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European Management Review (2010) 7, 180–189

& 2010 EURAM Macmillan Publishers Ltd. All rights reserved 1740-4754/10 palgrave-journals.com/emr/

Enterprise system investments for competitive advantage: An empirical study of Swiss SMEs Gianfranco Walsh1, Petra Schubert2 and Colin Jones3 1

Institute for Management, University of Koblenz-Landau, Koblenz, Germany; Copenhagen Business School, Centre for Applied Information and Communication Technologies, Frederiksberg, Denmark; 3 Australian Innovation Research Centre, University of Tasmania, Hobart, Australia 2

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Correspondence: Gianfranco Walsh, Institute for Management, University of Koblenz-Landau, Universita¨tsstrasse 1, Koblenz 56070, Germany. Tel: þ 49 261 287 2852; Fax: þ 49 261 287 2851; E-mail: [email protected]

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Abstract Previous research identifies various reasons companies invest in information technology (IT), often as a means to generate value. To add to the discussion of IT value generation, this study investigates investments in enterprise software systems that support business processes. Managers of more than 500 Swiss small and medium-sized enterprises (SMEs) responded to a survey regarding the levels of their IT investment in enterprise software systems and the perceived utility of those investments. The authors use logistic and ordinary least squares regression to examine whether IT investments in two business processes affect SMEs’ performance and competitive advantage. Using cluster analysis, they also develop a firm typology with four distinct groups that differ in their investments in enterprise software systems. These findings offer key implications for both research and managerial practice. European Management Review (2010) 7, 180–189. doi:10.1057/emr.2010.12; published online 12 August 2010 Keywords: competitive advantage; IT investment; SME; Switzerland

Introduction n element of firms’ competitive strategy that has attracted substantial research attention in the past three decades is the use of information technology (IT), especially enterprise software systems1 (Henderson and Venkatraman, 1999; Schubert, 2007). IT confers competitive advantages because it enables firms to increase their levels of coordination, productivity, differentiation, and product customization (Porter and Millar, 1985; Hu and Quan, 2005; Schubert and Williams, 2009). For example, McAfee and Brynjolfsson (2008) note that between 1994 and 2004, US firms enjoyed annual productivity gains of about 3% through investments in IT compared with approximately 1.4% in the previous 20 years. McAfee and Brynjolfsson (2008) and others attempt to link investments in IT to competitive advantage and firm performance, but despite the robust support for the notion that companies investing in enterprise software systems increase opportunities for success, this research stream contains at least three gaps.

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First, relatively few studies consider small and mediumsized enterprises’ (SMEs) IT investments in different functional areas and the utility associated with each type of investment (cf. Parker and Castleman, 2007). Yet in Europe, SMEs (defined as firms with fewer than 250 employees and annual turnover of less than h50 million) constitute the majority of businesses, and as a proportion of all business, they account for a large percentage of both employment and turnover (Beaver and Prince, 2004; Meckel et al., 2004; Eurostat, 2008). These SMEs differ from larger enterprises in various aspects, including their workflow, decision-making processes, levels of hierarchy, resources, and corporate culture. Therefore, management knowledge, such as that pertaining to IT investments, that has been developed in relation to large enterprises often is not applicable to SMEs (e.g., Dandridge, 1979; Deros et al., 2006; Radas and Boz˘ic´, 2009).

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to achieve competitive advantages (Barney, 1991; Peteraf and Barney, 2003). The RBV further suggests that sustained performance differences result from variation in resources and capabilities across firms (e.g., Mata et al., 1995). Following Wade and Hulland (2004: 109) who define resources as ‘assets and capabilities that are available and useful in detecting and responding to market opportunities or threats’, we view IT as an important firm resource. Investments in IT can but do not necessarily translate into IT capabilities (Bharadwaj, 2000). For example, according to the RBV (e.g., Clemons, 1991; Bharadwaj, 2000), if more than one firm possesses a resource such as IT capabilities (i.e., no heterogeneity), ceteris paribus that resource cannot contribute to competitive advantage. In turn, varying levels of investments in IT (i.e., competency developments) could equate to varying levels of IT success, which themselves may be a source of competitive advantage (Henderson and Venkatraman, 1999). Similarly, Tapscott (2001) argues that common forms of IT grant different firms varied value creation opportunities. Also consistent with the RBV, Schubert (2007) analyses case studies and provides support for the notion that enterprise systems help companies support their core processes and thus sustain a competitive advantage. In a recent study of 46 firms from one US state, Dibrell et al. (2009) demonstrate that investments in IT assets positively affect firm performance. Because these authors do not report the exact composition of their sample, it is unclear if and how many SMEs they surveyed. Caldeira and Ward (2003) examine the adoption and use of IT by 12 Portuguese SMEs; they report these SMEs did not consider access to unique software critical to their business and some even sold their software to potential competitors. However, Caldeira and Ward (2003) focus on a relatively small number of manufacturing firms, which limits the generalizability of their findings. McFarlan (1984) offers a typology of companies according to their IT use in the turnaround, strategic, factory, and support fields. Unlike that study, which investigated the use of IT, we classify firms according to their level of IT investment. Finally, Peppard and Ward (2004) show that organizations can continuously derive and leverage value from IT, and Hitt et al. (2002) find that adopters of cross-functional enterprise systems perform consistently better across a wide variety of measures than do non-adopters. Overall then, the issue of achieving a competitive advantage through IT remains somewhat unclear. Differences of opinion particularly remain with regard to which drivers represent ‘assets’ for developing a competitive advantage through IT adoption and development. Generally speaking, combinations of human, technology, and relationship assets should support IT processes (planning ability, cost-effective operations) and other intangible IT resources that provide a basis for greater performance than competitors (Ross et al., 1996). Wade and Hulland (2004) emphasize that strong top management support facilitates strong IT capabilities and an IT resource–performance link (see also Peppard and Ward, 2004). Following this line of reasoning, firms should make IT investments in primary process areas, because gains in those areas (e.g., coordination, productivity) will be more widely felt by customers and therefore lead to improved

