THE PERFORMANCE CONSEQUENCES OF EXPORT
MARKET-ORIENTED BEHAVIORS:
EVIDENCE ON MODERATOR VARIABLES USING CROSS-NATIONAL DATA

 

by

John W. Cadogan
Aston Business School, Aston University
Birmingham, B4 7ET, Great Britain

Tel: +44 (0)121-359-3011 x 5035
Fax: +44 (0)121-333-4313

e-mail: j.w.cadogan@aston.ac.uk

 

Sanna Sundqvist
Section of International Operations
Department of Industrial Engineering and Management
Lappeenranta University of Technology
PO Box 20, FIN-53851
Lappeenranta, Finland

Tel: +358-5-621-2610
Fax: +358-5-621-2644

e-mail: sanna.sundqvist@lut.fi

 

Risto T. Salminen
Section of International Operations
Department of Industrial Engineering and Management
Lappeenranta University of Technology
PO Box 20, FIN-53851
Lappeenranta, Finland

Tel: +358-5-621-2645
Fax: +358-5-621-2644

e-mail: risto.salminen@lut.fi

 

and

Kaisu Puumalainen
Section of International Operations
Department of Industrial Engineering and Management
Lappeenranta University of Technology
PO Box 20, FIN-53851
Lappeenranta, Finland

Tel: +358-5-621-2614
Fax: +358-5-621-2644

e-mail: kaisu.puumalainen@lut.fi

 

Our thanks to Nikki Paul for help with data collection.

 

 

ABSTRACT

Since the 1980's market orientation research has become an established field of study within the marketing domain and many different focus areas have risen. The research in the area has moved from the studies discussing the appropriate conceptual domain of the marketing concept to the research identifying antecedents to market orientation and the studies attempting to confirm market orientation’s positive association with business performance. Because the majority of previous studies have concentrated only on domestic settings, the universal functionality of the market orientation concept is yet to be verified. The present study extends the concept into a international business context by concentrating, particularly on the connection between an export market orientation and export performance.

Some of the especially interesting subjects, which have woken the researchers' interest recently, have been the dependence between the market orientation and company performance, and the possible moderator role of the environment. However, the results of the studies of the area are partly conflicting, which may be attributable to their failure to adopt methodologically and analytically rigorous approaches in their efforts to identify moderator variables. Hence, the objective of present study was twofold. First, it was needed to provide evidence of the relationship between a firm’s export market-oriented behaviors and its export performance. Second, the investigation whether the (potential) relationship between export market-oriented behavior and export success is moderated by the environment in which firms operate was required.

The present study has taken the lacks of earlier studies into consideration by applying hierarchical moderator analysis to capture a better understanding of the effects of the hypothesized moderators, and using large enough sample sizes to ensure the sufficient power of tests. On the basis of these very large data sets collected from New Zealand and Finland (292 and 783 respectively) present study confirmed the positive relationship between export market-oriented behaviors and the various important dimensions of export performance. Our study also indicated that several aspects of the environment do moderate the relationship between export market-oriented behaviours and export performance.

To facilitate our discussion of the potential consequences of export market-oriented behaviors, we have provided a definition of the construct. Following this we have described the possible impact of export market-oriented behavior on export performance, and highlighted potential weaknesses in the empirical literature which need to be addressed. As a result of this discussion, we also specified formal hypotheses. Our methodology and the analyses employed to test the hypotheses are then described, followed by the discussion of our findings. Finally, we have drawn conclusions, highlighted the study’s limitations and indicated several directions for future research.

 

THE PERFORMANCE CONSEQUENCES OF EXPORT
MARKET-ORIENTED BEHAVIORS:
EVIDENCE ON MODERATOR VARIABLES USING CROSS-NATIONAL DATA

INTRODUCTION

Over the last decade academic interest in the market orientation phenomenon has been escalating and, as a consequence, market orientation research has developed into a distinct and major field of study. This can be seen clearly when one examines the history and publication frequency of market orientation studies. During the 1980s, market orientation research was very much in its embryonic form. As such, most research in the area was limited to studies discussing the appropriate conceptual domain of the marketing concept (the latter being the philosophical tenet underlying market orientation – see Hunt and Morgan 1995). However, in the early 1990s, seminal contributions by Narver and Slater (1990) and Kohli and Jaworski (1990) moved the field forward considerably, providing a platform from which a "plethora of subsequent studies" (Harris and Ogbonna 1999, p 179) arose. As the decade draws to a close, it can be seen that most of the major marketing journals now regularly devote space to conceptual and empirical papers dealing with market orientation and many mainstream marketing conferences now offer a separate market orientation track. A second observation perhaps confirms market orientation’s importance as a field of study in its own right. Specifically, 1996 saw the launch of the Journal of Market Focused Management, a journal which explicitly devotes itself to the publication of research on issues pertaining to market orientation. It is, according to its editor, a multi-disciplinary, single theme journal (see Grover, 1996)!

Without a doubt, numerous contributions have arisen as a result of this proliferation of market orientation research. In particular, studies in the early part of the 1990s focused on (a) defining market orientation, (b) developing measures to operationalize the construct, (c) identifying antecedents to market orientation, and (d) attempting to confirm market orientation’s positive association with business performance. The latter was crucial since marketers had been preaching the benefits of adopting a market orientation for decades with little by way of empirical support for their claims (Lusch and Laczniak, 1987). The initial research findings were encouraging, since firms scoring high in terms of market-oriented behaviors also outperformed their less market-oriented counterparts (e.g., Narver and Slater, 1990; Ruekert, 1992). Importantly, the results of two major and highly visible studies also appeared to demonstrate that the market orientation – performance link holds regardless of the environmental conditions under which firms operate (Jaworski and Kohli, 1993; Slater and Narver, 1994). As a result, the message was strongly promoted (and readily accepted by scholars) that a market orientation positively influences business performance, regardless of environmental conditions; consequently, all firms should strive to maximize their market-oriented behaviors (see Slater and Narver, 1994).

Content with findings that supported the link between market-oriented behavior and business success, marketing scholars have broadened their research interests. As a result, recent articles have addressed a variety of themes with market orientation as a central construct. These have included, for example, studies which (a) attempt to identify factors which foster market-oriented behaviors (Bhuian, 1998), (b) question whether the development of a market-oriented organizational culture is possible (Harris and Ogbonna, 1999), (c) examine the relationship between organizational strategy and market orientation (Lukas, 1999; Morgan and Strong, 1998), (d) introduce a systems-based perspective to the market orientation literature (Becker and Homburg, 1999), (e) integrate market orientation into the relationship marketing literature (Baker et al., 1999), and (f) explore market orientation’s role in organizational learning (Sinkula et al., 1997; Hurley and Hult, 1998).

