it alignment and firm performance in small manufacturing firms

IT alignment and firm performance in small

manufacturing firms

Paul Cragga, Malcolm Kingb,*, Husnayati Hussinc

aUniversity of Canterbury, Christchurch, New ZealandbBusiness School, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK

cInternational Islamic University, Kuala Lumpur, Malaysia

Received 8 January 2001; revised paper accepted 1 May 2002

Abstract

The concept of IT alignment has been discussed in the literature, but almost always in the context

of large firms. Similarly, attempts to measure alignment and relate it to performance have been

made, but primarily based on work in larger firms. This study focused on measuring the alignment of

business strategy and IT strategy (ITS) among small UK manufacturing firms and then investigated

the link between alignment and performance. The method built on prior studies, developing

approaches to alignment used with larger firms and integrating other concepts and measures from the

small firm literature. Using a mail questionnaire, data from 250 firms was collected on nine strategy

areas so that business and ITS responses could be compared. IT alignment was explored using both

the matching and moderation approaches. The moderation approach appeared more effective in

identifying IT alignment. The results indicated that a significant proportion of small firms had

achieved high IT alignment. Furthermore, the group of small firms with high IT alignment had

achieved better organisational performance than firms with low IT alignment. This is consistent with

findings in large firms and opens up possibilities for further study of IT alignment in small

firms. q 2002 Elsevier Science B.V. All rights reserved.

Keywords: Alignment; Small firms; Strategy; Matching; Moderation; IT strategy; Survey research

1. Introduction

This study is concerned with extending the concept of the alignment between business

strategy and IT strategy in a way that is appropriate for small firms and investigating ways

0963-8687/02/$ - see front matter q 2002 Elsevier Science B.V. All rights reserved.

PII: S0 96 3 -8 68 7 (0 2) 00 0 07 -0

Journal of Strategic Information Systems 11 (2002) 109–132

www.elsevier.com/locate/jsis

* Corresponding author. Tel.: þ44-1509-223119; fax: þ44-1509-223960.

E-mail address: [email protected] (M. King).

http://www.elsevier.com/locate/jsis

of measuring such alignment so that it can be related to the performance of small firms.

Alignment between business strategy and IT strategy has been given significant attention

in recent year and has been ranked among the top 10 issues facing IT executives

(Brancheau et al., 1996). Most authors suggest that IT alignment has advantages for firms.

However, in practice, many firms struggle to achieve alignment (Baets, 1992; Reich and

Benbasat, 1996).

Researchers have been keen to study IT alignment as it may help explain the

relationship between the use of information systems in organisations and firm

performance. For example, Chan et al. (1997) found a positive relationship between IT

alignment and firm performance. However, their work was based on large firms and may

not apply to small firms, as small firms typically have few, if any, staff dedicated to IT

(Palvia et al., 1994). Furthermore, Chan et al. (1997) explored different ways of measuring

IT alignment, and it is not obvious that their conclusions will carry over exactly into the

small firm setting.

Although IT has been used significantly in many small firms, there have been few

reports of IT alignment in small firms. Thus, this study aimed to examine IT alignment in

small firms, and in particular explore the relationship between IT alignment and

organisational success. Appropriate aspects of the work of Chan et al. (1997) were used,

including alternative measures of alignment, but adapted for small firms, based on prior

research.

The chosen approach was a cross-sectional study using a mail questionnaire followed

by statistical analysis. In Section 2, prior research is reviewed to justify the need for the

study and introduce the key concepts. Following that, the paper discusses the research

framework and research design, including the research instrument. The results are

presented through a series of tables, with the wider implications and significance of the

results explored in Section 6.

2. Prior research

Prior research has examined the link between business strategy, IT, and firm

performance. Some have found a positive relationship between IT use and organisational

performance. For example, Dvir et al. (1993) found links between IT use and performance

across the typology of Miles and Snow (1978). The link to short and long term success was

most positive for defenders, while analysers gained in the short term and prospectors in the

long term. Weill (1990) also found that investment in strategic IT, rather than operational

IT, was a risky strategy but with potential for high payoff in the long term for early IT

adopters. Furthermore, Amstrong and Overton (1982) provided evidence of a positive

impact of IT on enterprise-level performance, based on a survey of small firms.

Other studies have failed to indicate a relationship between IT spending and business

profitability (Hitt and Brynjolfsson, 1996). This indicates that firm performance is likely to

be determined by the interaction of various factors. Chan et al. (1997) argued that the

impact of IT on performance may not be a direct one, but intermediated by other factors,

such as the alignment between business strategy and IT strategy. Chan et al.’s (1997)

survey of large firms found a positive relationship between IT alignment and

P. Cragg et al. / Journal of Strategic Information Systems 11 (2002) 109–132110

organisational performance, based on Venkatraman’s (1989b) nine dimensions of business

strategy.

This concept of ‘alignment’ or ‘fit’ expresses an idea that the object of design, e.g. an

organisation’s structure or its information systems, must match its context in order to be

effective (Iivari, 1992). Parsons (1983) was one of the first to argue that IT can affect a

firm’s ability to execute their business strategy. Since then, many others have emphasised

the need to develop a fit between information technology strategies and business strategies

(Henderson and Venkatraman, 1989; Galliers, 1991; Henderson and Venkatraman, 1993;

Chan et al., 1997).

