the impact of is-marketing alignment on marketing performance and business performance

The Impact of IS-Marketing Alignment on Marketing Performance and Business Performance Val A. Hooper School of Information Management Victoria University Sid L. Huff School of Information Management Victoria University Peter C. Thirkell School of Marketing and International Business Victoria University

Abstract

The importance of the alignment between information systems (IS) and the business has been emphasized for over a decade. To date, no empirical study has explored the impact of the alignment of IS and marketing, despite initial indications that such an alignment could impact favorably upon business performance. This study reports on a new conceptualization of alignment, together with the development and testing of a parsimonious model which addresses this issue. Data from a survey of 415 respondents from medium-large New Zealand companies were used to test the model. It was found that IS-marketing alignment had a positive impact on both business performance and marketing performance, and that marketing performance in turn had a modest but positive impact on business performance.

ACM Categories: H.5.3 Group and Organization Interfaces

Keywords: strategic alignment, information systems, marketing, strategic orientation, business performance, marketing performance.

Introduction

The strategic management literature emphasizes the importance of aligning functional strategies to the overall corporate strategy. Not only will this lead to a more concerted and focused pursuit of the corporate objectives, but the synergies derived from the alignment of various functions will enhance the achievement of these objectives. It has been argued that, broadly speaking, the strategic management of organizations is essentially the search for overall business alignment (Andrews, 1980; Drazin & Van de Ven, 1985).

Alignment has thus become an issue of importance and concern. In particular, alignment has been intensively examined from the information systems (IS) perspective – most commonly, at the level of IS and business strategy. Research on this matter has been underway for over 20 years now (Chan & Reich, 2007; Johnson & Carrico, 1988), and the findings indicate strongly that alignment between IS and business has a positive impact on business performance (Chan, 1992; Bergeron et. al., 2004). The question thus arises as to whether other modalities of alignment – for example, alignment between IS and other business functions such as marketing – may also exert an important impact on business performance.

The DATA BASE for Advances in Information Systems 36 Volume 41, Number 1, February 2010

The evidence is clear that IS contributes significantly to business performance (Galliers, 1993; Ross et al., 1996; Rivard et al., 2006), as does marketing (Jain, 1997; Kotler & Armstrong, 1996; Taghian & Shaw, 2008), and that they are both strategically important. However, despite indications that at an operational level a closer link between the two functions should have a positive impact on business performance (Zhu & Nakata, 2007; Fletcher & Wright, 1997; Sashittal & Wilemon, 1994; Winer, 2001), there have been no rigorous studies examining the strategic linkages between IT/IS and marketing, and the impact of such linkages on business performance. This paper addresses that gap.

Following a brief literature review, a set of three hypotheses are formulated, and a research model is presented. The model is tested using survey data gathered from 415 respondents, and conclusions are presented and discussed.

Literature Review

The years following World War II have been characterized by increases in the general scale of business operations and a greater focus upon strategic management. These changes in turn have led to more emphasis upon long-range planning, taking into account both external and internal considerations (Pearce & Robinson, 1988). Focus shifted during the 1980s to the importance of competitors (Day & Wensley, 1983), and during the 1990s to that of customers. Conducting business according to a defined strategy was perceived as essential (Jain, 1997). Strategy came to mean the “large-scale, future-oriented plans for interacting with the competitive environment to optimise achievement of organization objectives” (Pearce & Robinson, 1988, pp. 6-7).

Different levels of strategy can be distinguished. Corporate or business level strategies have a larger, longer term orientation, and focus more on ‘doing the right thing’ (effectiveness), while the functional levels (or regional or any other sub-division of the organization) focus more on ’doing things right’ (efficiency) (Pearce & Robinson, 1988, p. 9). Functional areas normally define their own strategic direction under the umbrella of the overall business strategy (McDonald, 1995). Synergy between the strategies of different business units would - it has been assumed - impact positively on the outcomes or achievement of the objectives defined by the overarching business strategy (Venkatraman, 1989a). There is thus a twofold challenge for managers of functional areas: to become aligned to the overall business strategy and objectives, and

also to become aligned with other organizational functions.

The IT/IS business function has been shown in numerous studies to have contributed importantly to overall business performance (Galliers, 1993; Ross et al., 1996; Rivard et al., 2006). There are likewise many studies which have illustrated the positive impact of the marketing function on business performance (Taghian & Shaw, 2008; Jain, 1997, p. 14; Kotler & Armstrong, 1996). So it is evident that both IS and marketing contribute, in their own right, to the achievement of corporate objectives – and they do so by means of their own individual strategies which are guided, or should be guided, by the overall business strategy (Berthon et al., 1999). Furthermore, in a number of organizations, the work of the IS unit has been shown to be closely linked with other business functions, in particular marketing (Ross et al., 1996). This close connection between IS and marketing has been fostered in part by the explosion of the Internet and the proliferation of IT-based marketing applications, including CRM systems and search engines. These tools facilitate – and sometimes drive - activities such as customer relationship management and marketing research. The mutation of direct marketing into database marketing, and the various IS applications aimed at enhancing customer analysis and corporate understanding of their customers (Fletcher & Wright, 1997) further underscores the important link between IS and marketing.

There is evidence to suggest, however, that in most companies, each function appears to pursue its strategic direction separately, with little if any regard for its alignment with the other (Pender, 2001). Yet a few studies have noted a positive impact on business performance in the cases where IS and marketing are well aligned (Zhu & Nakata, 2007; Fletcher & Wright, 1997; Sashittal & Wilemon, 1994; Winer, 2001), although the evidence is mixed. Some of these studies focused on operational aspects or sub-areas of marketing only. Others failed to demonstrate positive outcomes (Colgate 1998; Fletcher &Wright 1997; Nelson, 1999; Li, 1997). As one example of the inconsistencies in the literature, Winer (2001) illustrated how CRM could be used to both the customer’s and the firm’s advantage, although Cina (2002) noted that the CRM process was typically driven by IT, which interfered with the real objective, which was to foster long-term relations with customers.

There do not appear to have been any rigorous studies to date which have examined the strategic linkage between IS and marketing, and the impact of such a linkage on business performance.