However, against this background of continuing knowledge creation, doubts still linger concerning the universal positive influence of market-oriented behaviors. First, most research to date has had a domestic marketing focus: researchers have yet to determine whether firms that are market-oriented in their exporting operations experience greater export success than their less ‘export market-oriented’ counterparts. Yet, as we will discuss later, it is by no means given that a positive relationship between export market-oriented behaviors and export performance holds. Second, the role of the environment as a moderator of the market orientation – performance link has yet to be resolved to the satisfaction of all. In particular, key studies which have failed to identify moderator variables can be criticized for either focusing on too few performance dimensions (i.e., implying that market-oriented behaviors affect all aspect of performance in the same way), failing to generate sufficient variance on the moderator variables of interest (i.e., measures of the environment), and/or leaving themselves undefended against charges of low power.

The objective of this study, therefore, is twofold. First, we provide evidence of the relationship between a firm’s export market-oriented behaviors and its export performance. Second, we investigate whether the (potential) relationship between export market-oriented behavior and export success is moderated by the environment in which firms operate. In the next section, to facilitate our discussion of the potential consequences of export market-oriented behaviors, we provide a definition of the construct. Following this we describe the possible impact of export market-oriented behavior on export performance, and highlight potential weaknesses in the empirical literature which need to be addressed. As a result of this discussion, we also specify formal hypotheses. We then describe our methodology, the analysis employed to test the hypotheses, and discuss our findings. Finally, we draw conclusions, highlight the study’s limitations and indicate several directions for future research.

EXPORT MARKET-ORIENTED BEHAVIOR

In order to facilitate subsequent discussions, a definition of export market-oriented behavior is required. Although there are numerous different conceptualizations of the market orientation construct available, we adopt a variation on two of the most widely utilized definitions of the construct, namely those of Narver and Slater (1990) and Kohli and Jaworski (1990). While these two behavior-based approaches have been shown to overlap considerably (Slater and Narver, 1994), they also contain unique perspectives (Cadogan and Diamantopoulos 1995). Consequently, we utilize Cadogan et al.’s (1999) grounded integration of Narver and Slater (1990) and Kohli and Jaworski’s (1990) models since it ensures that we overcome any shortcomings encountered by selecting one of these latter approaches over the other. Furthermore, Cadogan et al.’s (1999) approach has also been shown to be applicable in an exporting context (see also Diamantopoulos and Cadogan 1996), and as a result, issues concerning conceptual and functional equivalence (on moving from a domestic marketing context to an export marketing context) have already been dealt with.

Briefly, before specifying the content of export market-oriented behaviors, it is worth noting that the definition of the construct we adopt is behavior-based, not culture-based. Clearly, there are good reasons for examining the cultural underpinnings of a market orientation. However, it is generally accepted that cultural-based aspects of market orientation are best treated as antecedents to a firm’s market-oriented behaviors (e.g. Bisp, 1999; DeshpandJ and Farley, 1998; Hunt and Morgan, 1995). As such, positing a direct impact of market-oriented culture on performance is questionable – see Kahn (1998) and Siguaw et al. (1998) for empirical support of this notion. As a result, Cadogan et al.’s (1999) conceptualization of export market-oriented behavior is free of cultural underpinnings (the latter are placed firmly within the conceptual domain of a separate construct labeled the coordinating mechanism - for detailed discussions see Cadogan and Diamantopoulos (1995), Diamantopoulos and Cadogan (1996) and Cadogan et al. (1999)).

In brief, export market-oriented behaviors consists of three highly related but qualitatively distinct sets of activities; export intelligence generation, export intelligence dissemination, and export market responsiveness. Cadogan et al. (1999) state that the conceptual domain of export intelligence generation includes all activities which constitute the creation of export market intelligence (such as undertaking export market research, obtaining export assistance, generating export information from other sources) and which are focused towards a) export customers, b) export competitors, or c) the environmental changes affecting the firm, its customers and its competitors. Similarly, the export intelligence dissemination concept includes all activities which constitute the distribution of the export market information generated. Again, the content of the information that is disseminated should relate to a) export customers, b) export competitors, or c) the environmental changes affecting the firm, its customers and its competitors. Finally, export intelligence responsiveness includes the design and implementation of all responses to the export intelligence which has been generated and disseminated. These responses must also be directed towards a) export customers, b) export competitors, or c) the environmental changes affecting the firm, its customers and its competitors. Clearly, it can seen that the construct of export market-oriented behavior as defined above reflects the customer and market-driven foundations of the marketing concept, with a strong implementation aspect pervading. With this definition in mind, we now turn our attention to the issue at hand. What are the consequences of generating market-focused export intelligence, disseminating it and responding to it (i.e., of being export market-oriented)?

THE CONSEQUENCES OF EXPORT MARKET-ORIENTED BEHAVIOR

Given that market-oriented behavior is generally accepted as the implementation of the marketing concept (Kohli and Jaworski, 1990), there is a clear theoretical rationale for a positive association between market-oriented behavior and business performance. As Dickinson et al. (1986, p. 18) note: the "foundation stone [of the marketing concept] is customer satisfaction, the belief that a business, if it is to be successful, should be oriented towards satisfying the needs of its customers... The concept makes good sense. If the buyer is rational, it follows, seemingly as a truism, that he or she will choose and come to prefer those firms whose market offerings best meets wants" (Dickinson et al. 1986, p.18). Consequently, firms that are market-oriented in their activities will offer superior products and services to customers as a result of constant monitoring and response to emerging needs and developments in the business environment. Thus, market orientation "creates the necessary behaviors for the creation of superior value for buyers and, thus, continuous superior performance for the business" (Narver and Slater 1990, p. 21).

Does this positive association between market-oriented behaviors and performance hold across all business situations? This is certainly the opinion that prevails in most marketing text books. Few of the latter would argue that there are situations in which being market-oriented may be detrimental to firms. Certainly, much cited empirical findings of the two pioneer research teams in market orientation (Jaworski and Kohli, 1993; Narver and Slater, 1990; Slater and Narver, 1994) provided strong support for the notion that market-oriented behaviors influence performance in a positive manner under all environmental conditions. However, some uncertainty still remains concerning the universal benefits of a market orientation. There are two main reasons for this.