The above research relates to IT alignment in large firms. IT alignment in small firms

has yet to receive much attention, although there is some evidence of its existence. For

example, Levy et al. (1998) identified ‘innovation’ firms, where “IS are an integral and

tightly woven part of the business strategy” (p. 6). They also provided evidence of a lack of

IT alignment in their ‘efficiency’ firms, where “there is no recognition of the role of

information in supporting the achievement of business strategy” (p. 5).

Southern and Tilley (2000) identified three types of firm based on IT use, and suggested

that small firms with more sophisticated IT probably had more sophisticated IT

management practices. For example, in ‘high user’ firms, “not only will IT be planned on a

much more formal basis, but ICTs will have become a formal responsibility, probably

delegated to a dedicated person, such as the IT manager” (p. 15). They provided both

evidence that IT management practices vary in sophistication across firms.

Some researchers have examined how small firms can align their IT strategy with their

business strategy. In particular, Blili and Raymond (1993) argued that small firms must

adopt some kind of framework for planning IT if they wish to create IT based strategic

advantage. Subsequently, Levy and Powell (2000) proposed an approach to IS strategy

(ISS) development aimed specifically at small firms, to help them align their IT and

business strategies. They report encouraging results, but have yet to report an evaluation of

the effectiveness of their ISS development approach.

Others studies have provided evidence of IT being used strategically by small firms. For

example, Poon (2000) reports on firms that have gained competitive advantage from use of

the Internet. Furthermore, Naylor and Williams (1994) concluded that small firms are

more successful with IT than is generally believed. Thus there is evidence of the strategic

use of IT in small firms. This implies a degree of alignment with business strategy, but as

yet this IT alignment has not been studied. We also know little about how small firm IT

alignment can be achieved.

Many studies have indicated that small firms do not have the resources to use IT

strategically. For example, managers in small firms are few in number, and have limited

time and IT expertise, which limits their ability to devise IT strategy (Mehrtens et al.,

2001). Also, Hagmann and McCahon (1993) claim that small firms tend not to develop

information systems strategies. Consequently this results in a lack of appropriate policies

towards IT assessment and adoption. Furthermore, Palvia et al. (1994) argued that the

computing environment in very small firms (with 50 or less employees) was

fundamentally different from medium-sized firms, where there was often a formal MIS

department and a community of end-users.

The above literature indicated that IT alignment could be important in understanding

P. Cragg et al. / Journal of Strategic Information Systems 11 (2002) 109–132 111

the link between IT use and performance in firms. However, there is little known about IT

alignment in small firms, including any link with performance.

3. Research framework

This study aimed to focus on the relationship between alignment and organisational

performance, based on the argument that strategic fit has performance implications.

Generally, the better the fit, the better the performance (Fry and Killing, 1989). More

specifically, the study wished to focus on one aspect of IT alignment, i.e. the alignment

between business strategy and IT strategy (Henderson and Venkatraman, 1989). Luftman

et al. (1993) emphasised that for companies to succeed in an increasingly competitive,

information-intense, dynamic environment, then the alignment of business strategy and IT

strategy was a necessity. The above studies were conducted in the context of large

organisations. This study hypothesised a similar relationship between IT alignment and

performance for small firms. Thus the study’s major proposition was: “Small firms that

align their IT strategy with their business strategy are more likely to be successful than

those that do not align their IT strategy with business strategy”.

It should be noted that this proposition only links IT alignment with organisational

performance. The proposition does not suggest a direct link between business strategy and

performance. Similarly, the proposition does not suggest a direct link between IT strategy

and performance. Instead, any link between strategy and performance is via IT alignment.

For business strategy or IT strategy to directly influence the performance would imply that

some specific strategy must be superior. If this were true then all firms in the same

industrial sector/market would adopt the same strategies. Instead, the impact on

performance of both business strategy and IT strategy comes from their alignment.

The proposition was concerned with total alignment and implied that firms that were

highly IT aligned would perform better than less well aligned firms, regardless of business

strategy and regardless of IT strategy. The study examined two approaches to measuring

alignment, matching and moderation, in an attempt to help advance the debate on methods

of examining alignment.

4. Research design

4.1. The research instrument

The research proposition referred to links between the following variables: business

strategy, IT strategy, IT alignment, and organisational performance, each of which were

operationalised on the research instrument as follows.

4.1.1. Business strategy

The role of business strategy has received attention among small business researchers.

For example, Storey (1994) identified strategy as one of the three main components that

contribute towards growth among small firms. The study of business strategy in small


firms has drawn on typologies based on large firms, for example Ansoff (1965) and Porter

(1980). Importantly, these studies of small firms indicate some support for typologies

based on larger firms. For example, Ansoff (1965) proposed a matrix of strategies based on

opportunities for new products and new markets. Barkham et al. (1996) examined the role

of business strategy in small firm growth and found support for all four of Ansoff’s

strategies. Hewitt-Dundas and Roper (1999) also found support for Ansoff’s market

extension and product development strategies.

Other studies lend support to Porter’s generic strategies applying to small firms. For

example, Storey observed that rapidly growing small firms often occupied niche markets.

Reid (1993) found that the focus strategy emphasised by SMEs was based more on

differentiation than cost leadership. This supports Namiki’s (1988) conclusions of small

firm’s competing through innovation, customer service, and product quality. Furthermore,

Julien et al. (1997) found that exporters competed on price, technical superiority, product

quality, and customer service.