Alignment - An IS Perspective

Although alignment is a universal issue in business and not solely the concern of IS management (Venkatraman, 1989b), it has been prominently addressed from the perspective of IS and business strategy (e.g. Henderson & Venkatraman, 1991; Luftman, 1997; Sabherwal & Chan, 2001). Among the possible reasons for the strong IS emphasis on alignment are the indications that aligned IT could enhance an organization’s competitiveness (Porter & Millar, 1985) or competitive advantage (Eardley et. al., 1996); that alignment could maximise IT investments and lead to improved profitability (Papp et. al., 1996); and that it could have a generalised positive impact on business performance (Venkatraman & Prescott, 1990). The fast-changing nature of IS technologies may also have contributed to concerns around alignment, as the role of IS within the organisation experienced frequent review and reassessment.

The issue of alignment of IS within the organization has remained among the top 10 major concerns of IS and general managers alike for over two decades (Boynton & Zmud, 1987; Niederman et. al., 1991; Luftman & Mclean, 2004). Remenyi (1996) further identified the lack of alignment between IT and business strategies as one of the ten most common IT mistakes.

One of the first models to address alignment at a strategic level was developed by Henderson and Venkatraman (1991). Although their Strategic Alignment Model depicted the conceptual relationship between IT and business, they did not attempt to quantify alignment. Since their original work, much of the alignment research has centred on four basic questions: (a) conceptualization - what precisely do we mean by alignment? (Henderson & Venkatraman, 1993; Avison et al., 2004); (b) measurement - how should alignment be measured or calculated? (Chan et al., 1997); (c) causation - what factors lead to/inhibit alignment? (Reich & Benbasat, 2000); and (d) impact - does alignment affect other variables, such as business performance? (Sabherwal & Chan, 2001; Cragg et al., 2002).

One of the first attempts to quantify alignment came from Chan (1992), who developed and validated a model which measured the alignment between the strategic orientation of a business and its IS strategy. IS strategic fit was defined as “the coherence or synergy between business strategic orientation and IS strategic orientation.”

Chan’s measures were based on the earlier work of Venkatraman (1985, 1989a). Venkatraman had developed a model of business strategy referred to as strategic orientation. He had argued that strategy was a multidimensional concept, characterized by an organization’s position on a set of seven key dimensions: aggressiveness, futurity, innovativeness, proactiveness, riskiness, analysis, and defensiveness. Following Venkatraman’s (1985) strategic orientation dimensions, Chan (1992) created parallel dimensions to apply to an organization’s IS function. By comparing an organization’s business strategy position along each of the seven dimensions with the extent to which its IS strategy supported the same dimensions, a means was provided for calculating the strategic alignment score for the organization. Chan et al. (1997) used survey data to show that the fit between business strategic orientation and IS strategic orientation in turn had a positive impact on both IS effectiveness and business performance. This work succeeded not only in developing a method and instrument to measure alignment, but also in demonstrating the positive impact of alignment of IS and business on business performance. Subsequent studies by Chan et al. (2006), Bergeron et al. (2004), Papp (1998), Tallon and Kraemar (1998) and Cragg et al. (2002) supported these findings.

Reich and Benbasat (2000) differentiated between the intellectual and social dimensions of alignment. The intellectual dimension has to do with the existence of a quality set of IT and enterprise strategic plans which show clear evidence of inter-linking. The social dimension has more to do with quality IT-business communication, and effective shared understanding in the minds of senior IT and other business executives. A large majority of the research on IT strategic alignment addresses the social dimension, including the present study.

More recently, researchers have begun to examine the issue of IT alignment within particular contexts, including business function, industry, sector, and firm size. Dery (2003), for instance, used a case study in a single large Australian firm to determine the factors which enable or inhibit strategic alignment between the IT and human resource (HR) functions. Franke et al. (2006) explored alignment within the financial services industry, in a study of five German banks. Gregor et al. (2004) focused on the public sector in Australia to determine the enablers of social alignment within that sector. Cragg et al. (2002) studied IT alignment within the context of small manufacturing firms. No studies, however, have yet examined the impact on business performance at the level of strategic alignment between IS and marketing.


Alignment - A Marketing Perspective

The term ‘alignment’ as discussed to this point in the article has no direct equivalent within the marketing literature. The central idea of marketing is to match a company’s capabilities with the desires, needs and wants of customers in order to achieve the objectives of both parties (McDonald, 1995). The term ‘alignment’ is thus used in the marketing literature to refer to, and emphasize, the importance of aligning the organization to the customers and their needs (Mitchell, 2001). As such, marketing performs a boundary role between the company and its markets. It is this role which is critical to strategy development. Along with recognizing the importance and necessity of a marketing strategy, it is the boundary role of marketing that underscores the strategic importance of marketing (Jain, 1997).

Writers have frequently emphasized the fact that an organization’s functional strategies should be guided by the overall business strategy (McDonald, 1995). It was therefore surprising that we were unable to locate any studies which focused specifically on the alignment (or ‘fit’ or ‘linkage’) between marketing strategy and business strategy. Nor in fact were studies found on the alignment between marketing strategy and the strategy of any other business function. Furthermore, no studies were located which examined the strategic orientation of marketing, although Noble et al. (2002) did interpret market orientation as being a type of strategic orientation.

Market orientation had its origins in the marketing concept, which was first articulated by Drucker in 1954 (Morgan & Strong, 1998). Only addressed formally in the 1990s, the two most widely accepted and applied models of market orientation are those of Narver and Slater (1990) and Kohli and Jaworski (1990). Narver and Slater depicted three components impacting on business performance: business specific factors, market orientation (customer orientation, competitor orientation and inter-functional coordination), and market-level factors. Kohli and Jaworski proposed that market orientation was “the organization-wide generation of market intelligence pertaining to current and future customer needs, dissemination of the intelligence across departments, and organization-wide responsiveness to it.” What is clear in both models of market orientation is that they share similarities with strategic orientation, albeit with a particular and distinct focus upon customers and competitors as well as the need for an integrated approach and inter-functional coordination. This in turn indicates that, for marketing to be fully effective in helping to advance the strategic objectives of the firm, it must

have a clear grasp of the overall business strategy if it is to harmonize and align with the efforts of other functional areas also seeking to advance those same strategic objectives.

It is this idea that led us to view alignment as extending beyond just an assessment of fit between a given function’s strategic objectives and overall corporate strategic objectives. A fuller conceptualization of alignment also takes into account the extent to which a given function’s understanding of firm-level strategic objectives aligns with the understanding of another functional area. Ideally, each function would have a similar understanding or perception of the firm’s strategic objectives so that their resultant functional strategies were based on a similar point of departure. In this study the functional areas under examination are IS and marketing.