First, the majority of the research into market orientation has been undertaken within the context of firms’ domestic markets, while little by way of research has been undertaken in the context of firms’ international operations (Cadogan and Diamantopoulos, 1998). Yet, on reflection, it is likely that firms will find it harder, and certainly more costly, to be market-oriented in their foreign market operations relative to their domestic market operations (c.f., Cadogan and Diamantopoulos, 1995). Focusing on exporters, it is well documented that managers experience "greater environmental impact, greater perceived environmental uncertainty, and greater decision making uncertainty in their export channels, compared to their domestic channels" (Raven et al. 1994, p. 50). This heightened environmental turbulence is compounded by the often vast difference in information environments that export managers operate within relative to their domestic counterparts. For instance, in addition to the fact that exporting firms experience increased information requirements as a result of their foreign market activities (Belich and Dubinsky, 1995; Darling and Postnikoff, 1985; Douglas et al., 1982; Grr nhaug and Graham, 1987), the environments in which they operate are often typified by export marketing information which is of poor quality, is inaccessible, or is not available (Craig and Douglas, 2000; Loudon, 1975; Seely and Iglarsh, 1983). Consequently, the costs associated with being market-oriented in a firm’s export operations are likely to be significantly higher than those associated with being market-oriented in its domestic market. This leads to the question: does the positive relationship between market orientation and performance still hold when it is the association between a firm’s market-oriented behaviors in its export operations and its export performance which are being examined? Clearly, conclusions regarding the universal benefits of a market orientation should be reserved until this question has been answered.

As second issue which raises doubts about the market orientation – performance linkage comes from an examination of a diverse set of research findings which have mostly been ignored by market orientation researchers. Specifically, several empirical studies have provided evidence to suggest that under certain environmental conditions market orientation may not be as beneficial for performance as has been credited in the literature (Appiah-Adu, 1997, 1998; Atuahene-Gima, 1995; Diamantopoulos and Hart, 1993; Greenley 1995). Several reasons have been proposed for the observed moderating role of the environment on the market orientation – performance linkage. For instance, when firms are operating under condition of extreme volatility and complexity in their customer, competitor technological and regulatory environments, monitoring every change that occurs will be very costly in terms of human and financial resources. Furthermore, the dissemination of vast quantities of information is likely to be overwhelming and to create blockages and overload in various parts of the dissemination infrastructure (c.f., Cadogan and Diamantopoulos, 1995). Moreover, even if the firm does manage to generate and disseminate information on all the environmental fluctuations occurring, responding to all these changes may strain the firm’s resources. For example, if customer preference patterns are changing very rapidly, the costs associated with modifying products to suit every customer whim may exceed the benefit gained from such responses. Alternatively, rapid changes in technology may mean that investments in production facilities and product alterations become redundant before a firm can recoup its costs.

Unfortunately, a problem with the body of research into moderators of the market orientation – performance linkage is that some of the study findings are contradictory. For example, Greenley’s (1995) results suggested that as the technological environments in which firms operate increase in complexity and dynamism, the relationship between market-orientation and new product performance decreases in strength (and actually becomes negative with high levels of technological turbulence). On the other hand, Atuahene-Gima’s (1995) study found just the opposite. Here, Atuahene-Gima argued that his results demonstrate that market orientation’s relationship between new product success is more positive under conditions of high technological environmental turbulence.

These contradictory results, together with studies which have failed to uncover moderator effects, need to be explained. In this respect, four methodology issues may help us understand why these apparently equivocal results have occurred, and what is actually happening in terms of the market orientation – performance linkage.

First, most of the previously discussed studies used multiple (i.e., more than two) performance indicators to draw inferences. However, an issue here is that they did not all use the same performance indicators. This raises the possibility that market orientation does not influence all aspects of performance in the same way. If market orientation is related to different performance dimensions in different ways, this could account for some of the equivocal findings. Consequently, research is needed which examines the relationship between market orientation and a wide array of performance dimensions in order to enhance the likelihood that different patterns of association can be identified.

Second, those who would argue that market-oriented behavior is always a good thing base their argument on the findings of Slater and Narver (1994) and Jaworski and Kohli (1993), both of whom report that their empirical findings failed to find evidence of moderator effects. However, Slater and Narver’s (1994) lack of support for the environment as a moderator of the market orientation – performance linkage may be partially attributable to low variance on key variables of interest. Specifically, Slater and Narver’s (1994) study was undertaken using samples of strategic business units (SBUs) from only two companies. Yet as Slater (1995) notes himself, the generalizability of this kind of study must be viewed with skepticism. In particular, by restricting the samples to two companies, the key variables of interest (i.e., the environment variables) were greatly restricted in the degree to which they could vary! Yet it is clear that in order to make truly informed comment on the influence of situational variables on the market orientation – performance relationship, one needs to sample from the full range of environmental turbulence values. Here, at the very least, a multi-industry study would be in order (Slater, 1995). Furthermore, to add power to the analysis, it would make sense to ensure that firms operating under a wide variety of environmental conditions are sampled, from very low to very high turbulence.

A third point concerns the type of analyses undertaken. Researchers often adopt multiple regression procedures to detect interactions in marketing research (Ping, 1996) and the market orientation field is no exception. However, there are different ways of using regression to identify moderators and, as Evans (1991) points out, the most appropriate method is hierarchical multiple regression analysis. This can be followed by split-group analysis for the identification of homologizer effects only after the hypothesized moderator has been ruled out as being related to the criterion or predictor variable or as a pure or quasi moderator (Sharma et al., 1981). Consequently, researchers relying only on split group analysis (i.e., not using hierarchical moderated regression) for the identification of interactions (e.g., Atuahene-Gima, 1995; Jaworski and Kohli, 1993), do so at the risk of wrongly identifying moderator variables. In the case of Atuahene-Gima (1995), for example, the identified moderator effects may not be moderators after all: as Sharma et al. (1981, p. 294-295) note, the "R2 will vary between segments or subgroups, leading one to conclude that the variable used for subgrouping is a moderator regardless of whether it is (1) a homologizer, (2) related to either the criterion or predictor variable, (3) a pure moderator, or (4) a quasi moderator variable." It is possible, then, that the contradictory empirical results of Atuahene-Gima (1995) and Greenley (1995) may be partially explained by the different approaches used in their analysis. Interestingly, although Greenley (1995) was correct in using moderated regression analysis, a hierarchical procedure was not used and as a result, there was no protection against large stepwise Type I error rates (Cohen and Cohen 1983, p. 173). As a consequence, our confidence that the various significant interaction terms observed were not due to Type I error is low. For example, with 13 variables in the regression equation, and assuming independence of these variables, a "ballpark" estimate of the stepwise error rate for the 13 t tests, each at " = .05, would be in the vicinity of 1 – .9513 = .49!