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Second, some research notes the extent of IT investments in firms (e.g., Silvius, 2006), yet no studies specifically examine the utility that firms derive from those investments, especially in relation to their functional areas. Third, extant studies in the field tend to consider the totality of IT investments in relation to firm performance (Silvius, 2006; Schubert and Williams, 2009). This important oversight hinders our understanding of the specific pathways (i.e., functional areas) through which IT investments lead to changes in firm performance. To address these gaps, we examine SMEs’ decisions and assessments about IT investments, specifically in enterprise systems. We draw on the resource-based view (RBV) and predict a positive relationship between SMEs’ investments in IT and financial and non-financial measures of firm performance (i.e., sales and efforts to gain competitive advantages). According to the RBV, differences in firm performance result from the heterogeneous resources and capabilities across firms (Barney, 1991), and IT is a key resource that facilitates business processes across functional areas. Therefore, we also compare investments in enterprise systems in different functional areas and the perceived utility of these investments, according to managers in the firms. To address the third research gap, we examine how IT investments in both primary and second value-generating activities affect sales. This activity classification follows Porter’s (1985: 38) definition of primary activities as those ‘involved in the physical creation of the product and its sale and transfer to the buyer as well as after-sale assistance’. Secondary or support activities instead entail complementary elements, such as technology, human resource management, and other firm-wide functions. Together, these value activities build competitive advantage, and by considering IT investments in primary and secondary activities separately, we heed Clemons and Row’s (1991) call for more empirical research into the link between IT investment and performance. For this investigation, we use data from more than 500 Swiss SMEs and test the hypothesized relationships with regression analyses. We adopt Porter’s (1985) value-chain concept in developing our hypotheses and designing the survey because it helps highlight the role of IT as a means to connect departments within the firm. Our analysis also enables us to identify company clusters on the basis of their IT investments and degree of competitive advantage in the market. This classification is of significant interest, in line with McAfee and Brynjolfsson’s (2008: 100) claim that ‘a new competitive dynamic has emerged y [with] greater gaps between leaders and laggards’, even as levels of IT investment have risen rapidly. With this study, we aim to augment existing literature by empirically investigating the perceived gains from IT investments in terms of development of a competitive advantage. Prior literature and hypotheses development Understanding how firms gain and sustain competitive advantages is a challenge for both the theory and the practice of management. The RBV argues that firms represent a collection of capabilities and possess tangible and intangible resources, a subset of which can enable them

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Firms can create and sustain a competitive advantage through various means that improve their cost structure, product quality, or customer service (Ireland et al., 2009). Therefore, we consider several activities geared towards achieving a competitive advantage (see the Appendix) and logically map them onto Porter’s (1985) framework of the sources of competitive advantage, such as cost leadership and market differentiation. We agree that a competitive advantage from IT investment relates to the way IT is managed and how the firm develops IT capabilities, yet we also argue that IT investments are the sine qua non of an IT competitive advantage. This view is consistent with the RBV, because it implies resource heterogeneity. Firms differ in their IT implementation (Delmonte, 2003), and therefore the main competitive advantage gained from IT should relate to the size of the IT investment rather than its management. This view also is consistent with conventional wisdom, which suggests that managers should invest in areas that promise the highest returns and maximize profits (Barney and Arikan, 2001; Grant, 2002). The higher the IT investment, the greater the firm’s ability to increase the value of its coordinated resources through economies of scale and scope (Clemons and Row, 1991). In addition, Feeny and Ives (1990) argue that IT can improve a firm’s market responsiveness, because it enables firms to link differentiated functions and resources for increased efficiency and quicker responses to market changes. The IT-derived ability to increase coordination, productivity, and reaction times represents a critical competitive advantage.

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Hypothesis 2: An SME’s level of investment in IT to support its secondary activities is positively associated with its performance.

Data collection We present a cross-sectional analysis of Swiss companies with 10–250 employees in different business sectors (i.e., all sectors included in the Swiss NOGA code). From a sample frame of 38,099 firms, the Swiss Federal Office of Statistics drew a stratified random sample of 4,393 companies. Over a 3-week period in autumn 2007, telephone interviews were conducted, using a standardized questionnaire available in both German and French. The questionnaire, which we developed in cooperation with business partners and pre-tested, aimed at senior managers working for Swiss SMEs. It contained three sections with questions about firm-related information (e.g., sector, size), IT investments by the firm, and the respondent’s perception of the utility of the IT investments. This data collection process yielded a sample of 917 respondent firms. After we excluded 334 firms with missing responses, a final sample of 583 firms remained for this study. Of these firms, the largest proportion represented the manufacturing sector (n ¼ 114; 19.6% of sample), followed by firms in retailing, maintenance, or repair services sector (n ¼ 98; 16.8%), and then the real estate, rent and lease, and data processing sector (n ¼ 44; 7.5%). Nearly all respondents were senior managers; specifically, 55% of the questionnaires represented responses by CIOs, 23% by CEOs, and 19% by other executives in commercial and technical areas, and only 3% of the respondents performed other functions in the company. The distribution of companies according to their size was balanced, according to the number of employees (or fulltime equivalent). That is, the total average number of employees was 82 (SD ¼ 65), 38% of the companies employed between 10 and 49 people, 29% between 50 and 99, and 33% between 100 and 250 employees.