An Extended Conceptualization of Alignment

The need for IS and marketing to work together effectively in pursuit of agreed strategic objectives for the enterprise will continue to grow as commercial activity moves more and more to the online environment. Both functions are interdependent in, for example, delivering value to customers and ensuring a satisfactory experience. Marketing has insights into customer behavior and how this translates into specific product and service requirements. IS has capabilities in managing information flows and ensuring that its services, among other things, fully support the product availability and service level needs of the market. IS and marketing also have a more general shared interest in effectively managing the customer database as a source of customer insights, and as a growing channel for service delivery and response.

One approach we considered was to extend the work of Chan (1992) by simply building parallel dimensions for an organization’s marketing function (STROMKT), and then assessing the fit or alignment of those measures against the equivalent seven strategic orientation dimensions (STROBE). There is some merit to such an approach because it would traverse themes that resonate easily with the domain of marketing decision-making and practice, albeit at a more operational level. These include innovation and creativity, customer preferences, response to changing trends, and relationships with suppliers and customers. However, such an approach would also add complexity because then there would be three sets of measures to consider (STROIS, STROMKT and STROBE), and it would still leave unanswered (and unanswerable) the question of how differing perceptions across IS and marketing


about overall strategic objectives might impact upon performance. A more parsimonious and straightforward approach was considered to be through directly profiling perceptions about the strategic objectives of a given enterprise on the part of both IS and marketing senior managers, modelling the differences across those profiles, and then assessing the relationship between the magnitude of those modelled differences and overall performance. We term this approach to measuring alignment as IS-marketing alignment.

This approach is consistent with previous studies in other areas. For instance, Schniederjans and Cao (2008) examined the alignment between the general managers’ and operations managers’ perceptions of operation strategy, perceptions of IS strategic orientation, and perceptions of the fit between operations strategy and IS strategic orientation. They based their work largely on that of Chan et al. (1997) and Joshi et al. (2003). Joshi et al. (2003) examined the alignment of perceptions of general managers and manufacturing managers regarding strategic manufacturing priorities, and the impact of that alignment on the manufacturing performance. Rapert et al., (2002) evaluated shared intra-organizational perceptions, values and beliefs and their impact on business performance.

There are several contributions resulting from such an extended conceptualization of alignment. It enriches our understanding of what alignment means across functional areas as well as within a particular functional area – in this instance between two functions that increasingly need to share a harmonized set of strategic priorities if they are to be fully effective. It holds diagnostic potential, allowing specific areas of difference across IS and marketing to be identified and resolved. It is parsimonious, with the measure of alignment being inferred on the basis of differences between responses from senior IS and marketing managers rather than having managers complete two sets of responses based on a similar item pool. Perhaps most importantly, the approach eliminates the need for managers within a given functional area to make judgements about the extent to which their views about strategic priorities agree or differ with the views of managers in other functional areas. In some instances such judgments simply could not be made. Yet knowing how well IS and marketing do align or not in their understanding of overall strategic objectives for the firm is important if each is to be effective, both individually and corporately.

The Dependent Variable

Business performance is the ultimate goal of all business/organizational activities. Achieving this goal underlies the corporate strategy, which in turn drives the strategies of the various functions. In the studies of IS-business alignment, most researchers emphasized the impact of alignment on business performance (e.g., Henderson & Venkatraman, 1993; Chan, 1992; Saberwal & Chan, 2001). Measures typically consisted of both financial indicators (e.g. ROA), and market-related indicators (e.g. market growth). In addition to business performance, Chan (1992) also chose to use IS effectiveness as a dependent variable, intermediating between alignment and business performance.

The measurement of marketing performance has been an issue of concern to the marketing discipline and industry for decades (Feder, 1965; Rayburn, 1977; Pulendran, et. al., 2000). Not only have different terms been used to denote marketing performance, but different components of performance have been assessed.

In 1977 Kotler produced guidelines for a marketing effectiveness audit. Walker and Ruekert (1987) subsequently suggested using efficiency and effectiveness to measure marketing performance, and in 1988 Bonoma and Clark presented their marketing performance assessment model which combined the two aspects of marketing effectiveness and marketing efficiency. Vorhies and Morgan (2003) similarly viewed effectiveness as being a component of marketing performance.

Many of the measures for marketing performance and marketing effectiveness reflect those of business performance. These include financial performance (Hansen et al., 1990), market share growth (Manu, 1993), profits, market size and growth (Theodorakioglou & Wright, 1998). Overall, they exhibit a strong focus on financial as well as market measures – both absolute and relative.

Customer responses – both attitudes and or behaviours - also featured in the work of a number of researchers. Tansuhaj et. al. (1988) used customer loyalty, customer perception of quality, and customer satisfaction as measures of marketing performance. Morgan et al. (2002) used customer perceptions and customer behaviour, as well as the more commonplace financial outcomes of marketing performance. Reim (2002) used changes in consumer behaviour as an outcome measure of marketing effectiveness, while Tezinde et al. (2002) along with Appiah-Adu et al. (2001) researched


response rates and customer retention respectively in their studies on marketing effectiveness.

In summary, a number of different conceptualizations have been used in both the IS and marketing research literatures to denote the outcomes of IS or marketing activity. There is no commonly agreed upon single dependent variable. In the IS research, business performance is a commonly used variable, while in the marketing area, marketing performance is perhaps more common. In this study, we have adopted business performance as the ultimate dependent variable, and marketing performance as an intermediate ‘dependent’ variable, as discussed below.

Research Model and Hypotheses

Research has clearly illustrated that both IS and marketing, each in their own right, exercise a positive influence on business performance. A number of studies have also shown a positive relationship between IS-business alignment and business performance. A few studies (Zhu & Nakata, 2007; Koo et al., 2007) have suggested that, where there is a close link between IS and marketing, albeit not necessarily at a strategic level, the impact on business performance appeared to be significant. We hypothesize a similar outcome for the alignment between IS and marketing at a strategic level.