Other studies provide evidence that small firms have to adopt numerous strategies. Hall

(1992) examined a group of 30 successful firms. Amongst his six hallmarks for success

were: focus/direction, customerising, and quality. Also, Gunasekaran et al. (1996)

identified productivity and quality improvement strategies for SMEs in the manufacturing

sector based on cost control, improving quality, new products, lower price, fast delivery,

increase market share, and business renewal through new products and designs.

However, these studies of small firm strategy have failed to provide a consensus model

of strategy for small firms. Different studies have produced different typologies and not all

types are necessarily present in all industries (Kim and Choi, 1994). This lack of consensus

could, in part, be due to the focus on implicit rather than explicit strategy by small firms,

which makes strategy in small firms more difficult to study. Lefebvre et al. (1992)

concluded that small firms were not as strategically oriented as larger organisations, which

makes the explicit identification of strategy more difficult. The approach to strategy

formation in small firms has been described as informal, inexplicit, intuitive, and

incremental (Mintzberg, 1988). In addition, Chell et al. (1992) recognised the lack of

strategic awareness in many small firms. “For small companies, implicit strategies are the

norm” (p. 5).

In the absence of an existing instrument to measure business strategy in small firms, the

above studies of small firm strategy were used to develop a measure for this study. Key

factors that contributed towards small firm competitiveness were extracted from these

studies. A list of business strategy items was then discussed with managers of several

small firms during pre-testing of the instrument. They were invited to suggest additions or

deletions or modifications. Only minor modifications were suggested and so the nine

business strategy items were used, as summarised in Table 1. The nine items used in the

questionnaire are shown in Appendix A.

4.1.2. IT strategy

While many researchers argue that most small businesses are informal and lack IT

planning, it is believed that ‘emergent’ and unformalised IT strategies do exist. As there

has been little research into IT strategy in small firms, this study adopted a similar

approach to Chan et al. (1997), where the instrument was developed to mirror the same


items as were used to measure business strategy. Thus the instrument was devised to assess

the level of IT support provided for business strategy, with an emphasis on realised IT

strategy. This study measured IT strategy using nine items, each similar to those that

measured business strategy. For example, for the business strategy item ‘we attempt to be

ahead of our competitors in introducing new products’, the IT strategy item was ‘our

current systems enable us to introduce new products earlier than our competitors’. The

nine items used in the questionnaire are shown in Appendix A.

4.1.3. IT alignment

The concept of fit has been debated in the literature. Venkatraman (1989a) provided a

classification for the concept of fit, in which he distinguished six interpretations of fit.

Different approaches require different mathematical models and have different theoretical

implications (Schoonhoven, 1981; Venkatraman, 1989a). The matching and moderation

perspectives have been used by a number of researchers, and other perspectives are still in

their exploratory stages and require further development. Van de Ven and Drazin (1985)

concluded: “Studies should be designed to permit comparative evaluation of as many

forms of fit as possible” (p. 358). This indicates that studies should examine different

approaches, as adopted by Chan et al. (1997). They used a combination of approaches,

with their results supporting the moderation model rather than the matching model, and the

systems approach rather than the bivariate approach. Hoffman et al. (1992) also favoured

the moderation model following their study of the effect of the organisation structure–

technology fit on performance. They claimed that the moderation model was less

ambiguous and more widely applicable, compared with matching.

For this study, IT alignment was viewed as the fit between business strategy and IT

strategy, similar to Chan et al. (1997). Two approaches were modelled—fit as ‘matching’

and fit as ‘moderation’ which both rely on the close correspondence between the nine IT

strategy items and the nine business strategy items. Fit as matching was based on the

Table 1

The nine business strategy items

Business strategy items Sources of literature

Pricing strategy Barkham et al. (1996), Julien et al. (1997) and Reid et al. (1993)

Quality product strategy Gunasekaran et al. (1996), Hall (1992), Julien

et al. (1997) and Namiki (1988)

Product differentiation strategy Barkham et al. (1996), Julien et al. (1997) and Reid et al. (1993)

Product diversification strategy Barkham et al. (1996) and Gunasekaran et al. (1996)

New product strategy Barkham et al. (1996), Hewitt and Roper

(1999) and Namiki (1988)

New market strategy Barkham et al. (1996), Hewitt and Roper

(1999) and Namiki (1988)

Quality service strategy Barkham et al. (1996), Gunasekaran et al. (1996), Hall (1992), Julien

et al. (1997), Reid et al. (1993) and Namiki (1988)

Intensive marketing strategy Gunasekaran et al. (1996) and Julien et al. (1997)

Production efficiency strategy Barkham et al. (1996), Gunasekaran et al. (1996) and Hewitt and

Roper (1999)


difference between each of two pairs of related items. For each of the nine strategies, the

absolute difference between the business strategy score and the corresponding IT strategy

score was formed. Fit as moderation was modelled as the interaction between each

business strategy and the related IT strategy. Thus, for each of the nine strategies, the

product of the business strategy score and the corresponding IT strategy score was formed.

However, the study examined alignment from an overall perspective rather than an

individual/specific strategy perspective. Thus the study measured total alignment, rather

than splitting alignment into various parts, e.g. alignment with pricing strategy. As a result,

the nine values were totalled to give an overall value for both the matching and the

moderation approaches.

4.1.4. Organisational performance (including IT impacts)

Organisational performance was the dependent variable in this study. Researchers have

offered a variety of measures of organisational performance. Subjective measures were

used rather than objective measures as subjective measures have been shown to capture a

broad concept like business performance (Khandwalla, 1977). The study adopted the

instrument developed by Khandwalla (1977), based on the manager’s assessment of the

company’s performance relative to its competitors. Thus four items were used to measure

long term profitability, availability of financial resources, sales growth, and image and

client loyalty. Each was measured using a five point scale, as shown in Appendix A.