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Hypothesis 1: An SME’s level of investment in IT to support its primary activities is positively associated with its performance.

Empirical analysis and results

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performance. Drawing on prior research (Powell and Dent-Micallef, 1997; Melville et al., 2004; Leem et al., 2007), we argue that firms can increase their competitiveness through investments in IT that support primary activities and processes, which contribute directly to the fulfilment of customer needs. However, a company also must manage its secondary processes to maintain its operations (Schubert and Wo¨lfle, 2007). Even if these activities do not contribute to firm value generation, they are essential to running the company and contribute to its overall productivity (Porter, 1985). Recent research suggests that many SMEs use crossfunctional enterprise systems to support their secondary processes (Dettling et al., 2004; Schubert, 2007). In turn, we posit:

Hypothesis 3: An SME’s level of investment in IT to support its primary activities is positively associated with its efforts to gain competitive advantage. Hypothesis 4: An SME’s level of investment in IT to support its secondary activities is positively associated with its efforts to gain competitive advantage.

Analysis: descriptive results We focus particularly on cross-functional enterprise systems, which provide interrelated software modules to support primary and secondary processes in a company. Therefore, we first interviewed companies about their past investments in enterprise systems for 11 process areas (see Table 1), asking, ‘To what extent has your firm made IT-related investments (in the last three years) in the following functional areas: [Supplier Relationship Management, Procurement and procurement processes, etc.]’. They indicated the expected utility of the use of enterprise systems in each area by responding to the following stem: ‘What utility did your firm expect to gain from IT-related investments in the following functional areas over the last three years: [Supplier Relationship Management, Procurement and procurement processes, etc.]?’ The questions referred to a 3-year time window to include as many managers as possible who worked for the firm when the IT investment was made. The utility measure reflected the return on investment (ROI). For these questions, the respondents used 5-point response scales, with scale endpoints of low level of investment/utility (1) and high level of investment/utility (5).

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183 Table 1 Comparison investment in IT and expected utility of IT investment

Functional area

Expected utility of IT investment

Level of investment in IT

Mean difference

3.69 3.35 3.22 3.40 3.02 3.33 3.64 3.37 3.70 3.43 3.57

3.88 3.76 3.86 3.92 3.80 3.77 3.84 3.98 4.00 3.84 3.96

0.19 0.41 0.64 0.52 0.78 0.44 0.20 0.61 0.30 0.41 0.39

Procurement Supplier relationship management (SRM) Materials logistics Processing of orders Sales processes Marketing/CRM Customer service Collaboration Accounting and finance Human resource management/payroll accounting General management

Scale: 1 ¼ low level of investment/utility; 5 ¼ high level of investment/utility.

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measures of firms’ IT investments in primary and secondary activities. The first factor includes all areas that represent primary processes (see Table 2); we refer to it as primary processes modules. The second factor contains accounting and finance, human resource management, payroll accounting, and general management (i.e., reporting, business intelligence), and we name it secondary processes modules. The former factor comprises eight items, and the second contains three items. Using AMOS 16, we assessed a two-factor measurement model according to its sample variance-covariance matrix and maximum likelihood estimation. This measurement model revealed a good fit, with w2/df ¼ 3.64, Po0.001, goodness of fit index ¼ 0.95, comparative fit index ¼ 0.97, root mean square residual ¼ 0.056, and root mean square error of approximation ¼ 0.068, all of which meet the usual conventions (Hu and Bentler, 1999). All items exhibit standardized coefficients above 0.70 (Nunnally, 1978), and the average variance extracted (AVE) is greater than the critical value of 0.50 for both factors (see Table 2). To assess the discriminant validity of our scales, we applied Fornell and Larcker’s (1981) criterion; the AVE for any two constructs exceeds the squared correlation between them, such that we establish discriminant validity for all our constructs. The Cronbach’s a, a measure of the reliability (or internal consistency) of a measurement scale, exceeds the recommended threshold of 0.70 for both factors. However, because our use of the same source data raises the potential for common method bias, we also conducted a CFA with Harman’s single-factor approach (Podsakoff et al., 2003). If common method bias is a threat, a single latent factor should yield a better fit than the two-factor measurement model. In contrast, our examination indicates a worse fit for a single-factor model, which indicates that a common factor bias does not pose an important threat.