H1: The stronger the IS-marketing alignment, the stronger the business performance

Chan (1992), Chan and Huff (1993) and Chan et al. (1997) additionally found a positive impact of the alignment between IS and business on IS effectiveness. We hypothesize a similar effect from

IS-marketing alignment, namely, that IS-marketing alignment leads to a stronger marketing outcome. However, rather than use the narrower concept of marketing effectiveness as a dependent variable, a broader and more defensible approach as argued above was to use marketing performance which incorporates aspects of both marketing effectiveness and efficiency.

H2: The stronger the IS-marketing alignment, the stronger the marketing performance

Chan (1992) and Chan et al. (1997) found that IS effectiveness acted as an intermediary variable impacting positively in turn upon business performance. Given some initial evidence (Jain, 1997; Carrillat et al., 2004) that marketing performance contributed to business performance, and the expectation that IS-marketing alignment would impact positively on marketing performance, it follows that marketing performance would have a positive intermediary effect upon business performance.

H3: The stronger the marketing performance, the stronger the business performance.

All three hypothesized relationships are depicted in the research model (Figure 1).

Construct Measures

Chan (1992) developed and validated an approach for calculating IS-business strategic alignment; it was decided to follow an approach similar to hers in the present study for determining IS-marketing alignment. An alternative would have been to develop a set of questionnaire items designed to tap the respondent’s perceptions of alignment directly, and to construct a psychometric measurement instrument from them.

Figure 1: Research model

IS-marketing Alignment

Business performance

Marketing performance

H1

H2

H3


Table 1: The seven dimensions of strategic orientation Dimension Description Aggressiveness The way in which companies approach their external environment – and their competitors in particular. The

focus is more on effectiveness than efficiency. Aggressiveness could be based on product innovation and/or market development, or on high investment to improve relative market share. It could also reflect an expansion of market share by ‘multiplication’ (Venkatraman, 1985).

Futurity The distinction between long-term and short-term perspectives, and the degree of focus on long term sustainability. Venkatraman (1985) highlighted the forecasting of sales and customer preferences, as well as formal tracking of electronic trends.

Innovativeness The level of attention to innovation and creativity with regard to offerings in the marketplace, ways of conducting business, and the use of IT in promoting innovativeness in general (Chan, 1992).

Proactiveness The degree of striving to improve a company’s strengths and to seize as many opportunities as might arise. While there is an element of aggressiveness in terms of beating the competition to seize opportunities, the focus is more on achieving the lead thereby being more able to control than being controlled. Venkatraman (1985) cited Miles and Snow (1978) who had emphasized the continuous search for market opportunities, and experimentation with potential responses to changing trends.

Risk aversion A reflection of the caution that a company might exercise in the way it approaches both its internal operations, as well as its external environment. Related to but opposite in focus to ‘riskiness.’

Analysis The degree of caution exhibited by a company in making decisions, particularly about major business situations. Venkatraman (1985) indicated that this reflected comprehensiveness and a searching for the root of problems in order to find the best solutions.

Defensiveness The extent to which there is a strong focus on a company’s protection of itself from the competition. This may take the form of protecting and maintaining their internal strengths, or improving their internal efficiency. It may also reflect entrenching relationships with suppliers or customers, or adjustments in bargaining power (Venkatraman, 1985).

However alignment is a multifaceted construct embodying multiple dimensions of both IS and marketing characteristics. The likelihood of individual respondents possessing the necessary personal knowledge to provide an appropriate basis for responding to such items was considered to be low, and questions of validity would have remained.

Chan’s approach earlier, and ours here, are based on Venkatraman’s (1985) concept of strategic orientation. An organization’s strategic orientation comprises seven distinct dimensions: aggressiveness, futurity, innovativeness, proactiveness, risk aversion, analysis and defensiveness. These seven dimensions are elaborated in Table 1. Venkatraman originally developed and validated scales for each dimension, and a corresponding questionnaire he called STROBE (Strategic Orientation of the Business Enterprise). Chan modified the Venkatraman scales somewhat to develop a parallel instrument, STROIS, Strategic Orientation of Information Systems. She was then able to calculate an alignment value for each responding firm by comparing the responses to the STROBE and the parallel STROIS questions.

For the present study, a variation on Chan’s approach was followed. In each responding organization, the head of IS, and the head of marketing, completed the original STROBE instrument. This then provided a proxy for the information systems function’s perception of the company’s strategic orientation, and also for the marketing function’s perception of the company’s strategic orientation. IS-marketing alignment was then calculated using these two sets of responses, as depicted in Figure 2. (Details of the calculation are discussed later.)

A variety of different approaches were considered for operationalizing the marketing performance and business performance constructs, drawing from measures published in earlier research, and reflecting the inherently multidimensional nature of the outcome measures. This was not surprising given the complexity and diversity of modern business environments. For example, with regard to business performance, Chan and Huff (1993) produced a comprehensive list of typical business performance measures used in 19 studies. These included ROE, ROI, ROA, return on total assets, market share, sales growth.


Marketing’s

perception of the strategic

orientation of the firm

IS’s perception of the strategic orientation of

the firm

IS- marketing alignment


Marketing

performance

Figure 2: Expanded research model

Based on their analysis they reduced this large collection to a parsimonious set of four measures: market growth, financial performance, product-service innovation, and company reputation. Henderson and Venkatraman (1993) further suggested, among others, improvement in operational costs, increased ability to respond to market conditions, and superior information flow and processing. Bonoma and Clark (1988) argued that measures of business performance fall into four main categories – absolute and relative financial measures, and absolute and relative market measures. In order for a measure of business performance to be comprehensive, it should embrace measurement items from all four categories.

For the present study, we drew from the previous literature and identified six indicators for each of the performance constructs, as shown in Table 2. To capture the various nuances of marketing performance, both efficiency (Vorhies & Morgan, 2003) and effectiveness (Morgan et. al., 2002) were measured. This approach was consistent with the approach of Chan (1992) who used some satisfaction measures for IS effectiveness which echoed customer-relatedness (eg. satisfaction with IS staff and services), and also efficiency measures such as IS contribution to operational efficiency.

Scales from instruments which had been validated and found reliable in previous research were used where possible to ensure content validity (Zmud & Boynton, 1991). Chan’s (1992) adaptation of Venkatraman’s original STROBE instrument formed the basis for items used in the measurement of the perceived strategic orientation of the firm.