A final potential reason for the observed equivocal results in terms of the moderators of the market orientation – performance relationship concerns sample size. In particular, we argue that the sample sizes typically used for model testing have not been sufficient to generate adequate power in the analysis. This point brings into question the lack of support for moderators reported Slater and Narver (1994) and Jaworski and Kohli (1993), as well as other research findings. Specifically, researchers have tended to use relatively ‘small’ sample sizes (less than 250 responses) to test for moderator variables. For example, Appiah-Adu (1998) attempted to identify moderator variables using responses from 74 firms, Slater and Narver (1994) used a sample representing responses from 107 SBUs, Jaworski and Kohli (1993) used samples representing responses from 222 and 230 SBUs, and Greenley (1995) used a sample representing responses from 240 companies. Under normal model testing conditions (i.e., when one is not attempting to identify interaction terms), such sample sizes are usually adequate for model testing purposes (Slater, 1995). However, as Evans (1991) has noted, the identification of moderator effects using hierarchical regression procedures makes huge demands on sample size if adequate power is to be achieved. There are three reasons for this. First, power is reduced simply because the number of variables entered into the regression equation increases dramatically upon the creation of interaction terms1. Second, when conducting moderated regression analysis, interaction terms are created by multiplying two variables (x and y) together. Consequently, power is further reduced since the reliability of the resulting xy interaction term is roughly equal to the product of the reliabilities of the x and y variables (Jaccard and Wan, 1995; Lubinski and Humphreys, 1990). Thus, if the x and y variables each have a reliability of .70, the xy variable will have a reliability in the region of .49. Third, when testing for interaction terms, it is likely that the effect sizes of the moderator variables are relatively small and this directly reduces the power of the analysis.

Such threats to power were explicitly recognized by Jaworski and Kohli (1993), who acknowledged that their lack of support for moderators of the market orientation – performance relationship could have been due to a lack of power. Few other researchers have made comment on power, and one assumes that they have not considered it to be an issue. Yet some simple calculations show otherwise. For example, Slater and Narver’s (1994) regression analysis failed to uncover any significant interaction terms. However, using the data provided in their paper, the power of their analysis can be estimated. We use sales growth as the dependent variable to illustrate. Under the assumption that the hypothesized moderators will contribute approximately 10% of the total variance explained when added to the regression equation (i.e., contribute 10% to the final R-squared value returned), and using simple formula provided by Cohen and Cohen (1983), we estimate that Slater and Narver’s (1994) regression had a power of less than .40. In other words, we suggest that there was a probability of less than 40% that Slater and Narver’s study would have identified significant interaction terms in the regression analysis has they existed. Even in the social sciences, such low power is generally considered unacceptable. A second simple calculation also suggests that to achieve a more acceptable power level of .95, Slater and Narver would have needed to obtain a sample size well in excess of 400 responses. Similar calculations can be undertaken for Greenley’s (1995) study: here, we estimate the power to be less than .30, and a sample size well in excess of 1000 would have been needed to ensure a power of .95. Interestingly, Greenley’s (1995) study is in the somewhat paradoxical situation where the significant interaction terms may have been observed due to inflated Type I error, while the non-significant interaction terms may be due to very low power!

Clearly, then, the evidence to date on the existence of moderator variables is in some disarray, mainly due to the methodologies and analysis techniques used to identify moderator variables. It is also clear that more work is needed to shed light on the issue of the moderators of the market orientation – performance linkage. In particular, we suggest that rather more attention needs to be paid to power considerations – in this respect it is essential that evidence be generated from a larger sample than those which have been reported in the literature to date. It is also the case that hierarchical regression procedures should be undertaken to minimize stepwise Type I error (Cohen and Cohen, 1983; Lubinski and Humphreys, 1990). We also stress the importance of obtaining sufficient variance on the variables of interest and, consequently, advocate multi-industry examinations to test for moderators. Finally, a wider range of performance dimensions should be examined for moderator effects.

Therefore, in order to follow our own recommendations, we formally specify two hypotheses to be tested:

H1: Export market-oriented behavior is positively associated with export performance.

H2: The relationship between export market-oriented behavior and export performance is moderated by turbulence in the export environment.

The potential contribution of the study is twofold. In providing support for the hypotheses we would be contributing to our understanding of export performance. The latter is an important literature stream in its own right, and is gradually gaining in maturity as a field of research (Zou and Stan, 1998). We will also provide deeper insights into the market orientation phenomenon. In particular, concerning the environment as a moderator of the market orientation – performance link, our results will have a greater degree of confidence attached to them than has previously been the case. For example, we test for moderating influences using a large number of performance indicators in order to see whether moderators exist for certain types of performance dimension, and not for others. Also, by testing for moderators using a cross section of exporting firms, we hope to generate sufficient variation in the environment measures to ensure that extremes in environmental turbulence are sampled. Finally, we provide evidence on moderator effects using two national samples, one of which produced nearly 300 responses, the other nearly 800 responses; as a result, and especially in the latter sample, we hope previous studies’ weaknesses in terms of power issues are overcome. We now describe the research methodology.

METHODOLOGY

Data Collection

To provide insights into the hypotheses, we utilized data obtained from national mail surveys undertaken in New Zealand and Finland. In developing the research instruments care was taken to ensure that equivalence issues were considered. In particular, special attention was paid to conceptual, functional, measure, sample and data collection equivalence issues (e.g., Cavusgil and Das 1997; Samiee and Athanassiou 1998). In terms of the data collection process itself, Profile Direct’s entire listing of New Zealand exporting firms with 50 or more employees was used, as was Kompass Finland’s entire database of exporting firms with 50 or more employees. Each firm was contacted by telephone to (a) determine eligibility and (b) elicit cooperation in the study. Following this, a questionnaire was sent to the person in charge of export operations for the firm. One week (ten days in the Finnish sample) after the initial mailing, a reminder card was sent to each non-respondent. A further seven days after the reminder cards were mailed, a second questionnaire was mailed to non-respondents together with a cover letter.

Of the original 853 New Zealand firms, only 415 proved eligible (the remaining 438 company names provided on the database were found to be ineligible since, for example, the firm had never exported, the firm no longer exported, the firm was listed more than once). In total, useable responses from 292 New Zealand exporting firms were obtained, corresponding to a response rate of 70% (i.e., 292/415). Of the contacts listed on the Finnish database, only 237 of the original 1205 firms proved to be ineligible, leaving a total of 968 eligible contacts. A total of 783 Finnish exporting firms provided usable responses to the questionnaire, corresponding to a response rate of 81% (i.e., 783/968). A comparison of early and late respondents on all variables of interest uncovered no significant differences and consequently, there was no evidence to suggest that non-response bias was a problem in either sample (Armstrong and Overton 1977).

Measures

EMO behavior was measured using Cadogan et al.’s (1999) measure. The measure consists of an 11-item measure of export intelligence generation, an 18-item measure of dissemination and a 17-item measure of responsiveness. All items were measured on seven-point scales, Score for each of these sub-components were then calculated, by averaging each of their item scores. Finally, and following Cadogan et al. (1999), a score for EMO behavior was created by summing these three average scores.