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As we show in Table 1 (last column), the highest IT investments went to the accounting and finance functional area, probably because many SMEs have begun using electronic interfaces to exchange data with their banks or government agencies (e.g., DATEV standard interface, ELSTER method to submit tax declarations electronically). This finding is particularly interesting because accounting and finance, according to Porter’s (1985) terminology, are secondary activities. Other areas that attract high IT investments are general management, processing of orders, and collaboration; in this context, the latter refers to the exchange of data across company borders. Overall, we find a high degree of IT investments, which suggests that the majority of Swiss SMEs consider their investments in these areas important. These results align with those from a previous study that show accounting and finance (55.8%), human resource management (46.4%), and sales processes (39.1%) are the three most important enterprise systems modules for SMEs (Dettling et al., 2004). However, the values for the expected utility of investments in enterprise systems modules are lower (second column) than that for the prior investments (third column). These figures reflect approximately even estimates of the future ROI in different areas, though the highest expected return level again cites accounting and finance (3.70), a supporting function that does not contribute directly to the firm’s value generation. This high value reflects the importance SMEs attach to a smooth, fully functional accounting and finance system. Procurement (3.69) has the second highest utility score, which again underlines the continuing trend towards electronic support of the buying process. In a recent study of 200 leading Swiss companies, almost 80% of the respondents stated that IT contributes critically to the successful operation of the procurement function (Tanner et al., 2008). The third highest expected ROI comes from customer service (3.64). Finally, we note the relatively large discrepancy between investments and expected utility in two primary process areas – material logistics and sales processes. Descriptive statistics, reliability, and validity Before testing the hypotheses, we estimated a confirmatory factor analysis (CFA) to test the adequacy of our two

Analysis: hypotheses testing Rouse and Daellenbach (1999: 488) argue that ‘the presence of competitive advantage is normally inferred from sustained periods of above-average performance’. Therefore, we consider one key performance measure: change in

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184 Table 2 Confirmatory factor analysis results

Cronbach’s a/AVE/Factor loading (from CFAa ) Factor 1: Primary Processes Modules Supplier relationship management (SRM) Procurement and procurement processes Materials logistics/merchandise management Customer service Sales processes Marketing/customer relationship management (CRM) Collaboration with market partners Processing of orders

a ¼ 0.81; AVE ¼ 0.57; CR ¼ 0.91 0.82 0.79 0.78 0.76 0.76 0.70 0.71 0.70

Factor 2: Secondary Processes Modules Accounting and finance Human resource management/payroll accounting General management (reporting, business intelligence)

a ¼ 0.87; AVE ¼ 0.64; CR ¼ 0.84 0.84 0.80 0.76

CFA ¼ confirmatory factor analysis; a ¼ Cronbach’s a; AVE ¼ average variance extracted; CR ¼ composite reliability.

Table 3 Multinominal logistic regression results

1.067E3 1.051E3

Degrees of freedom

Significance

17.717 2.432

4 4

0.001 0.657

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Dependent variable: Change in sales.

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Factor 1: Primary Processes Modules Factor 2: Secondary Processes Modules

w2

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2 log-likelihood of reduced model

Effect

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sales (Marr, 2005), measured as the percentage change in sales over a 3-year period. We test our first two hypotheses with multinomial logistic regression, because the dependent variable (change in sales) is a nominal (categorical) variable. The pseudo R2 statistics indicate a strong goodness of fit; the Nagelkerke value is 0.48 and provides an estimate of the variance explained in the dependent measure by the independent variables (i.e., Factors 1 and 2). In our case, it indicates that the logistic regression model can explain 48% of the variance (see Table 3). Moving from the general to the specific, we also glean that investments in IT that support primary processes (Factor 1) positively affect changes in sales, in support of Hypothesis 1. However, in contrast with Hypothesis 2, investments in IT that support secondary processes (Factor 2) have no such effect. That is, IT investments in secondary process areas behave like a hygiene factor: A lack of IT investments causes deteriorating performance, but IT investments do not automatically lead to performance gains. To test Hypotheses 3 and 4, we used an ordinary least squares regression, because in these tests, the dependent variable (i.e., efforts to gain competitive advantage) is a discrete variable. We included firm type2 and firm size (i.e., number of employees) as control variables. Regarding firm type, Stabel and Fjeldstad (1998) argue that the process of value creation in manufacturing and service firms differs, such that manufacturing firms create value in accordance with Porter’s (1985) inputs–outputs value-chain logic, whereas service firms likely create value through unique allocations of resources on a project-by-project basis. The

predicted relationships therefore may differ between manufacturing and service firms. Furthermore, previous studies find an association between firm size and its intention to invest in IT (Nagm and Kautz, 2007). The multiple linear regression results show a similar pattern. Specifically, as we detail in Table 4, investments in IT that support primary processes (Factor 1) relate positively to firms’ assessments of efforts to gain competitive advantage, whereas we find no such relationship for investments in IT that support secondary processes (Factor 2). That is, we find support for Hypothesis 3 but not Hypothesis 4. Both independent variables represent a firm’s investment decisions, which may suggest possible multicollinearity problems. We therefore computed variance inflation factor scores and achieved a result of 1.031 (Hair et al., 1998). Multicollinearity is not an issue (see Table 4). The results also suggest that neither firm type nor firm size add to the explanation of SMEs’ efforts to gain competitive advantage. Analysis: identifying company clusters After establishing, in our dependency analyses, a differential effect of the independent variables (i.e., IT investments in primary and secondary processes) on financial and non-financial performance outcomes, we aimed to investigate whether the independent variables also could be used to develop a typology of firms that differ in their IT investments and other relevant variables. Previous research suggests that firms can be grouped according to their IT investments (e.g., McFarlan, 1984; Day, 1994). To identify groups of firms, we performed a

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185 Table 4 Regression analysis of the independent variables determining SMEs’ activities geared towards gaining competitive advantage

Factor 1: Primary Processes Modules Factor 2: Secondary Processes Modules Firm size (number of employees) Firm type (manufacturing vs services)

Beta (b)

t-value

Variance inflation factor

0.31** 0.05 0.03 0.06

6.434 1.265 0.878 1.225

1.023 1.022 1.017 1.031

Summary results: R ¼ 0.55; R 2 ¼ 0.30; adjusted R 2 ¼ 0.29; standard error of estimate ¼ 0.60. **Po0.01.