Table 2: Items used in measuring business performance and marketing performance

Measure Source

Business performance measures

Net profits Derived from Kohli & Jaworski (1990); Derived from Chan & Huff (1993)

ROI Narver & Slater (1990); Chan & Huff (1993)

Revenue growth Derived from Chan & Huff (1993)

Sales growth Prasad et al. (2001)

Market share gains Derived from Kohli & Jaworski (1990)

Overall performance Jaworski & Kohli (1993)

Marketing performance measures

Customer satisfaction Tansuhaj et al. (1988)

Customer retention Appiah-Adu et al. (2001)

Customer loyalty Tansuhaj et al. (1988)

Return in marketing investment (ROMI)

Derived from Narver & Slater (1990)

Efficiency of marketing promotions

Derived from Bonoma & Clark (1988)

Overall marketing performance

Derived from Jaworski & Kohli (1993)

calculate


Twenty three items were selected to measure the seven dimensions of strategic orientation. Seven additional items were included to measure key demographic variables.

Data Collection

In order to test the hypotheses, a quantitative survey approach was adopted. Two sources were used for the sampling frame of medium-large New Zealand companies: The Atlantis 800 Business Directory (2003) and Top 2003 (Deloitte Management, 2003). A random sample of companies was selected and potential participants were contacted by telephone. No company was included in the survey unless both the heads of IT/IS and marketing agreed to take part. In total, 281 companies (562 individuals) indicated they would participate. Questionnaire packages were then posted to each individual together with a personalized covering letter. Each questionnaire was coded in order to identify the respondent.

In each responding organization, the head of IT/IS and the head of marketing were asked to complete survey questionnaires which contained similar items in every respect, except that the heads of marketing would answer additional questions on marketing performance. Five point Likert scales were used for the response options to all of the independent and dependent variables. Only a few demographic questions required open-ended responses.

A number of items were reverse coded in order to reduce response set bias (Hinkin, 1998).

Although most of the proposed items for the questionnaire had been validated in prior research, pre-testing of both versions of the survey instrument was conducted with seven marketing and IT/IS managers. The pre-test turned up a few instances of inconsistent wording, which were corrected.

Following the guidelines of Dillon et al. (1994), there were two main follow-up stages, a reminder letter at three weeks and a phone call at six weeks. In total 415 completed questionnaires were returned – a 74% response rate. There were some instances where only the head of IT/IS or the head of marketing responded. In total 175 companies were represented by both the required heads – a 62% company response rate. Because of the high response rate, non-response bias did not pose a significant problem.

In order to ascertain the convergent validity and discriminant validity of the factors, the loadings of

each item onto the respective factor should be above 0.6 although loadings of 0.5 and above are acceptable for larger samples such as 400 or more (Hair et al., 1998). To determine reliability of a factor, a Cronbach’s alpha of over 0.6 was specified (Hair et al., 1998).

Principal components analysis was used with a limit of 25 iterations for convergence (using SPSS versions 11.5 and 12). An eigenvalue of greater than 1 (Field, 2000, p. 436) was selected as the main benchmark for factor retention, although scree plots were also used as a guide. Varimax rotations with Kaizer normalization were used to maximize the loadings of the variables onto their relevant factors and to reduce any ambiguities that might confound interpretation of the analysis (Hair et al. 1998, p. 109).

In the analysis of the strategic orientation items, all the items loaded onto the intended factors. Only one of the ‘risk aversion’ items demonstrated a loading (0.407) of less than 0.6 (see Table 3). The Cronbach alphas of all factors met the 0.6 standard except for the ‘risk aversion’ factor which was 0.42. The proactiveness factor was marginal at 0.59 and did not improve with the deletion of any item, but was nevertheless retained for subsequent analysis.

Data Analysis

The data analysis proceeded in three separate stages: factor analysis to examine whether the proposed construct measures exhibited integrity; the calculation of alignment; and hypothesis testing using PLS.

Factor Analysis

Factor analysis was conducted in order to ascertain the validity and reliability of the various construct measures. The analysis of the perceived strategic orientation measures was treated as confirmatory, while the analysis of the business performance and marketing performance measures was both developmental and confirmatory.

The ‘risk aversion’ factor was significantly improved to a Cronbach alpha score of 0.73 by the exclusion of a weakly-loading item (the same item that fell short of the 0.6 threshold above), which referred to risky strategies adopted by the company as opposed to the other items which referred to risk averse strategies. It was therefore deleted from subsequent analysis.


The dependent variables were analyzed together. Items all loaded onto their intended factors in terms of the split between business performance and marketing performance, although ‘market share

gains’ cross-loaded on both factors. This ambiguity was not totally unexpected, and the stronger loading onto business performance supported the view that the item was a more appropriate measure for

Table 3: Strategic orientation, Factor analysis and item loadings

Item Factor

Aggressiveness Futurity Innovativeness Proactiveness. Risk Aversion

Analysis Defensiveness

F3 Q1 – Strive to be top company

.774

F4 Q2 – Try to be ahead of competition

.845

F5 Q3 – Act aggressively .780

F6 Q4 – Budget allocations short-term

.755

F7 Q5 – Long-term research for future

.773

F8 Q6 – Future-oriented .857

F9 Q7 – Innovative and imaginative

.848

F10 Q8 – Early adopters of innovations

.871

411 Q9 – Creative and original

.878

F12 Q10 – Seeking new business opportunities

.736

F13 Q11 – On lookout for business units to

.758

F14 Q12 – Expand capacity ahead of competitors

.745

F15 Q13 – Operations riskier than competitors’

.407

F16 Q14 – Conservative decision making

.791

F17 Q15 – Operations follow ‘tried and true’

.751

F18 Q16 – Risk averse .818

F27 Q25 – Require deal of information for decision

.785

F28 Q26 – Comprehensive analysis of business

.903

F29 Q27 – Highly analytical in decision making

.889

F19 Q17 – Attention to efficiency of operations

.740

F21 Q19 – Optimise coordination among

.757

F51 Q49 – Emphasis on relationships with key

.638

F63 Q61 – Emphasis on relationships with

.613

Cronbach alpha .70 .71 .83 .59 .73 .82 .63 (after item removed)


business performance. The items of business performance all demonstrated high loadings (over 0.7) onto the factor, its Cronbach alpha was 0.9008 (see Table 4). Similarly, marketing performance items loaded well onto their common factor, with loadings ranging from .62 to .80. The Cronbach alpha for the marketing performance scale was 0.80. The measures of business performance and marketing performance therefore demonstrated acceptable convergent and discriminant validity, as well as reliability.