On the advice of Cavusgil and Zou (1994) and Matthyssens and Pauwels (1996) among others, multiple approaches were used to capture various aspects of the multi-faceted export performance construct. Specifically, 15 different dimensions of export performance were captured. These included objective and subjective measures of export sales performance, export growth, and export profitability, as well as a general assessment of export performance. Sales Performance. To indicate firms’ absolute sales performance, we asked respondents to provide the percentage of sales obtained from exporting; we also calculated the absolute export sales per company employee, and the total export sales per country exported to (Cadogan and Diamantopoulos 1998). Satisfaction. Managers’ levels of satisfaction with their firms’ export performance was also captured on four independent dimensions corresponding to the following potential export objectives; satisfaction with export sales volume, export market share, export profitability, and export market entry. Ten-point scales were used to capture satisfaction with each of these objectives (from 1 = very dissatisfied to 10 = very satisfied) (c.f., Cavusgil and Zou, 1994). A weighted satisfaction measure was also created by asking managers to rate the importance of each of these export objectives for the firm. A constant sum scale, adding up to a total of 100 points, was used for this rating process. The importance rating for each export objective was then multiplied by the corresponding ten-point scales score for each objective. Cadogan and Diamantopoulos’ (1998) algorithm was used as the basis to calculate a single weighted satisfaction measure. Growth. Absolute growth in sales volume over the previous 3 year period was captured as a simple average percentage. However, in order to control for industry effects, we also asked respondents to rate their average annual growth in export sales relative to the industry average on a ten-point scale ranging from 1 = poor to 10 = outstanding. Profitability. Respondents were asked to rate the profitability of the company’s export operations for each of the last three years on a ten-point scale (ranging from 1 = poor to 10 = outstanding). The average relative growth in the profitability of the firm’s export operations over the last three years was determined by calculating the difference in relative profitability between year t and year t-2. Overall Performance Assessment. Finally, a single item indicator was used to capture manager’s overall global assessment of the firm’s export performance. Respondents were asked to rate their firms export performance over the last three years, ranging from 1 = poor to 10 = outstanding.

Several variables were identified from the literature as having a potential impact on performance but not as having moderator effects (see Jaworski and Kohli, 1993; Slater and Narver, 1994). These control variables included (measured on a single-item seven-point scale except where stated) the relative size of the business compared to that of the firm’s largest competitor in its export markets; relative average total operating cost of the business compared to that of the firm’s largest competitor in its principal export market segment; ease of market entry, measuring the likelihood that a new entrant in the firm’s main export markets could earn satisfactory profits; and export product quality, measured on a three-item, seven-point scale (Menon et al., 1997 ).

Finally, several variables were identified which may moderate the export market-oriented behavior – export performance link (Jaworski and Kohli, 1993; Slater and Narver, 1994). In particular, we included a measure of buyer power, the extent to which export customers can negotiate lower prices: this was measured on a single item seven-point scale (all remaining measures were based on a seven-point scale) Market turbulence, a measure of changes in customers’ preferences and their composition was measured using Jaworski and Kohli’s (1993) five-item market turbulence measure. The competitive turbulence scale captured the intensity and adaptability of the firm to competitors in the environment, and was based on Jaworski and Kohli’s (1993) six-item scale. The technological turbulence scales captured the degree to which technology within an export market was in a state of flux, and was measured using four items from Jaworski and Kohli’s (1993) five-item scale. Finally, the regulatory turbulence scale capture the degree to which regulations within the firm’s export markets compromised business activities, and was measured using Dwyer and Welsh’s (1985) environmental forces scale.

ANALYSIS AND RESULTS

The reliability of measures was assessed using coefficient alpha. All multi-item scales returned coefficients in excess of .70. In order to provide evidence on the hypotheses, four regression equations were constructed in a hierarchical fashion (Sharma et al., 1981), for each of the export performance dimensions, as follows:

(1) PERFi = CVs

  1. CVs + EMO
  2. CVs + EMO + MVs
  3. CVs + EMO + MVs + ITs

where, PERFi = the performance dimension i; CVs = the entire set of control variables; EMO = the export market-oriented behavior score; MVs = the entire set of hypothesized moderator variables, and; ITs = the entire set of interaction terms obtained by multiplying each of the MVs by EMO. Prior to running the regressions, all variables for which main interaction effects were to be estimated were mean centered to reduce the risk of mulicollinearity (Cronbach, 1987). Clearly, support for H1 will occur if the R-squared change on moving from equation (1) to equation (2) is significant; similarly, support for H2 will occur if the R-squared change on moving from equation (3) to equation (4) is significant (see Cohen and Cohen 1983; Lubinski and Humphreys, 1990). Table 1 reports the results for equation (2). Table 2 reports the results for equation (4) (only the coefficients for the export market-oriented behavior and the interaction terms are reported). Following Sharma et al. (1981), for each sample, when a hypothesized variable did not return a significant interaction term for a performance indicator: (a) its relation with that performance indicator was assessed - if the relationship was significant, the hypothesized moderator was not tested for homologizer effects; and (b) its relation with export market-oriented behavior was assessed – if the relationship was significant, again the hypothesized moderator was not tested for homologizer effects. All remaining hypothesized moderator variables, however, were tested for homologizer effects. To do this, and using buyer power and overall export performance in the New Zealand sample as an example, the sample was split into quartiles on the buyer power variable. The partial correlation coefficient between export market-oriented behavior and overall export performance was then calculated separately for the upper and lower quartiles of the buyer power variable, and the significance of the difference in partial correlation coefficients between the two quartiles was determined using Fisher’s z-test (Arnold, 1982). Significant differences in partial correlation coefficients would indicate that the hypothesized moderator is a homologizer variable (c.f., Slater and Narver, 1994). Table 3 provides the findings of the homologizer analysis.

Table 1: OLS Standardized regression coefficients:
main effects of control variables and market-oriented behavior with significance of R-squared increase

New Zealand b

Finland b

Export performance dimension

) R2

Size

Cost

Entry

Qual

EMO

) R2

Size

Cost

Entry

Qual

EMO

Percentage of sales from exporting

.10**

.21**

-.08

.01

-.05

.36**

.06**

.24**

.12**

-.09**

.08*

.27**

Export sales per employee - US$ a

.12**

.20**

-.04

.06

-.13*

.39**

.06**

.25**

.04

-.10**

-.09*

.27**

Export sales per country exported to - US$ a

.08**

.12

.03

.00

-.15*

.31**

.03**

.24**

.05

-.06

-.12**

.21**

Satisfaction with export sales volume

.05**

.24**

-.12*

-.10

.09

.26**

.03**

.35**

-.09*

.04

.02

.20**

Satisfaction with export market share

.02*

.30**

-.13*

-.14*

.16**

.15*

.03**

.37**

-.05

.01

.03

.19**

Satisfaction with export profitability

.01

.20**

-.08

.09

.13*

.09

.03**

.19**

-.18**

.06

.10**

.18**

Satisfaction with new market entry

.03**

.09

-.15*

-.08

.12

.19**

.03**

.18**

-.01

.01

.02

.20**

Overall weighted satisfaction score

.02**

.263**

-.19**

-.08

.18**

.16**

.03**

.327**

-.17**

.04

.08*

.21**

Absolute growth in export sales a

.04**

.04

-.09

.04

.10

.23**

.00

.15**

-.12**

.13**

.05

.06

Growth in export sales relative to industry norm

.06**

.29**

-.10

.02

.17**

.27**

.04**

.22**

-.04

.07*

.10**

.23**

Profitability of export operations (year t-2)