Khandwalla found that these measures correlated fairly strongly with objective

performance measures and they have since been validated in the small business context

by Miller (1987) and Raymond et al. (1995).

Second, as a complementary approach, the study used an instrument to measure IT

impact, based on Thong et al. (1996). Six items were used and all were measured on a five

point scale and summed to provide a score for IT impact, as shown in Appendix A. At the

operational level, two items measured perceptions of time and cost savings. At the

management control level, two items measured perceptions of integration and quality of

decisions. At the strategic level, two items measured image and competitive advantage.

4.2. The research method

A mail questionnaire survey was used to gather data as the main aim of the study was to

compare IT alignment across a range of small firms. Extracts from the questionnaire are

shown in Appendix A. The manufacturing sector was selected as they can provide a range

of levels of IT sophistication (Cragg and King, 1993). The Dun and Bradstreet database

provided a total of 2272 addresses. Fifty addresses were used for the pilot survey, and 1400

were used for the main survey covering the UK.

As suggested by Dillman (1978), the questionnaire was refined in three stages: pre-

testing with academics and research students, pre-testing with small business managers,

and pilot testing with small business managers. The questionnaires were addressed to the

Managing Directors. Personalised cover letters and addresses were used, as well as a pre-

printed ‘freepost’ return envelope (Dillman, 1978). 256 usable questionnaires were

returned. This was an 18% response rate, similar to the Cambridge SBRC in 1992.

Non-response was examined using time trend extrapolation (Amstrong and Overton,


1982). The first 30 and last 30 responses were compared on 22 major variables. Only two

variables proved significantly different: ‘intensive marketing’ strategy and ‘IT support for

intensive marketing strategy’. For the reasons explained by Amstrong and Overton, this

suggested that non-response was not a significant factor that could affect the conclusions

about the variables being studied.

5. Results

Preliminary analysis of the sample showed that nearly 70% of the firms were

independent companies, with 30% as a subsidiary of another organisation. Mann–

Whitney tests on 22 questionnaire items found no significant differences between these

two groups of firms. Thus it was concluded that both types of company could be treated as

one sample for further analysis. Slightly more than half of the firms were engineering-

based while others consider themselves as non-engineering-based companies. Again,

Mann–Whitney tests identified only two variables that differed across these types of firm,

which suggested that the engineering and non-engineering firms were similar and could be

treated as one sample.

Eighty three percent of the sample were more than 10 years old, with 37% founded

Table 2

Mean ratings for the nine business strategy items

Business strategy Mean rating Standard deviation

Quality service 4.71 0.59

Quality products 4.41 0.74

Production efficiency 4.41 0.80

New market 3.74 1.05

New products 3.69 1.12

Product diversification 3.70 1.10

Product differentiation 3.61 1.03

Intensive marketing 3.14 1.04

Pricing/cost reduction 2.67 1.16

Table 3

Mean ratings for the nine IT strategy items

IT support for Mean Standard deviation

Quality service 3.84 0.99

Pricing/cost reduction 3.81 0.99

Production efficiency 3.78 1.00

Quality product 3.35 1.13

New product 2.66 1.09

Product diversification 2.57 0.99

Product differentiation 2.54 1.12

Intensive marketing 2.48 1.08

New market 2.14 1.05


between 11 and 20 years ago. Thus most of the firms were mature companies. Fifty eight

precent of firms had between 50 and 100 full-time employees. Eleven percent had fewer

than 50 employees. Most firms had annual sales of under 5 million pounds, but 46% were

over 5 million pounds. About a third of the firms had used computers for between 5 and 10

years, and another third for 11–15 years. Thus the sample contained many firms with

considerable experience with computers, indicating the sample was appropriate for the

substantive analysis proposed.

Following the preliminary analysis of the sample characteristics, a factor analysis was

conducted for all 18 items that measured business strategy and IT strategy to test the

reliability of the construct. Such a test is desirable to check that respondents were

distinguishing between business strategy and IT strategy despite the similarity of the item

questions. The analysis was conducted in SPSS using principal component analysis and

varimax rotation with Kaiser normalisation (Hair et al., 1995) and an extract from the

output is shown in Appendix B. This analysis indicated that the two sets of items measured

different things. The two factor solution had all the IT strategy items loaded on one factor,

with factor loadings ranging from 0.48 to 0.74. No business strategy item loaded higher

than 0.24 on this factor. The other factor had seven business strategy items with loadings

greater than 0.5, one at 0.45, and one at 20.20. The highest factor loading for any of the IT

strategy items on this second factor was 0.20. These different loadings indicated that the

two sets of items measured different things. Thus nine items were retained as reflecting IT

strategy and the other set of nine as reflecting business strategy. The one business strategy

item (cheaper pricing) that loaded negatively was retained as a business strategy item,

since it has strong support in the literature and was expected to correlate negatively with

quality strategies.

5.1. Business strategy

The descriptive statistics for the nine items used to measure business strategy are

reported in Table 2. Responses for each item took the full range of values from 1 to 5.

The highest rated items of business strategy were ‘quality service’ and ‘quality

products’. ‘Pricing/cost reduction’ and ‘intensive marketing’ were ranked lowest.