Calculation of Alignment

The second stage of the analysis consisted of the development of a method for the calculation of alignment. This calculation was to be derived from the responses of the heads of IT/IS and marketing to matching items in their respective questionnaires.

The starting point was the work of Chan et al. (1997) which favored the moderation approach initially proposed by Venkatraman (1989a). Chan at the time acknowledged however that alternative models should not be dismissed in future work, and the moderation approach is known to have limitations in that it is not able to accommodate the ‘anti-synergy’ which might result from the IT/IS and marketing respondents’ scores being very different. To illustrate, take for example an item such as ‘We constantly try to be ahead of the competition’ where for one firm the pair of responses is 1 and 4, and for a second firm is 2 and 2. Using the moderation approach the calculated alignment score (xy) in each case would be 4. Yet intuitively, where both respondents produced similar scores we would interpret them to be of similar mind and more likely to act accordingly in their approach vis-à-vis the competition. In contrast, if each had widely different perceptions regarding their approach to the competition, they would act differently, and in what might even be a contradictory or ‘anti-synergistic’ way, whereby the difference would be magnified. Yet the moderation approach takes no account of such differences.

Given this limitation, a ‘matching’ approach was considered (Venkatraman, 1989a), which used the absolute difference between the marketing and IT/IS scores on each matching item. The rationale was that any difference would reflect a lack of alignment between the two respondents, with higher absolute differences reflecting lower alignment in respect of that item. The absolute difference is used, reflecting the assumption that only the magnitude of misalignment matters. However, as Chan (1992) pointed out, this approach fails to reflect the interaction effect: alignment along more salient dimensions of strategic orientation should be more heavily weighted than alignment along less salient dimensions.

In order to address the shortcomings of both moderation and matching approaches, while retaining as far as possible the merits of each, the following formula was developed:

(4- |x-y|)((x+y)/2)

The formula combines the moderation and matching methods, and also reverses the sign of the result so that smaller absolute differences between the IT/IS and marketing responses for any dimension of the perceived strategic orientation of the firm (i.e., stronger alignments) result in larger values for the alignment index. Given 5-point Likert scales on the various questionnaire items, the alignment index can

Table 4. Factor analysis results for business performance and marketing performance

Item Factor Bus

Performance Mkt Performance

F67 Q65 – Revenue growth

.872

F70 Q68 – Overall performance

.872

F65 Q63 – Net profits .843 F66 Q64 – Return on

investment .839

F68 Q66 – Sales growth .782 F69 Q67 – Market share

gains .910

F76 Q74 – Overall marketing performance

.789

F74 Q72 – Return on marketing investment

.723

F72 Q70 – Customer retention

.711

F73 Q71 – Customer loyalty

.696

F71 Q69 – Customer satisfactions

.626

F75 Q73 – Efficiency of marketing promotions

.618

Cronbach alpha .90 .80


assume values ranging from 0 (very low alignment) to 20 (very high alignment).

To extend the example cited earlier, the first pair of responses (1, and 4) would lead to an alignment score of 2.5, whereas the second set (2, and 2) would lead to a score of 8, a more intuitively valid result which reflects appropriately both the matching (absolute difference) and interaction effects.

The formula was applied only to the 175 pairs of responses, that is, the companies from which both the heads of IT/IS and marketing had responded. The alignment index was calculated as the average score across the alignment on all seven dimensions, and formed the sole independent variable in the model which would be tested in the next stage of the analysis.

Structural Equation Modelling

The final stage of the analysis consisted of testing the research model. Structural equation modeling (SEM) using Partial Least Squares (PLS) was the specific technique adopted. PLS is a multivariate technique that simultaneously executes both factor analysis and aspects of multiple regression in order to estimate interrelated dependent relationships (Hair et al., 1998, p.583). It also allows path analytic modeling to be performed with latent (unobserved) variables (Chin, 1998).

In the case of this research, the constructs business performance and marketing performance were comprised of reflective factors, and the construct alignment was comprised of a single, formative factor – the calculated alignment index. Partial least squares analysis accommodates reflective indicators for some constructs in conjunction with formative indicators for others (Chin, 1998). PLS analysis was conducted in two stages: an assessment of the measurement model, followed by an assessment of the structural model (Compeau & Higgins, 1995). Chin’s PLS-Graph, version 3.0, was used for the analysis.

Measurement Model

In order to obtain an indication of the convergent validity and the extent to which the reflective factors were internally consistent, the level of their loadings onto their respective constructs was determined, as well as the significance of these loadings. Although loadings of above 0.5 are acceptable (Aubert et al., 1994), they should preferably be above 0.7 (Chwelos et al., 2001).

Normally the same procedure is followed for formative factors, but using weights instead of loadings. However, as the alignment construct only consisted of one formative factor, this would necessarily result in a perfect weight of 1.00, and such assessment was superfluous.

The loadings of the reflective factors, as well as their significance levels, are shown in Table 5. The loadings of the business performance factors were all above the 0.7 level, indicating that these measures demonstrated convergent validity. In addition, they all achieved a very high significance level (p value <0.001). Most of the loadings for the marketing performance factors were above the 0.6 level, although the loading of ‘return on marketing investment’ (ROMI) at 0.4971 was marginally below the 0.5 threshold. All factors reflected a high degree of significance (p-values <0.001), and all were retained in the construct measurement.

To determine the convergent validity, or internal consistency, of the measures, the composite reliability coefficient values should be at least 0.3, although the higher the value, the more internally consistent and reliable the measure (Tetiwat, 2003).

Table 5: Loadings of reflective factors for business performance and marketing

performance

Loading p


Net profits 0.8613 < .001

ROI 0.8572 < .001

Revenue growth 0.8748 < .001

Sales growth 0.8528 < .001

Market share gains 0.7489 < .001

Overall performance 0.9334 < .001


Customer satisfaction 0.6919 < .001

Customer retention 0.7931 < .001

Customer loyalty 0.7686 < .001

ROMI 0.4971 < .001

Promotional efficiency 0.6775 < .001

Overall marketing performance

0.8640 < .001


Table 6: Composite reliability coefficients and AVE for business performance and marketing

performance

Construct Composite reliability coefficient

AVE


0.943 0.734


0.866 0.525

The average variance extracted (AVE) of each construct should also be higher than 0.5 (Goo et al., 2004). These values are depicted in Table 6. Because alignment only consisted of one factor, examining convergent validity or internal consistency of that construct was unnecessary.