.00

.26**

.03

.06

.05

-.02

.01**

.13**

-.14**

-.03

.05

.13**

Profitability of export operations (year t-1)

.00

.26**

.05

.06

.01

.07

.02**

.21**

-.16**

.02

.03

.17**

Profitability of export operations (year t)

.02*

.20**

-.04

.14*

.11

.15*

.04**

.21**

-.16**

.06

.05

.23**

Average profitability of last 3 years

.01

.30**

.01

.10

.07

.08

.03**

.22**

-.19**

.02

.05

.21**

Growth in export profitability

.01*

-.05

-.07

.06

.05

.13*

.00

.04

.01

.07

-.00

.06

Overall export performance

.16**

.29*

-.14**

-.01

.10

.45**

.08**

.30**

-.12**

.06

.06

.32**

a: Log transformations were required because of deviations from normality.

b: Size = relative size; Cost = relative operating costs; Entry = ease of market entry; Qual = export product quality; EMO = export market-oriented behavior.

) R2 = change in R-squared associated with moving from equation (1) to equation (2)

* < .05; ** < .01



Table 2: OLS standardized regression coefficients:
export market-oriented behavior and interaction terms (significance of R-squared increase)

 

New Zealand b

Finland b

Performance dimension

) R2

EMO

BPI

MDI

CII

TTI

RTI

) R2

EMO

BPI

MDI

CII

TTI

RTI

Percentage of sales from exporting

.02

.35**

-.06

.06

-.07

.02

-.10

.01

.26**

.01

-.04

-.02

.06

-.05

Export sales per employee - US$ a

.03

.38**

-.15*

.06

-.01

-.07

.01

.01

.25**

-.05

-.04

-.01

-.02

-.03

Export sales per country exported to - US$ a

.04

.27**

-.21**

.09

.08

-.01

-.07

.00

.16**

-.03

-.03

.05

-.01

.01

Satisfaction with export sales volume

.02

.27**

.08

.09

-.17*

-.01

-.03

.01

.21**

-.05

-.02

.06

.03

-.07

Satisfaction with export market share

.03*

.20**

.10

.04

-.20**

.07

.00

.01

.22**

.00

.03

.05

.03

-.06

Satisfaction with export profitability

.02

.15*

.10

.08

.16*

-.01

.00

.01

.21**

-.04

-.07

.03

.02

-.04

Satisfaction with new market entry

.02

.22**

-.3

.11

-.07

-.05

.06

.00

.22**

.04

-.03

.00

.01

-.01

Overall weighted satisfaction score

.02

.19**

.08

.08

-.17*

-.03

-.02

.01

.23**

-.04

-.06

.03

.03

-.05

Absolute growth in export sales a

.02

.20**

-.02

-.11

.02

-.08

-.01

.02*

.04

-.11**

-.09*

.02

.03

.03

Growth in export sales relative to industry norm

.01

.25**

-.06

.04

-.01

.04

.02

.01*

.23**

-.03

-.09*

.06

.10**

.00

Profitability of export operations (year t-2)

.01

.00

.01

.08

.00

-.05

.01

.02*

.13**

.08*

-.11**

.06

.02

.00

Profitability of export operations (year t-1)

.00

.09

-.01

.03

.00

-.02

-.03

.02*

.18**

.06

-.08*

-.02

.10*

-.03

Profitability of export operations (year t)

.01

.18**

.10

.04

-.05

-.03

.00

.01

.26**

.01

-.07

-.03

.07

.00

Average profitability over last 3 years

.01

.10

.04

.06

-.02

-.04

.00

.01

.22**

.06

-.10**

.01

.07

-.01

Growth in export profitability

.01

.14

.08

-.04

-.05

.02

.00

.02*

.07

-.07c

.05

-.08c

.04

.01

Overall export performance

.02

.45**

.07

.13*

-.13*

-.06

-.03

.00

.34**

.02

-.05

.03

.03

-.02

a: Log transformations were required because of deviations from normality.

b: ) R2 = change in R-squared value on moving from equation (3) to equation (4); EMO = export market-oriented behavior; BPI = buyer power x EMO interaction; MDI = market dynamism x EMO interaction; CII = competitor intensity x EMO interaction; TTI = technological turbulence x EMO interaction; RTI = regulatory turbulence x EMO interaction.

* < .05; ** < .01

c: the coefficients for these interaction terms were significant at p < .10.

 

Table 3: Homologizer analysis:
partial correlation coefficients for upper and lower quartiles of hypothesized moderator variables

Sample

New Zealand b

Finland b

Hypothesized Moderator

BPI

MDI

CII

TTI

RTI

BPI

MDI

CII

TTI

RTI

Lo

Hi

Lo

Hi

Lo

Hi

Lo

Hi

Lo

Hi

Lo

Hi

Lo

Hi

Lo

Hi

Lo

Hi

Lo

Hi

Performance dimension

Percentage of sales from exporting

.25

.05

.36*

.21

q

q

.46*

.16

.30*

.20

.36*

.24*

q

q

.25*

.17*

Export sales per employee - US$ a

.39*

.19

.43*

.26

q

q

.31*

.41*

.38*

.18

.31*

.27*

q

q

h

Export sales per country exported to - US$ a

.29

.30

.33

.31*

q

q

.37*

.18

.24*

.12

.22*

.16

q

q

.09

.17*

Satisfaction with export sales volume

.43*

.20

.39*

.34*

q

q

.32*

.25

.23*

.27*

.15

.16

q

q

.26*

.07

Satisfaction with export market share

.27

.12

.37*

.20

q

q

.11

.15

.20*

.27*

.09

.19*

q

q

h

Satisfaction with export profitability

.25

.43

.32*

.31*

q

q

.06

.21

.07

.21*

.26*

.14

q

q

.21*

.06

Satisfaction with new market entry

.26

.12

.29

.38*

q

q

-.06

.29*

.23*

.13

.22*

.16

q

q

.15

.12

Overall weighted satisfaction score

.39

.32

.42*

.36*

q

q

.19

.22

.12

.29*

.21*

.10

q

q

.26*

.08

Absolute growth in export sales a

.22

.22

.28

-.06

q

q

.25

.29*

m

m

q

q

.00

-.01

Growth in export sales relative to industry norm

h

h

q

q

.19

.31*

.11

-.02

m

q

q

.10

.22*

Profitability of export operations (year t-2)