5.2. IT strategy

While 68% of the sample had a written business plan, only about a quarter (26%) had

formalised their IT strategy. The nine IT strategy items and their mean values are shown in

Table 3. Responses for each item took the full range of values from 1 to 5.

5.3. Business strategy and IT strategy

Since business strategy was measured using nine items and nine matching items were

used to measure IT strategy, it was possible to explore how important a specific strategy

was to a firm and how well this strategy was supported by IT. This section reports results

for each strategy. To simplify the data, the responses of ‘strongly agree’ and ‘agree’ were

combined into one category called ‘agree’. Similarly, ‘disagree’ and ‘strongly disagree’


were combined into one category called ‘disagree’. The ‘neutral’ category was left

unchanged. The results are presented in Table 4. For example, for the results referring to

‘production efficiency’, 161 respondents fell into the bottom right hand cell, referred to as

the agree–agree category (or combination) as it represents those respondents who agreed

Table 4

Business strategy and IT strategy

Business strategy Supported by IT strategy

Disagree Neutral Agree

Production efficiency

Disagree 3 2 3

Neutral 6 6 7

Agree 16 44 161

Product differentiation

Disagree 25 4 2

Neutral 37 30 9

Agree 53 50 35

Pricing/cost reduction

Disagree 10 25 72

Neutral 7 17 56

Agree 7 15 39

Intensive marketing

Disagree 49 11 2

Neutral 46 44 7

Agree 25 28 34

Quality service

Disagree 1 0 1

Neutral 3 1 2

Agree 18 56 165

Product diversification

Disagree 18 14 1

Neutral 25 28 6

Agree 55 68 26

Quality product

Disagree 3 0 3

Neutral 4 5 2

Agree 47 44 114

New product

Disagree 24 6 2

Neutral 24 29 6

Agree 46 60 46

New market

Disagree 30 5 2

Neutral 28 12 1

Agree 91 58 20


or strongly agreed both with the business strategy and the supporting IT strategy question.

This means that 161 respondents rated this business strategy as important for their firm and

considered it to be well supported by the firm’s IT. Bold and italic font is used within Table 4 to

make it easy to identify large occurrences. The italics in Table 4 represents occurrences of

more than 100, while the bold represents occurrences of 50 and above but less than 100.

Table 4 indicates similar patterns for the three items of production efficiency, quality

service and quality product, i.e. a large number of occurrences for the category agree–

agree. In other words, many companies had adopted these business strategies and at the

same time they perceived that their existing IT supported these strategies. A different

pattern of distribution is observed for the strategies of product differentiation, new market

and product diversification. For these strategies, bold & italic is observed for agree–

disagree and agree–neutral combinations, which implies that while quite a number of

companies adopted these business strategies, they did not perceive that IT was supporting

these strategies or were neutral about it. The new product strategy falls somewhere

between these two groups and the pricing/cost reduction strategy figures indicate few

small firms rely on a low pricing strategy but perceive that their IT supports cost reduction.

The intensive marketing strategy figures show another different pattern, with high scores

in the diagonal boxes. Thus the data in Table 4 indicates varying degrees of alignment

between business strategy and IT strategy for different strategy areas.

5.4. IT alignment using the matching approach

The previous discussion explored IT alignment without employing a specific analytical

scheme of measuring alignment. This section reports results using the matching approach

for measuring IT alignment based on deviation scores (Venkatraman, 1989a). This

approach was taken despite the initial evidence that the matching approach was less

appropriate. The matching approach was retained because the literature encourages

researchers to test alternative perspectives. The deviation scores for a specific strategy area

were computed as the absolute difference between the rating for that business strategy, BS

and the rating for the related IT strategy, ITS. A low value for the difference indicates that

the alignment between the two variables is high, while a high value for the difference

implies that there is a high degree of misalignment. For each company and each strategy

Table 5

IT alignment for each strategy (matching)

Business strategy/IT strategy Mean (absolute difference) Standard deviation N

Production efficiency 0.8 0.89 248

Intensive marketing 0.9 0.94 246

Quality service 1.0 0.93 247

New product 1.2 1.04 243

Product differentiation 1.3 1.08 245

Quality product 1.3 1.09 247

Product diversification 1.4 1.14 241

Pricing/cost reduction 1.6 1.14 248

New market 1.7 1.14 247


area, the absolute difference between the ratings of the business strategy and IT strategy

items was calculated. The mean difference for each strategy area was calculated by

summing the absolute difference for all companies, divided by the number of companies.

The results for all the nine strategy areas are shown in Table 5.

Since a low mean indicates high alignment, production efficiency, intensive marketing

and quality service strategies were most aligned, whereas the two greatest ‘mis-match’

scores were observed for the new market and pricing/cost reduction strategies.

5.5. IT alignment based on the moderation approach

An alternative perspective of fit is to consider fit as moderation (Venkatraman, 1989a), as

measured by multiplying the ratings for BS and ITS items (Chan et al. 1997; Raymond et al.

1995). In this case, a high rating for BS and a high rating for ITS will result in a high alignment

measure. On the other hand, a low rating for BS and a low rating for ITS will give a low

alignment score. For each company and each strategy area, the rating for BS was multiplied by

the rating for ITS. The mean for each strategy area was calculated by summing up the result of

the multiplication for BS and ITS variables for all companies and dividing by the total number

of companies. The mean product for each strategy area is shown in Table 6. The data shows

that quality service and production efficiency had high alignment scores, while intensive

marketing and new market strategies received low alignment scores.