The composite reliability coefficients of business performance and marketing performance were 0.943 and 0.866 respectively, thus indicating that these were highly reliable constructs. In terms of AVE, both those for the marketing performance and business performance constructs were above 0.5, indicating an acceptable level of average variance of all measures within each construct.

The discriminant validity of the measurement model was determined by examining the correlations between constructs and ensuring that the square root of the AVE of a construct is greater than the correlations between the construct and other constructs (Aubert et al., 1994; Chwelos et al., 2001). These are shown in the Table 7.

In all instances, the square root of the AVE was greater than the correlations between the other constructs, thus demonstrating discriminant validity for each of the constructs.

Structural model

In order to ascertain the model’s predictive validity, the explanatory effect of the independent variables upon the dependent variables, and the magnitude and significance of the path coefficients between the variables was assessed. The explanatory effect was assessed by means of the explained variance in the dependent variable, and should be above 0.1 to be substantive (Chan, 1992), while the path coefficients should be both substantive and significant (Goo et al., 2004).

As shown in Figure 3, the explained variance in business performance was 0.158, denoting an adequate predictive ability of the model. In the case of marketing performance (R2 = 0.070), only 7.0% of the variance in that construct was accounted for by alignment. However, given that many influences, over and beyond the IS-marketing alignment, impact on marketing performance, the fact that the explained variance is rather small was understandable and should not, in and of itself, necessarily suggest inadequacy in the model. Such an interpretation is strengthened by the observation that the path coefficient from alignment to marketing performance was substantive and highly significant.

In order to assess the significance of the paths between the constructs, a bootstrap procedure was applied. Convergence occurred within six iterations. This is within the acceptable range of up to 20 to indicate how well the model fitted the data (Hulland et al., 1996). The paths from alignment to business performance and marketing performance were both significant with path coefficients of 0.314 (p<0.001) and 0.264 (p<0.001) respectively. Hypotheses 1 and 2 were thus supported.

The path coefficient between marketing performance and business performance was rather small at 0.174 but significant at p<0.100. Hypothesis 3 was thus also supported, albeit less decisively than the other two hypotheses.

In addition, the model was evaluated by examining the Q2 predictive relevance for the dependent constructs. Q2 of greater than 0 implies that the model has predictive relevance (Eom, Ashill & Wen, 2006). A blindfolding procedure was employed, using communality measures. The Q2 of business performance was 0.6191 and that of marketing performance was 0.3261, indicating that the model possessed predictive relevance. This added further support for all three hypotheses.

Table 7: Inter-construct correlations and square root of AVE for business performance and

marketing performance

Construct Alignment Business performance


Alignment 1.000


0.360 0.857


0.264 0.257 0.725

Note: Bold, italicized values are the square root of AVE


R2 = 0.158

R2 = 0.070

IS-Marketing Alignment



.314 ****

.264 ****

.174*

**** p < 0.001 * p < 0.05

Figure 3: Structural model

Discussion and Implications

This research has addressed a matter of consistent concern to all areas of business – that of strategic alignment. It has focused on the alignment between two major business functions, IS and marketing, which has not been addressed previously at a strategic level.

A parsimonious model was developed which provides positive support for the suggestions in the literature that alignment between IT/IS and marketing can contribute positively to business performance (Fletcher & Wright, 1997, Hooper & Van Erkom Schurinck, 2003). In addition, the model demonstrates that IS-marketing alignment is also significantly related to marketing performance.

This study extends the application of Venkatraman’s (1989a) work and lends support to the robustness of his conceptualization and measurement of strategic orientation. It extends the conceptualization of alignment to embrace the importance of agreement of the perceptions of the firm’s strategic objectives by two functional areas of the firm. It also provides a diagnostic mechanism for identifying specific areas of difference between the strategic perceptions of IS and marketing, and suggests directions for resolving those discrepancies.

The research applies a new formula for the calculation of alignment. It builds on the moderation approach adopted by Chan (1992) but also incorporates elements of the matching approach. That is, it includes the aspect of synergy between IS

and marketing, which is considered to be as important as the level of agreement (lack of difference) alone.

An important feature of this research is the fact that it is cross-disciplinary, and generalizable. It demonstrates how strategic conceptualizations of one discipline can, indeed, be applied to another – so long as they remain true to the research demands of validity and reliability. It also highlights that the interests and concerns of different disciplines, at least in the management area, are becoming more intertwined.

As markets continue to change and evolve, and as the rate of ICT change increases, so there is a need to adopt a more holistic view of the business as a whole if senior managers are to make sense of the changes and respond in strategically coherent ways. This entails looking for common ground and connections between understandings of customer needs and wants, and the new possibilities created by technological change in meeting those needs and wants. Put another way, it means that marketing and IS in particular must share in this dynamic ‘matching process’ between customer needs and ICT evolution and change if the firm wants to advance its competitive position. For theorists and researchers in the respective disciplines, it suggests in turn the need for a common language to facilitate shared understanding, and a strengthened commitment to cross-disciplinary work that advances knowledge across a combined front for both marketing and IS.

The study has some limitations. In particular, the sample of firms from which data were gathered


included mainly medium-sized companies. Studies involving large firms present a future research opportunity. Also, while the model statistics calculated from the data were generally strongly significant, nonetheless some of them were only modest in size, e.g., an R-squared statistic of .07 for the variance in marketing performance explained by IS-marketing alignment. Clearly and not surprisingly, other factors than IS-marketing alignment also impact marketing performance. The findings are strong enough however to convince us that strategic alignment, as conceptualized in this study, is a fruitful area for continued enquiry.

In addition to the academic insights gained, a number of more general implications of the impact of the strategic IS-marketing alignment on business performance have been uncovered.

