting IS are more likely to be successful. Potential IS users should be sponsored by their companies to attend computer courses to increase their appreciation of the IS implementation process and the potential benets of IS implementation. With increased IS knowledge, these potential users will be able to contribute more eectively to the IS implementation process and develop more realistic expectations of the IS. Hence, taken together with the importance of external IS expertise, a major nding of this study is that the lack of IS expertise, whether internal or external, is the primary barrier to successful IS implementation and is consistent with Attewell s [53] notion of knowledge barriers to successful IS implementation. IS investment is the second most important determinant of IS implementation success. This nding provides empirical support for Ein-Dor and Segev s [2] hypothesis that budgeting adequate nancial resources will increase the likelihood of IS implementation success. DeLone [1] has also found that small businesses tend to spend proportionately less of revenue on IS implementation than large businesses. If small businesses could allocate sucient resources for IS investment, not withstanding their tight cash ow, they will be able to engage more experienced external experts and contract for better IS that meet their objectives. If small businesses decide to choose the lowest-cost external experts and IS solutions, they may end up with IS solutions that do not meet their business requirements. These scaled-down IS solutions could ultimately even end up as white elephants, as observed in some of the small businesses interviewed. Thus, small businesses that are able to acquire the needed capital for IS investment are more likely to secure successful IS. The interviews also revealed that small businesses that lack nancial resources need to be proactive in securing low-interest bank loans to nance their IS investment. After technical expertise and nancial resource variables, time-constrained variables are the next most important factors for IS implementation success. Due to time constraints in small businesses, the CEOs and potential IS users could not spend adequate time on IS planning and IS implementation. Among the three a priori time-constrained variables, user involvement in IS implementation is the most important for successful IS implementation. If the potential IS users participate actively in the process of IS implementation, they will be able to ensure that their suggestions and requirements are incorporated into the IS, feel a sense of ownership over the nal IS, and lower their resistance to adapt to new work procedures. Adequate involvement of users can compensate for the lower level of CEO involvement. While the CEOs should be involved in key decisions aecting the business and business processes, they need not be actively involved throughout the IS implementation process. In fact, given the heavy demand on the CEOs time and attention, it is impractical to advise CEOs of small businesses to devote a signicant amount of attention to the IS implementation project. Surprisingly, the level of IS planning has no eect on IS implementation success. A possible explanation that emerged from the interviews is that most small businesses do not consciously conduct IS planning. Beyond deciding on how much to spend on IS, they are likely to depend on the external experts in formulating other details of IS planning. In this study, IS planning is positively correlated with the level of consultant eectiveness. Due to the recognized importance of IS planning in prior IS implementation research in large businesses, further studies need to be conducted to determine the specic attributes of IS planning that may have an eect on IS implementation success in small businesses. There are three limitations that should be noted in interpreting the ndings of this study. First, as this is a cross-sectional study, causality of relationships cannot be demonstrated completely. Further, feedback eects cannot be investigated. Longitudinal studies are needed to con- rm the direction of causality and test for feedback eects. Second, signicant percentages of the IS implementation success variances remain unexplained. More research on this important topic is needed. Finally, in making generalization from the research sample, one has to take into consideration the context of Singapore, a newly industrialized Asian country. The ndings may not be universally true, but they are likely to be applicable to IS implementation success in small businesses with similar cultural contexts [39]. Findings from this study may also be applicable to small businesses in developing countries that are interested in adopting IS. 7. Conclusion This paper has developed and tested a resource-based model of IS implementation success in small businesses. Based on Welsh and White s [43] framework of resource constraints in small businesses and Attewell s [53] knowledge barrier theory, three types of resource constraints were conceptualized: time constraint, nancial constraint, and
154 J.Y.L. Thong / Omega 29 (2001) 143 156 expertise constraint. Various key factors of IS implementation success were identied as technical expertise resource, nancial resource, and time resource. The relative importance of these key factors of IS implementation success was examined using a second generation multivariate technique or structural equation modeling. The PLS analysis showed good support for the IS implementation model with the external technical expertise factor being the most important followed by the nancial and time-constrained factors. User information satisfaction was found to be a signicant mediator between these factors and the eventual organizational impact of the IS. The implication for small business management is that to achieve a high level of IS implementation success, they should direct their eorts at lowering three types of resource barriers. The rst resource barrier is technical expertise constraints. Due to the lack of internal IS expertise, small businesses need to engage experienced consultants and IT vendors to undertake their IS implementation. They should also increase the level of IS knowledge among potential IS users by sending employees for computer courses and training. The second resource barrier is nancial constraints. Small businesses need to allocate sucient funds for their IS investment. While they should not throw money at IS investment, which has not been shown to be eective, they should also not opt for the lowest-cost solutions which do not fulll their business requirements. Rather than purchase more expensive custom-developed software, well-tested dedicated packages may well suit their needs. The third resource barrier is time constraints. The busy CEO should ensure that potential IS users are given time-o from their normal duties to participate in the IS implementation process. Potential IS users can provide useful inputs that will lead to IS that better meet the requirements of the small business. The implication for research is that the resource-based view of the rm and the resulting framework of resource constraints are useful theories to ground future research on IS implementation in small businesses. In this study, the resource-based view was applied in conjunction with the unique characteristic of small businesses and Attewell s [53] knowledge barrier theory to conceptualize the key factors of IS implementation success in small businesses in terms of three types of resource constraints. The proposed research model for IS implementation in small businesses is one of the few models that are guided by a strong theory. Future research can extent the proposed model by examining other key factors due to the three types of resource constraints that may lead to successful IS implementation in small businesses. 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