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processes modules factor, which suggests they consider IT investments a means to differentiate themselves from their competitors. However, these firms are less likely to invest in IT that supports basic business functions and the secondary processes modules. They may have invested substantially in basic software modules in the past, leaving them ‘saturated’ in this area. More than one-quarter of firms in this cluster belong to the manufacturing sector; they also have the highest average number of employees. Also, firms in this group report more activities targeted at creating a competitive advantage, consider it important to apply new technologies, and think they can derive a competitive advantage from IT. More than 67% of firms in this cluster report recent sales growth of at least 2%. The IT sceptic firms in segment 3 are the second smallest group. More than 18% of them belong to the manufacturing sector, but this cluster contains firms with the lowest average number of employees. One of their defining characteristics is that they score low on both factors, reaching the lowest score on secondary processes modules. These firms appear to find no real value in investing in IT; specifically, they do not agree with the statement ‘We need to apply technologies’ and, especially compared with segments 1 and 2, perceive less value from investing in IT as a means to gain competitive advantage. It is perhaps not surprising that these firms report flat to low sales growth. Perhaps these IT sceptics believe their business processes already have been optimized and will not change much in the future. Alternatively, perhaps these firms have invested in IT in the past but experienced no performance gains, leading to their current scepticism. Only 45% of firms in this cluster report growth in sales of at least 2%. Finally, the firms in segment 4, which is the smallest of the four clusters, agree that investing in secondary processes modules makes good business sense but think investing in primary processes modules does not. Fifteen per cent of these firms belong to the manufacturing sector. They are the least likely to engage in efforts to gain a competitive advantage and seem less likely to subscribe to the notion that IT helps them do so. Therefore, the firms in this group can be described as IT pragmatists. Their sales growth is lower than that of firms in segments 1 or 2. Overall, the results of the cluster analysis show that firms attach varying levels of importance to IT. For example, whereas IT differentiators value the use of IT and identify business opportunities from it, IT pragmatists completely disagree and use IT only to run their business, not to improve or gain competitive advantage from it. These cluster analysis results are consistent with the results of the

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hierarchical cluster analysis followed by a k-means clustering, in which we estimated each firm’s relative standing on two factors, namely, investment in IT that support primary processes (Factor 1) and secondary processes (Factor 2). We computed the factor scores (from an exploratory factor analysis), then used them as input variables for the clustering. The more strongly negative these values (i.e., factor scores), the more firms rate the respective factor as below a cluster average; positive values instead indicate a rating above the cluster average. To calculate the distances between the clusters, we used a Euclidean distance measure and aggregated clusters using Ward’s procedure. To reflect the true structure of the data set, we employed an elbow criterion that determined the number of clusters and found thresholds at four and six clusters. To identify the most appropriate solution, we performed an additional multiple discriminant analysis for both solutions; the hit rate (i.e., proportion of firms correctly classified) was highest for the four-cluster solution, according to the confusion matrices. In addition to the two variables measuring IT investments, we employed so-called secondary cluster variables, such as the number of employees and the need to apply new technologies, as well as efforts to gain a competitive advantage, to profile the clusters (see the Appendix for the scale items). With an analysis of variance, followed by a Scheffe´ (1953) test, we examined the inter-group differences according to these profile variables. Four clusters emerged from the analysis, and we label them according to their factor scores on the measures of IT investment in primary and secondary processes (see Table 5). The cluster names also capture key differences among the four clusters in terms of IT investments. Segment 1, the largest of the four clusters, represents firms we describe as IT convinced. Nineteen per cent of the firms in this cluster belong to the retailing, maintenance, or repair services sector. They score positively on both primary and secondary processes modules and are likely to engage in activities to pursue a competitive advantage in the marketplace. Of all clusters, this group perceives the greatest need to apply new technologies in the IT landscape. They are also the ones most likely to have gained competitive advantages with IT. Fifty-eight per cent of the firms in this cluster report sales growth of at least 2% per year in the past 3 years, consistent with the RBV’s position that technological competencies represent critical capabilities that drive firm success (Wade and Hulland, 2004). The firms in segment 2 are striving for differentiation through IT. They exhibit the highest score for the primary

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186 Table 5 Characterization of the firm clusters

Cluster 1: IT convinced (n ¼ 231)

Cluster 2: IT differentiators (n ¼ 165)

Cluster 3: IT sceptics (n ¼ 108)

Cluster 4: IT pragmatists (n ¼ 79)

Identifying Firm Clusters (primary cluster variables) Factor 1: Primary Processes Modules Factor 2: Secondary Processes Modules

0.4678 0.6763

0.6754 0.8402

1.0705 0.9538

1.3310 1.0457

Profiling Firm Clusters (secondary cluster variables) Mean number of full-time employees