The moderation and matching data provided inconsistent results for the intensive

marketing strategy. The matching analysis indicated high alignment for this strategy whereas

moderation gave low alignment. The data in Table 4 helps explain this inconsistency. For

intensive marketing, Table 4 showed 49 firms with the low–low combination of ‘disagree/

disagree’. Under the matching approach, this combination was considered to have a high

degree of alignment (the same as agree/agree). Under the moderation approach, the low–low

combination implied a low degree of alignment, as the strategy was considered to be of low

importance. Thus, for the strategy of ‘intensive marketing’, the moderation approach seemed

to provide an appropriate indication of the level of alignment. This indicated that the matching

approach could provide misleading data.

5.6. Total IT alignment and organisational performance

Total IT alignment scores were computed for each firm by summing the relevant alignment

scores for all nine items, for both the matching and the moderation approaches. However, it

has to be remembered that these two approaches work in opposite directions. Theoretically, a

totally aligned firm on the matching approach would score zero on each item and so zero for

total alignment (matching). A completely unaligned company could have a maximum

deviation score of 5 for each strategy area, leading to a maximum total score of 45. On the

other hand, a totally aligned firm on the moderation approach would score 25 on each item and

so 225 for total alignment (moderation) while a completely unaligned company could have a

minimum of 1 for each individual score, leading to a minimum total score of 9.

For the matching approach, the scores for Total Alignment (matching) ranged from 1 to

32, with a mean of 11.0 and standard deviation of 5.0. For the moderation approach, the

scores for Total Alignment (moderation) ranged from 20 to 206, with a mean of 106.1 and


a standard deviation of 30.0. This high variability indicated that a wide range of different

situations were covered in the sample. It also showed that respondents had used the full

range of possible responses on the questionnaire.

Four measures of organisational performance were employed in this research as

well as six items for IT impact. Their descriptive statistics are shown in Table 7.

Table 7

Descriptive statistics for the measures of organisational performance

Mean Standard deviation

Long term profitability 3.79 0.75

Sales growth 3.75 0.71

Financial resources 3.68 0.88

Image and client loyalty 3.92 0.80

Reduce cost 3.70 1.03

Improve image 3.46 1.02

Time saving 4.22 0.76

Quality of decisions 3.60 1.02

Better internal integration 3.62 0.97

Competitive advantage 3.55 0.98

Total IT impact 22.16 4.23

Table 6

IT alignment by strategy (moderation)

Business strategy/IT strategy variables Mean (BSpITS) Standard deviation N

Quality service 18.21 5.56 247

Production efficiency 16.98 5.97 248

Quality product 14.88 5.76 247

New product 10.27 5.66 243

Pricing/cost reduction 10.11 5.18 248

Product diversification 9.59 4.94 241

Product differentiation 9.53 5.46 245

Intensive marketing 8.29 5.30 246

New market 8.21 5.01 247

Table 8

Correlation coefficients for IT alignment with organisational performance

Profit Sales Resources Image IT impact Matching

Long term profitability 1

Sales growth 0.368 1

Financial resources 0.454 0.168 1

Image and client loyalty 0.202 0.294 0.253 1

Total IT impact 0.171 0.025 0.167 0.147 1

Total IT alignment (matching) 0.017 20.022 20.035 20.071 20.131 1

Total IT alignment (moderation) 0.128 0.189 0.173 0.175 0.373 20.333


Responses for each question item took the full range of values from 1 to 5 and,

interestingly, total impact took the full theoretically possible range of values from 6

to 30.

Inter-correlations between the organisational performance, total IT impact and total IT

alignment variables are shown in Table 8. All correlations were statistically significant for

the moderation approach, and none were significant for the matching approach except for

IT impact. However, the difference in signs was expected because the total alignment

scores work in opposite directions.

5.7. IT alignment and performance

ANOVA was used to test the relationship between IT alignment and each measure of

organisational performance. For this analysis, the sample of 235 firms with complete data

was split into three nearly equal groups of firms based on the total IT alignment

(moderation) scores for each firm. For ease of reference, these three groups of firms were

referred to as ‘high’, ‘medium’ and ‘low’ alignment. The ANOVA compared the mean

performance scores for the three groups. The means and F values are reported in Table 9.

The F values reported in Table 9 were all significant. This indicates that the three

alignment groups were significantly different for all four measures of performance as well

as for IT impact. Performance was consistently highest in the highly aligned group of

firms. This data provides support for the study’s major hypothesis that IT alignment

influences organisational performance. However, a causal link cannot be proved by this

data. The above results refer only to alignment based on moderation. A similar analysis

was conducted based on matching, and as expected following the insignificant correlations

in Table 8, none of the one-way ANOVA results were significant.

6. Discussion

This research focused on IT alignment in small firms. Studies in large firms have

indicated that practitioners struggle to achieve IT alignment (Chan et al. 1997; Luftman

1996). In the small firm sector, there are claims that small firms tend not to develop

information systems strategies (Hagmann and McCahon 1993). Furthermore, Brouthers

et al. (1998) claimed that small firms “tend not to be rational in a number of key strategic

activities” (p. 136). Thus an important finding from this study was that many small

manufacturers had achieved a high degree of alignment between their BS and IT.

However, we do not know how this alignment was achieved. It may or may not have been

planned using systematic frameworks, as argued by Blili and Raymond (1993). A quarter

of the sample (26%) had formalised their ITS, while 68% of the sample had a written

business plan. This supports the notion that IT planning does exist in small firms, but much

of it is carried out informally (Lefebvre and Lefebvre, 1988).