.04

.25

-.12

.03

q

q

.01

-.04

m

m

q

q

.11

.04

Profitability of export operations (year t-1)

.25

.31

.08

.10

q

q

.10

.00

-.01

.25*

m

q

q

.10

-.04

Profitability of export operations (year t)

.22

.48*

.24

.42*

q

q

.10

.07

.15

.09

.37*

.27*

q

q

.11

.17*

Average profitability over last 3 years

.18

.41

.08

.22

q

q

.06

.01

.01

.28*

.36*

.19*

q

q

.13

.07

Growth in export profitability

.11

.28

.26

.28

q

q

.03

.08

m

.05

.12

q

q

-.04

.08

Overall export performance

.49*

.58*

.49

.56*

q

q

.48*

.46*

.25*

.33*

.35*

.31*

q

q

h

a: Log transformations were required because of deviations from normality.b: EMO = export market-oriented behavior; BPI = buyer power x EMO interaction; MDI = market dynamism x EMO interaction; CII = competitor intensity x EMO interaction; TTI = technological turbulence x EMO interaction; RTI = regulatory turbulence x EMO interaction.c: Lo = Low environmental turbulence (lower quartile); Hi = High environmental turbulence (upper quartile).z: the partial correlation coefficients in the low and high environmental turbulence environments differ significantly (two-tailed; p < .05).*: partial correlation significant at p < .05 (two-tailed)q = homologizer analysis not applicable since hypothesized moderator predicts export market-oriented behavior.m = homologizer analysis not applicable since hypothesized moderator returned pure / quasi moderator effects.h = homologizer analysis not applicable since hypothesized moderator predicts the export performance dimension.

 

As a result of the above, several main effects and moderator effects were identified. Regarding H1, table 1 shows that export market-oriented behavior contributed to explaining the variance of most performance variables, and in this regard, there is strong support for the notion that firms with higher levels of export market-oriented behavior outperform their less market-oriented counterpart.

Regarding the moderating role of the export environment on the export market-oriented behavior – export performance relationship, an examination of table 2 demonstrates that several moderating effects have been identified, thus providing some support for H2. As expected, given the large Finnish sample size relative to the New Zealand sample, the majority of significant effects were uncovered in the Finnish data. Before making inferences concerning the hypotheses, it was important to compare the power of the analysis in the two samples. To do this, the expected effect size (ES) in the population needed to be estimated. As previously mentioned, and based on results from previous studies, it can be inferred that the hypothesized moderators, when converted into interaction terms and added to the regression equation, will have a relatively ‘small’ impact on the proportion of variance explained. Cohen and Cohen (1983, p. 161) cautiously suggest that a value as low as .01 could be treated as being a small R-squared in the social sciences. However, for present purposes, we suggest that a simple rule of thumb is to calculate the power by assuming that the moderator variables of interest will contribute approximately 10% to the total variance explained in the population by the variables in the model (i.e., the hypothesized moderators will contribute 10% of the total R-squared returned from equation (4)). We further suggest that our variables should be able to capture at least 25% of the variance in the various performance dimensions (i.e., the population R-squared for equation (4) is approximately .25). Consequently, our estimated ES is .025 (.10 times .25). This figure is neither so large that it will inflate our power estimates, nor too small to be of substantive interest. Using Cohen and Cohen (1983), and adopting an " = .05, the power of each of the regression equations (for each sample and for each performance dimension within each sample) was estimated. For the New Zealand sample, the power returned was always in excess of .50, with most equations returning a power of around .60. For the Finnish sample, the power returned was always in excess of .95. Cohen and Cohen (1983) suggest that while a power of .50 or .60 is poor and potentially unacceptable, power in excess of .95 is highly desirable. Clearly, then, on the basis of this finding, the results from the Finnish data are more powerful, and should be used for making inferences concerning moderator variables rather than those resulting from the New Zealand data (c.f., Sawyer and Ball, 1981)2.

Looking briefly at the New Zealand data, the solitary significant moderator could simply be a result of Type I error arising from the multiple tests3. Given the very low power, not much better than 50/50, the lack of support for H2 is not very meaningful. Clearly, if we were to rely only on the New Zealand data to provide evidence on the moderating role of the export environment, we would reach conclusions very different from those we will make based on the results from the Finnish data. Concerning the latter, it is apparent that several aspects of the environment do moderate the relationship between export market-oriented behaviors and export performance, thus providing support for H2.

In terms of the homologizer analysis, no significant differences were observed in the magnitude of the correlation coefficients between export market-oriented behavior and export performance across environments characterized by low and high environmental (table 3). Once more, power calculations are needed to help make inferences about this finding. Using formula and tables provided by Cohen (1977), the power associated with the z-statistic used to test for homologizers was calculated4. As a consequence, it was seen that the power for the homologizer analysis was no greater than .20 and .50 in the New Zealand and Finnish samples respectively. This finding suggests that there is not really enough power to make inferences concerning the presence of homologizers in either sample (i.e., there was a greater than 50% chance in both samples that true differences in partial correlation coefficients, if they existed, would not be identified).

DISCUSSION AND IMPLICATIONS

There are a number of important managerial and theoretical implications to be gained from the findings of this study. First, it appears that there is indeed a positive association between export market-oriented behavior and export performance. Looking at table 1, it can be seen that export market-oriented behavior returned the largest standardized regression coefficient in eight and six of the regression equations for the New Zealand and Finnish samples respectively. This provides an indication of the importance of being market-oriented in the firm’s export activities. However, it can also be seen that export market-oriented behavior may not predict the profit aspects of export performance (especially in the New Zealand sample) as well as other export performance dimensions. In the New Zealand sample, the firm’s export profitability ratings for the financial years t-1 and t-2 were not predicted by export market-oriented behavior. One potential reason for this may be that the measure of export market-oriented behavior captures current behaviors, rather than capturing behaviors which have occurred two or three years ago. Consequently, one might argue that while a measure of the firm’s market orientation two or three years ago may predict profitability then, and perhaps even now (market-oriented behaviors are likely to take time to influence performance), why should a measure of the firm’s current export market-oriented behavior predict profitability two or three years ago? However, it is generally accepted that market-oriented behaviors do not change very much over time, and so we can be fairly confident that the measure of export market-oriented behavior provides an indication of the firm’s export market orientation over the last few years (c.f., Pelham and Wilson, 1996). Alternatively, the failure to predict these performance dimensions may be attributable to the presence of moderator variables, even though they were not detected in the New Zealand sample. The remaining export performance indicators which did not return a significant main effect for export market-oriented behavior (i.e., satisfaction with export profitability in the New Zealand sample, and growth in export sales and export profitability in the Finnish sample) did, however, return positive coefficients as predicted.