84a*

89 a

66b

82a

(26.1%) (5.5%) (7.3%) (19.4%) (5.5%) (3.6%) (8.5%) (5.5%) (6.7%) (3.6%) (3%) (5.5%)

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43 9 12 32 9 6 14 9 11 6 5 9

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20 7 11 12 6 7 3 5 10 9 8 10

2.92a 3.17a 2.99a

(18.5%) (6.5%) (10.2%) (11.1%) (5.6%) (6.5%) (2.8%) (4.6%) (9.3%) (8.3%) (7.4%) (9.3%)

12 6 4 10 4 3 7 11 10 9 3

2.77b 2.78b 2.69b

(15.2%) (7.6%) (5.1%) (12.7%) (5.1%) (3.8%) — (8.9%) (13.9%) (12.7%) (11.4%) (3.8%) 2.69b 3.13a 2.57b

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no answer

(16.9%) (6.9%) (5.6%) (19%) (6.5%) (7.8%) (7.4%) (10%) (3.9%) (3%) (6.1%) (6.9%) 3.00a 3.30a 3.13a

Activities to gain competitive advantage Need to apply new technologies Competitive advantages with IT could be gained Sales (development over last 3 years) c 46% decrease per year 42% decrease per year Stayed stabile +/2% per year 42% to 6% increase per year 46% increase per year

39 16 13 44 15 18 17 23 9 7 14 16

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Sectoral classification Manufacturing Utilities (gas, electricity, water) Building/construction sector Retailing, maintenance, repair services Hotels, restaurants Transport and telecommunications Credit institutions and insurances (financial services) Real estate, rent and lease, data processing Public sector Education Healthcare Other public services

1 1 66 70 64

(0.4%) (0.4%) (28.6%) (30.3%) (27.7%)

29 (12.6%)

1 3 38 56 51

(0.6%) (1.8%) (23%) (33.9%) (30.9%)

16 (9.7%)

1 2 42 27 22

(0.9%) (1.9%) (38.9%) (25%) (20.4%)

14 (13%)

2 27 23 19

— (2.5%) (34.2%) (29.1%) (24.1%)

8 (10.1%)

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*If means within a row are significantly (Po0.05) different from each other, they have a different superscript (according to LSD test). The same superscript indicates that is no significant difference between two groups. c 2 w test was applied to this variable.

dependency analysis, which suggested a relationship between IT investments and competitive advantage. Firms that are more successful in terms of sales growth (i.e., segments 1 and 2) invest more in IT (especially in that in support of secondary processes) than do the less successful firms (segments 3 and 4). Therefore, firms with a past history of IT development appear more likely to achieve a competitive advantage. Discussion Management scholars have long understood the importance of firm resources in gaining a competitive advantage. Firm resources consist of all tangible and intangible assets possessed or controlled by firms that permit them to devise and apply value-enhancing strategies. A firm’s IT, coupled with human resource skills, represents just such an asset. As our overarching theme, we posit that IT can affect firm performance and be a source of competitive advantage.

Although investments in IT attract considerable research and managerial attention, we still know little about the utility that managers associate with IT investments for different functional areas of the firm or the relationships between those investments and firm success. To address this void, we have used survey data from Swiss SMEs and identify two company and IT areas (i.e., primary and secondary processes) in which firms invest, which we name primary processes modules and secondary processes modules. In agreement with the RBV, we demonstrate a positive relationship between firms’ investments in IT resources that support primary processes and (a) their financial performance in terms of sales growth and (b) firms’ assessment of their own activities to create competitive advantages. These findings are consistent with the RBV and with the notion that firm resources and capabilities, such as IT and its use, create discrepancies in both competitiveness and performance (Barney, 1991; Bharadwaj, 2000; Caldeira

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Conclusions and limitations Despite a consensus in management literature that IT, such as enterprise systems, can provide a source of competitive advantage, diametrically opposed opinions remain. In line with Porter and Millar (1985), one faction believes that firms invest in IT to improve their performance in different functional areas and that IT has a particular potential for efforts to achieve a competitive advantage (e.g., Weill and Broadbent, 1998; Porter, 2001). The other faction takes the position that the IT diffusion process has become so advanced that it already represents a commodity that is available to all, such that it must have lost its effectiveness as a strategic instrument of differentiation (Carr, 2003; Rettig, 2007). Yet even with these opposing viewpoints about IT as a source of competitive advantage (e.g., Strassmann, 1999; Dehning and Richardson, 2002; Hitt et al., 2002), relatively little empirical evidence is available to guide managers’ IT investment decisions. We attempt to shed some light on this issue by drawing on Porter’s (1985) value chain and the distinction between primary and secondary activities. We propose in particular that IT investments in primary activities affect firm performance. Although our findings lend support to the notion that IT can help a firm improve its primary processes, which translate into performance gains and competitive advantage, we do not make a claim for conclusive findings. Several limitations and research opportunities related to our research deserve mention. First, we have surveyed ‘typical’ Swiss SMEs from different business sectors. To generalize our findings to other countries and settings, more research is needed. Therefore, we call for replications of this study in different environmental (e.g., multinational firms) and cultural contexts, but we do so mindful of recent concerns about the types of research methods used for RBV research. For example, Rouse and Daellenbach (1999) argue that the origin of a firm’s competitive advantage often relates to intangible processes that are mired in causal ambiguity. Therefore, we also need studies that combine qualitative research methods that can isolate value-creating processes with quantitative approaches that can facilitate inter-firm comparisons. Furthermore, additional studies should attempt to confirm the presence of the IT convinced, IT differentiators, IT sceptics, and IT pragmatists and provide richer descriptions of what unites and separates them when it comes to their IT usage and marketplace positioning. Second, survey research using data from a single source at a particular point in time suffers limitations (e.g., Joshi, 2009). The use of a single data source for both dependent and independent variables creates the potential for artificially high correlations, and the use of cross-sectional data to test cause-and-effect relationships violates an important tenet for establishing causality, that is, that the cause must be empirically established to exist before the effect occurs. We have addressed the former concern by testing for common method bias. However, the important task of testing our research hypotheses with non-cross-sectional data remains for further research. Such research also could address the issue of reverse causality. As Hu and Quan (2005) argue, more empirical studies are needed before we can fully