This study is the first to identify a positive association between IT alignment and small

firm organisational performance. While causal links cannot be deduced from this research,

the results indicate that IT alignment has a positive relationship with organisational

performance. This finding is consistent with studies of larger organisations (Chan et al.,


Table 9

One-way ANOVA between IT alignment groups and performance (moderation)

Performance Low alignment (77) Medium alignment (78) High alignment (80) F ratio F prob. Significance

Long term profitability 3.57 3.78 3.90 3.947 0.021 Significant

Sales growth 3.62 3.68 3.96 5.717 0.004 Significant

Financial resources 3.45 3.64 3.89 5.030 0.007 Significant

Image and client loyalty 3.77 3.77 4.16 6.987 0.001 Significant

Total IT impact 19.66 22.23 24.28 31.590 0.000 Significant

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1997; Burn, 1996). The findings suggest that the positive relationship applies to small

firms, not just large firms. The study is one of few to provide evidence that some IT

investments could impact on small firm performance. The study also indicates that IT

alignment could be important in understanding the relationship between IT and firm

performance. However, further study of IT alignment is required so that we can better

understand this relationship, and also understand best practices that help achieve

alignment.

The study also found that IT impact was related to alignment. Companies with a high

degree of IT alignment perceived that they were receiving greater IT impacts compared

with companies that have a lower degree of IT alignment. This agrees with Chan et al.

(1997) that IT strategic alignment was consistently related to various dimensions of IT

effectiveness, including organisational IT impacts.

Although the study focused on ‘total alignment’, it also provided new evidence of

elements that contribute to alignment. For example, the study showed that some business

strategies were more important than others for these small manufacturers. In particular,

quality service, quality products and production efficiency were rated highly. These ratings

were similar to prior studies of small firms (Cambridge SBRC, 1992; Pratten, 1991).

Importantly, this study provided new evidence that IT was aligned well with these three

business strategies. However, the fourth highest rated BS of ‘new market’ had the weakest

alignment score for both matching and moderation. This indicates that identifying new

markets was important for these firms, but IT was not being used to support this strategy.

This seems to present a significant opportunity for the strategic use of IT in small

manufacturing firms. Small firms will need to adopt some new business practices, possibly

learning from practices in large firms.

The alignment data also presented an optimistic rather than a pessimistic view of IT in

small firms, as argued by Naylor and Williams (1994). As well as some firms showing high

total alignment, the data indicated high alignment for three business strategies. This new

evidence provided unexpected insights into small firm computing. For example, using IT

to support quality service, quality products and production efficiency suggest rather

sophisticated uses of IT, i.e. much more than just using an accounting package with a focus

on administrative efficiency. Thus many small firms are using IT well.

6.1. Implications for research

Prior literature has encouraged researchers to examine alignment using many

perspectives of fit. It has also highlighted differences between the matching

perspective and the moderation perspective. This study provided evidence of

inconsistent results from the two measurement approaches. The matching

perspective indicated high alignment for the intensive marketing strategy, while

the moderation perspective rated its alignment as weak. Other data indicated weak

alignment for intensive marketing, thus the moderation approach seemed to

outperform the matching approach. This is consistent with Chan et al. (1997) and

suggests that researchers should at least be wary when using the matching approach.

The study provided another reason for researchers to use the moderation perspective

as the data showed that some business strategies were more important than others.


The moderation perspective supports such differences as moderation gives more

weight to strategies of high importance. Alternatively, researchers could also choose

to focus on how IT is used to support specific strategies, e.g. service and product

quality.

It has already been noted that the positive association between alignment and

organisational performance is important. It was surprising that the results were so strongly

significant for the moderation approach but not for the matching approach (Table 8). This

suggests that the matching approach measures something quite different. This study

therefore goes further than earlier literature in raising doubts about the use of this more

intuitively appealing approach. The difference between the results is even more striking

since both of the approaches measured ‘total fit’ and thus must be due to more than just the

quirks of one particular strategy area. This shows that further research is required to

explain some of these unexpected and possibly counter-intuitive results.

Firms achieved differing levels of alignment. This suggests that some firms are doing

things differently, so the process of alignment deserves further study. For example, the

high alignment of many firms implies different levels of IT planning. Blili and Raymond

(1993) argued that SMEs must adopt some kind of framework for planning IT. In an

attempt to address this weakness, Levy and Powell (2000) proposed an approach to ISS

development aimed specifically at small firms. The Levy and Powell approach to ISS

development is relatively new. Further research could examine whether it can be used to

increase IT alignment.

Prior studies in large firms show that alignment is influenced by a broad range of factors

(Luftman et al., 1999; Reich and Benbasat, 2000). Importantly, both IT and non-IT

managers influence alignment in large firms. However, most small firms do not have IT

managers or an IT department. Thus small firm alignment requires further study as it

seems likely that not all of the factors identified by Luftman et al. (1999) and Reich and

Benbasat (2000) seem applicable to small firms. Less formal aspects may be key within

small firms. For example, the multiple responsibilities taken on by some managers within

small firms could mean that many managers are involved in strategy development, making

it easy to share ideas about opportunities for IT, and thus fostering connections between

business and IT planning processes.

At another level, the study showed that some business strategies were better aligned

than others, in particular, quality service, quality products and production efficiency.

Future studies could focus on specific business strategies to understand how each is

supported by IT. Maybe some strategies are easier to support with IT. Alternatively, some

firms may be targeting IT at specific strategies.