Firstly, the research highlights the benefit of different functions of a business striving towards a common purpose – a purpose which results from each function perceiving the strategic objectives of the firm in a similar manner, and then basing their individual strategies on their shared understanding of those objectives. High levels of cross-functional alignment result in synergistic benefits, the results of which are reflected in improved business performance. Strategic alignment ensures a more concentrated focus on the organization’s common purpose. In support of each function striving towards a common goal, the mindset of alignment would need to filter down through each function’s strategic planning, including aspects such as governance and ownership. It might well prompt companies to reassess the position of IT/IS in terms of its status vis-à-vis other functions, and the position of its head in senior management fora. In particular, there seems to be merit in having both the CIO and CMO (or their equivalents) operating at the same level of seniority within the firm. It was noted at the outset that marketing performs a boundary-spanning role between the company and its markets, and also that both IS and marketing have a shared interest in effectively managing the customer database. The findings suggest that the boundary-spanning role of marketing has an internal as well as an external aspect to it, in that the ultimate success of marketing endeavour is increasingly intertwined with its capacity to have a close, genuine and harmonious relationship with IS. In short, strategic success for the enterprise is likely to be determined even more in the future by the level of strategic alignment between these two key functions.

Closer alignment also places greater emphasis on teamwork, especially cross-functional teams. These include not only project teams but also permanent

teams, such as the cross-functional liaison units established in some companies. Closer alignment would therefore appear to favour less hierarchical and matrix-type organizations where reporting lines are ambiguous so as to foster working relationships across as well as within notional functional areas. The working imperative becomes more about developing a shared understanding of strategic priorities and then working with whomever is needed, regardless of disciplinary title, in pursuit of these shared objectives.

A consequential effect is that tighter connection between functions emphasizes the need for, and implicitly results in, a greater shared understanding of the different functions. Companies might foster this by means of formal training, job rotation, career path planning, or by relying on the establishment of cross-functional teams and units to enhance this cross-functional understanding. They might also emphasize the appointment or promotion of staff who have expertise in more than one function. Perhaps most importantly, a move to improve strategic alignment should have implications for the performance outcomes rewarded by the organization. Managers from both marketing and IS (and perhaps in time other functional areas as well) should be able to note one or two metrics which reflect how they have sought to build common understanding around market-based factors and ICT developments, including a shared set of agreed kpi’s. These could readily be slotted into a balanced scorecard type approach already in use by the firm. The result of such an approach would be the breaking down of the functional silos which have developed in many companies – often exacerbated by the individual functions’ developments or acquisitions of their own IT applications. Closer alignment between functions should in time impact favourably on overall organisational culture, including the way in which senior managers are assessed and rewarded.

A variety of future research opportunities present themselves. While our measures of business performance and marketing performance demonstrated reliability and validity, and although the business performance measures were generally clear, the possible ambiguity regarding whether ‘market share gains’ should be incorporated in the measure of business performance, or marketing performance, requires clarification. As well, the possible split between customer-related measures and those which focus on marketing efficiency could be further explored.

An additional opportunity for future research is an exploration of the relationships between alignment


and the individual dimensions of business performance and marketing performance. This would provide a more specific indication of the impact of alignment and strengthen the diagnostic potential of this approach. It might be influenced by the industry in which a company operates. For instance, market share gains might be more important in a growing industry than in a more mature industry where profit might be more important. By adopting an alignment mentality however, senior managers would become attuned to learning over time the specific facets of marketing and IS where alignment is most beneficial in noticeably improving marketing and business performance for their particular firm. This could been done in a stepwise fashion, whereby one area of activity (such as database configuration or ICT strategic trend assessment) was consciously targeted to improve and strengthen overall strategic alignment. More ambitiously, it could lead to a series of combined initiatives aimed at strengthening the points of contact between senior IS and marketing staff. This could include shared strategy sessions, physical collocation of staff, equivalent levels of seniority for the CMO and CIO, intertwined reward systems and common kpi’s, job rotation through both areas, recruitment of staff with backgrounds across both disciplines, cross-functional project teams, joint field visits, and IS representation on new product/service development teams. The key point to be made is that there be an intentionality within the firm to strengthen the strategic alignment between marketing and IS, and in time perhaps other functional areas as well. The study clearly opens up the possibility of assessing the alignment between other business functions, or perhaps, in an expanded approach, a simultaneous holistic assessment of multi-functional alignment. Doing so would be consistent with the calls for ‘systems’ studies of alignment by Bergeron (2004) and others.

The criticality of the strategic alignment of IS has been repeatedly emphasized in the research and also practitioner literature in recent years. Yet, nearly all previous studies have focussed on the alignment of IS with the overall business strategy. This study presented and tested a new conceptualization of this construct – the strategic alignment between the IS function and another major business function (here, the marketing function). By doing so it has expanded our understanding of “strategic alignment,” and opened the way to a deeper understanding of this important matter.

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About the Authors

Val Hooper is a Senior Lecturer in Information Management. She has researched and published in the areas of strategic alignment of IS, alignment of IS and marketing, the use of IT/IS in developing countries, and online behaviour as evidenced in online auctions and online gambling. Her current research focuses on strategic alignment of IS in the public sector, the IT productivity of SMEs, promotion of potentially harmful products and behaviour via the Internet, and the development of social norms in the online environment. Sid Huff is Professor of Information Systems and Head of the School of Information Management at Victoria University of Wellington, New Zealand. His teaching and research focus on IS strategy, IT governance, senior management roles in information systems, and IS management. His work has appeared in numerous academic and practitioner

journals, including MIS Quarterly, Information Systems Research, Journal of MIS, Journal of Strategic Information Systems, Communications of the ACM, CAIS and others. He has also written over 60 teaching cases for educational use, and was the originator of the IS World web site on Teaching IS with Cases. His most recent book is Managing IT Professionals in the Internet Age, co-authored with Dr. Pak Yoong. He holds degrees in Applied Mathematics, Electrical Engineering and Business Administration, from Queens University. He received his Ph.D. in Information Systems from the M.I.T. Sloan School of Management. Peter Thirkell is Professor of Marketing at the Victoria University of Wellington. He has interests in emarketing, the impact of the Web on business models and marketing strategies, and the relationship aspects of logistics and consumer behaviour. Peter has published on relationship marketing and its connection with Web-based technologies, the strategic value of CRM systems, and the development of customer loyalty in journals such as Market-Focused Management, the Journal of Marketing Management, the Journal of Strategic Marketing, the Asia Pacific Journal of Marketing and Logistics, and the Journal of Services Marketing. His current research is focused on understanding consumer behavior in a virtual context, and the factors which affect the migration from traditional to online forms of media and retailing.


the impact of is-marketing alignment on marketing performance and business performance

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