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and Ward, 2003). We build on and extend extant research that explores and confirms the relationship between firms’ IT investments and their performance (e.g., McAfee and Brynjolfsson, 2008). Specifically, we build on two variables that measure firms’ IT investments in primary and secondary processes to develop a firm typology. Firm typologies offer greater clarification about how firms should invest in IT to support their management decisions – especially if those typologies cut across the boundaries of multiple business sectors (Hambrick, 1983). Four types of companies emerge from our data: (1) IT convinced, (2) IT differentiators, (3) IT sceptics, and (4) IT pragmatists. By identifying these four groups, we confirm that IT adoption and its potential contribution to firm performance, as well as competitive advantage development, remains complex and difficult to investigate. Some firms make IT investments in all functional areas; others offer only limited commitments to IT investments. Some managers learn to harness their IT capability for performance gains, but others seem less convinced of its ability to deliver a competitive advantage. This latter finding is consistent with previous research that highlights the dissatisfaction many business managers express with the value they perceive they have derived from IT system investments (Peppard et al., 2001). The findings related to our firm typology also pertain to the RBV. That is, to some extent, we can link the different strategic behaviours implied by our four clusters to firm performance and therefore to the RBV. Our findings suggest varying levels of performance depend on the way firms invest in IT. According to the RBV of the firm, we should expect that firms that invest in both primary and secondary processes, or at least in primary processes (which are inextricably linked to their value-enhancing capabilities), outperform firms that do not invest in IT. Our findings show that 65% of firms that invest in primary processes (i.e., segment 2, IT differentiators) report sales increases of at least 2%, and 58% of those that invested in both primary and secondary processes (i.e., segment 1, IT convinced) report similar sales increases. In contrast, firms that invest in either primary and secondary processes (i.e., segment 3, IT sceptics) or only in secondary processes (i.e., segment 4, IT pragmatists) achieve sales increases of only 45% and 53%, respectively. As we noted though, the relationship between IT investments and firm performance is less straightforward than it appears. Theory predicts that firms that invest in both primary and secondary processes should exhibit greater incremental sales than firms that invest in either one. Yet our findings show that IT differentiators in segment 2 achieve more incremental sales than the IT-convinced firms in segment 1. Nevertheless, in combination with our examination of causal relationships between IT investment and changes in sales, as well as between IT and competitive advantage, our results suggest that firms investing in IT (especially IT that supports primary processes) perform better and perceive a competitive advantage in their industry. This finding is consistent with previous RBV research that highlights a similar relationship (e.g., Feeny and Willcocks, 1998; Bharadwaj, 2000). However, our findings strongly contest Carr’s (2003) titular assertion that ‘IT doesn’t matter’.