Venkatraman (1989b) discussed the ‘strategic orientations’ of firms. It seems

possible that the varying levels of alignment reported in this study are a reflection

of ‘IT orientations’, i.e. ways that managers and employees within firms view and

treat IT. The generic IS linking strategies proposed by Parsons (1983) may provide

a good starting point for identifying IT orientations. Some of the Parsons’ strategies

may apply to small firms, particularly centrally planned, scarce resource and

necessary evil. Also, Berry (1998) proposed a strategic planning typology for small

firms. Furthermore, Joyce et al. (1996) identified ‘strategic planning styles’ linked to

process and product innovation in small firms. Importantly, these or other IT


orientations may reflect IS cultures that have strong influences on IT alignment in

small firms.

6.2. Implications for practice

This research demonstrated that alignment between business strategy and IT strategy

was clearly linked to organisational performance. This evidence supports prior research

findings in large firms and implies that either high performing firms are good at aligning IT

with business strategy, or that IT alignment influences organisational performance. The

latter possibility, i.e. that IT alignment influences performance, suggests that small firms

can benefit from IT alignment. Thus managers of small firms should give high priority to

IT projects that support their business strategies.

This study found that it was useful to view IT alignment as the interaction between

business strategy and IT strategy, rather than the simple match between the two. This

suggests that firms should aim to support their major strategies with IT, rather than attempt

to support all strategies. For example, if quality and customer service are significant

strategies, then managers should make sure that their IT is highly aligned to quality and

customer service. The need for a strategic perspective implies the involvement of senior

managers. This probably requires the owner–manager or CEO to take an active role in

seeking IT alignment.

6.3. Limitations and research opportunities

In the absence of prior instruments aimed at small firms, the study had to create a

research instrument to measure IT alignment. Efforts were made to ensure that the

principal aspects of BS were present in the instrument, based on an extensive search of the

literature and pre-testing with small business managers. However, other elements of

strategy may have been overlooked. Future research could focus on rigorously validating

the instrument used to measure IT alignment, including using multiple items to measure

each strategy, similar to Chan et al. (1997).

It is also important to note that this study was based on a survey. This approach has

shortcomings as it captures a situation or an event at a point in time. For example, the

organisational impact of IT may not have been fully realised unless the IT had been

implemented well before the study. Future research could employ a more qualitative

approach, such as the case study or a longitudinal study.

This study measured IT alignment based on paired items for both business strategy and

IT strategy. The study also focused on measures of total alignment. Future research could

explore other ways of measuring fit. For example, alignment with a firm’s dominant

business strategy based on Porter (1980), or a more systems/holistic approach.

Another limitation of the study concerns the cause and effect relationship between IT

alignment and organisational performance. There are potentially other factors that could

influence alignment and business performance. A cross-sectional study such as this cannot

prove cause and effect relationships. Causal effect can only be assumed by virtue of the

non-experimental research design adopted throughout this study.


7. Conclusion

While many theoretical frameworks relate IT strategy, business strategy and

performance for large firms, this is the first study to propose and use a measure of IT

alignment in small firms. Furthermore, few studies have examined the strategic use or

impact of IT in small firms. This study provided a substantial contribution to our

understanding of strategic IT in small firms. It not only gained evidence of the importance

of IT alignment in small firms, it also indicated that the moderation approach could be

superior to the matching approach. The findings could foster strategic planning for IT in

small firms, and encourage researchers to further examine the links between business

strategy and IT strategy in small firms.

Appendix A. Extracts from questionnaire showing main variables

The following statements help us understand your business strategy. Please indicate by

ticking the appropriate box the extent to which you agree with each statement as best

reflecting your company’s business strategy in the past two years

One of the main aim of the study is to see how certain IT strategies influence

performance of the firm. The following questions relate to your firm’s overall performance

and the perceived IT impacts on the firm.


Relative to your industry’s average or to comparable organisations, what is, in your

opinion, the performance of your organisation in regard to the following criteria:

The following statements help us understand the strategic value of your current

information systems. Please indicate by ticking the appropriate box the extent to which you

agree with each statement

What, do you think, are the impacts of IT on your organisation for the past two years?

Please indicate your level of agreement with each statement of impacts below


Appendix B. SPSS output for factor analysis of all 18 strategy items showing rotated

component matrix

Strategy variables Component1 2

BS1 ¼ cheaper price of products 20.126 20.203BS2 ¼ quality products 20.038 0.690BS3 ¼ product differentiation 0.224 0.577BS4 ¼ new products 0.178 0.634BS5 ¼ product diversification 0.032 0.448BS6 ¼ production efficiency 0.120 0.613BS7 ¼ quality service 20.105 0.677BS8 ¼ intensive marketing 0.238 0.579BS9 ¼ market growth 0.153 0.515ITS1 ¼ reduce cost 0.514 0.056ITS2 ¼ product differentiation 0.679 0.149ITS3 ¼ product quality 0.701 0.051ITS4 ¼ new product introduction 0.743 0.086ITS5 ¼ production efficiency 0.533 0.088ITS6 ¼ product diversification 0.720 0.097ITS7 ¼ quality customer service 0.478 0.199ITS8 ¼ intensive marketing 0.697 0.168ITS9 ¼ identify new markets 0.704 0.103

Extraction method: principal component analysis; rotation method: varimax with Kaiser normalisation;

rotation converged in three iterations.


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