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Barney, J. B. and A. M. Arikan, 2001, “The resource based view: Origins and implications”. In M. E. Hitt, R. E. Freeman and J. S. Harrison (eds.) The Blackwell handbook of strategic management. Oxford, UK: Wiley-Blackwell, pp: 124–188. Beaver, G. and C. Prince, 2004, “Management, strategy, and policy in the UK small business sector: A critical review”. Journal of Small Business and Enterprise Development, 11(1): 34–49. Bharadwaj, A. S., 2000, “A resource-based perspective on information technology capability and firm performance: An empirical investigation”. MIS Quarterly, 24(1): 169–196. Caldeira, M. M. and J. M. Ward, 2003, “Using resource-based theory to interpret the successful adoption and use of information systems and technology in manufacturing small and medium-sized enterprises”. European Journal of Information Systems, 12(2): 127–141. Carr, N. G., 2003, “IT doesn’t matter”. Harvard Business Review (May), 81(5): 41–49. Clemons, E. K., 1991, “Corporate strategies for information technology: A resource-based approach”. Computer, 24(11): 23–32. Clemons, E. K. and M. C. Row, 1991, “Sustaining IT advantage: The role of structural differences”. MIS Quarterly, 15(3): 275–292. Cohen, W. M. and D. A. Levinthal, 1990, “Absorptive capacity: A new perspective on learning and innovation”. Administrative Science Quarterly, 35(1): 128–152. Dandridge, T. C., 1979, “Children are not little grown-ups: Small business needs its own organizational theory”. Journal of Small Business Management, 17(2): 53–57. Davenport, T., 1998, “Putting the enterprise into the enterprise system”. Harvard Business Review, 76(4): 121–131. Day, G., 1994, “The capabilities of market-driven organizations”. Journal of Marketing, 58(4): 37–52. Dehning, B. and V. J. Richardson, 2002, “Returns on investments in information technology: A research synthesis”. Journal of Information Systems, 16(1): 7–30. Delmonte, A. J., 2003, “Information technology and the competitive strategy of firms”. Journal of Applied Management and Entrepreneurship, 8(1): 115–129. Deros, B. M., S. M. Yusof and A. M. Salleh, 2006, “A benchmarking implementation framework for automotive manufacturing SMEs”. Benchmarking: An International Journal, 13(4): 396–430. Dettling, W., U. Leimstoll and P. Schubert, 2004, Netreport’5: The use of business software in Swiss small and medium-sized enterprises, original title: ‘Netzreport0 5: Einsatz von Business Software in kleinen und mittleren Schweizer Unternehmen’. Basel: University of Applied Sciences Basel (FHBB), Working Report E-Business No. 15. Dibrell, C., P. S. Davis and J. B. Craig, 2009, “The performance implications of temporal orientation and information technology in organizationenvironment synergy”. Journal of Strategy and Management, 2(2): 145–162. Eurostat, 2008, European business facts and figures – 2007 Edition. Luxembourg: Office for Official Publications of the European Communities, pp: 28–31. Feeny, D. F. and B. Ives, 1990, “In search of sustainability: Reaping long-term advantage from investments in information technology”. Journal of Management Information Systems, 7(1): 27–46. Feeny, D. F. and L. P. Willcocks, 1998, “Core IS capabilities for exploiting information technology”. Sloan Management Review, 39(3): 9–21. Fornell, C. and D. G. Larcker, 1981, “Evaluating structural equation models with unobservable variables and measurement error”. Journal of Marketing Research, 18(1): 39–50. Grant, R. M., 2002, Contemporary strategic analysis, 4th edn. Malden, MA: Blackwell Publishers. Hair, J. F., R. E. Anderson, R. L. Tatham and W. C. Black, 1998, Multivariate data analysis, 5th edn. Englewood Cliffs, NJ: Prentice Hall. Hambrick, D. C., 1983, “An empirical typology of mature industrial-product environments”. Academy of Management Journal, 26(2): 213–230. Henderson, J. C. and N. Venkatraman, 1999, “Strategic alignment: Leveraging information technology for transforming organizations”. IBM Systems Journal, 38(2/3): 472–484. Hitt, L. M., D. J. Wu and X. Zhou, 2002, “Investment in enterprise resource planning: Business impact and productivity measures”. Journal of Management Information Systems, 19(1): 71–98.

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understand if higher IT investments contribute to better performance or better performance leads to higher IT investments. Third, we have relied on Porter’s (1985) value-chain logic, at the expense of considering other sources of value creation. Further research might incorporate Stabel and Fjeldstad’s (1998) value shop and value network logics to investigate possible value-creation processes that relate directly to customer-specific problem-solving processes. In addition, incorporating the dynamic capabilities perspectives (Teece et al., 1997) into research efforts could isolate particular firm-specific learning capabilities that have the potential to contribute to a competitive advantage. Furthermore, research could examine the mechanisms that underlie the link between IT investments and firmlevel performance. Firms vary in their ability to recognize the value of new technology and information, assimilate it, and use it to create a competitive advantage (Seaton and Cordey-Hayes, 1993). Cohen and Levinthal (1990) refer to such firm capabilities as absorptive capacity. Additional studies therefore should explore SMEs’ overall ability to take effective advantage of IT. Fourth, we measured the performance associated with IT investments as changes in sales; researchers also could measure sales changes using a continuous variable and thereby treat the change as a dependent variable in multivariate analyses. Also, changes in sales for the different segments might be viewed a suboptimal measure for competitive advantage because they might be temporary or subjective. To address this shortcoming, research should employ other competitive advantage measures. In conclusion, though the results of this research should be used with caution, we believe that it offers an important step with regard to adding insights to the field related to the development of an IT-specific competitive advantage in SME firms.

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1 In this study context, IT refers to the use of technology (e.g., computers, hardware, software, the Internet) to manage information. We focus on enterprise (software) systems, which Davenport (1998: 121) defines as commercial software packages that enable the seamless integration of all information flowing through a company, including financial and accounting, human resource, supply chain, and customer information. 2 The firm type variable distinguishes manufacturing from service firms, which likely vary in their IT investments. On the basis of the Swiss NOGA Code, we first assigned all firms in the sample assigned to one of 12 business sectors: manufacturing; utilities; building/construction; retailing; maintenance; repair services, hotels, restaurants; transport and telecommunications; financial services; real estate, rent and lease, data processing; public sector; education; healthcare; and other public services. The 12 business sectors then were collapsed into manufacturing (20%) and services (80%).

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Appendix See Table A1. Table A1 Items included in cluster profiles

Activities geared towards gaining competitive advantage (a ¼ 0.75) We contrast with our competitors because we are among the cheapest in the market We contrast with our competitors because of the uniqueness of our products We can persist in the market because we focus on the specific needs of market segments The quality of our products is key to gaining competitive advantages The quality of our added services is key to gaining competitive advantages Having a cross-company coordination of order processing is a deciding factor for maintaining a competitive edge Our customers view us as an innovative company that tends to be the first to introduce new products into the market