As indicated in the results section, since the power of the moderator analysis in the New Zealand sample was so poor, the lack of support for moderator variables in this samples looses meaning. Consequently, we concentrate on the findings from the Finnish sample. Here, power for the hierarchical regression procedure was adequate (although power was insufficient for the homologizer analysis). It can be seen that the significant moderator effects occurred only for the growth in sales, profitability and growth in profit measures. As indicated above, if the environment does moderate the relationship between export market-oriented behavior and the profitability of export operations as the findings suggest (for years t-1 and t-2), it could be that this played a role in the lack of support for main effect as seen in the New Zealand sample. What exactly do these significant moderator results and coefficients indicate? Using the now standard partial derivative procedure (see Greenley 1995), it can be shown that under certain environmental conditions, export market-oriented behaviors have a positive relationship with export profitability, export sales growth, and export profit growth, while under opposite environmental conditions a negative relationship exists. Table 4 summarizes the findings of this process.

Table 4: Summary of findings from the hierarchical moderated regression analysis

Environmental factor

Performance dimension and its relationship with export market-oriented behavior

Interpretation

 

 

Buyer

power

 

Low

Absolute growth in sales (+)

Profitability year t-2 (-)

Growth in profitability (+)

 

High levels of buyer power may force firms to focus on maintaining and servicing current customers. consequently, being market-oriented under conditions of high buyer power means that growth in both sales and profit margins cannot be achieved. However, a market-oriented strategy may eventually pay off in terms of profits.

High

Absolute growth in sales (-)

Profitability year t-2 (+)

Growth in profitability (-)

 

 

 

 

Market

dynamism

 

 

Low

Absolute growth in sales (+)

Relative growth in sales (+)

Profitability year t-2 (+)

Profitability year t-1 (+)

 

 

Paying too much attention to customer needs and wants actually leads to reduced sales growth and profitability. It may be that under conditions of very high market dynamism, in which customer preferences and wants are changing very rapidly, the costs associated with generating customer information and of responding to it in an appropriate fashion outweigh the benefits sought. Consequently, firms need to be aware that being over-sensitive to markets which are very turbulent is potentially dangerous. More research on long term profitability required.

 

High

Absolute growth in sales (-)

Relative growth in sales (-)

Profitability year t-2 (-)

Profitability year t-1 (-)

 

 

Technological turbulence

 

Low

Relative growth in sales (-)

Profitability year t-1 (-)

Market oriented-behaviors help organizations to take advantage of advances in technology and to survive in a turbulent technological environments. In particular, growth in sales relative to that of competitors and profitability is aided by closely monitoring the technological environment and responding accordingly. It is likely that new markets can be penetrated more easily as a result, and that niche markets can be protected.

High

Relative growth in sales (+)

Profitability year t-1 (+)

 

Competitive turbulence

Low

Growth in profitability (+)

Under conditions of extreme competitive hostility and turbulence, responding to competitor actions via decreasing prices may be the only short term viable strategy, but also appears to reduce profitability.

High

Growth in profitability (-)

 

From a substantive perspective, while there are possible situations in which export market-oriented behavior reduces performance in some way, these are often rare situations, requiring extremely large or small values of environmental turbulence which are unlikely to affect the majority of exporting organizations. Consequently, it seems that an appropriate recommendation would be to encourage firms to adopt market-oriented behaviors in their export activities. However, we do add some qualifications here. First, the environment does appear to be a moderator and exporters operating under conditions of extreme environmental turbulence may find that short term export profits and growth are hampered by behaving in a highly export market-oriented fashion, especially when market dynamism and buyer power are very high. Second, we have not explored the long term effects of export market-oriented behavior on export performance. It may well be that although short term export performance may suffer, in the longer term, the firm’s survival and success in it export markets is positively related to its ability and willingness to exhibit export market-oriented behaviors. Clearly, more research is needed here.

From a theoretical and methodological perspective, the current study has moved a step closer to a better understanding of the market orientation phenomenon. In this respect, the study has made two important contributions. First, it confirmed the positive relationship between export market-oriented behaviors and various important dimensions of export performance. The second contribution was the provision of a more powerful examination of the moderating role of the environment on the market orientation – performance linkage; this was required given the proliferation of studies which have failed to adopt methodologically and analytically rigorous approaches in their efforts to identify moderator variables. In confirming the moderating role of the export environment, we further add to our understanding of market orientation. Comparing our results with others reported in the literature, we see several differences. For example, we obtained findings which are opposite to those of Greenley (1995). Our results also partially negate the findings of Appiah-Adu (1998), although we concur with the impact of market-orientation on profitability when moderated by market dynamism. Furthermore, our study also is at odds with findings that have suggested that no variables moderate the market orientation performance linkage (e.g., Jaworski and Kohli, 1993; Slater and Narver, 1994). Given the above mentioned studies’ general lack of power and, in some cases, high probability of Type I error, we feel confident that our results represent reality reasonably closely. However, the issue of low power in the homologizer analysis requires additional work, and we discuss this below.

LIMITATIONS AND DIRECTIONS FOR FUTURE RESEARCH

An inevitable limitation of this study concerns the fact that the power estimates rely on an estimate of the population effect size (ES) – in our case, for the regression analysis we suggested that the true population effect size for the moderator variables may be in the region of .025. However, relatively small absolute changes in the estimated ES can make quite big differences to the power estimates. For example, if in fact the actual population ES for the moderator variables in the regressions is closer to .01 (what Cohen and Cohen (1983) describe as a ‘small’ but potentially interesting ES)5, the power of the New Zealand sample would decrease to well under .30 and would decrease in the Finnish sample to around .65! While the latter level of power is marginally acceptable, it could be much better. Furthermore, the power of our homologizer analysis appeared to be very low (despite some interesting looking differences in correlation coefficients in table 3). Thus, future researchers who wish to provide more conclusive evidence on the existence of homologizers will need to consider ways of ensuring adequate power. Of course, the only real solution to this problem is to replicate our study using a much larger number of responses. This would ensure that relatively smaller ESs be picked up. This would also overcome the reduction in sample size caused by splitting the file into quartiles when undertaking the homologizer analysis6. This may cause problems for researchers in relatively small nations (such as those sampled in this study) since the total population of target exporting firms may not be much in excess of the total number of responses obtained in our samples. Furthermore, attention to response rates will most likely be an important requirement. Both of these issues will be influenced by resource availability, and future studies need to plan for power very carefully.

A further issue which may well be pursued concerns the variables chosen as potential moderators. In this study, we chose to examine a selection of potential moderators which have, for the most part, already received attention in the marketing literature and for which the evidence of significant effects was equivocal. Clearly, our results shed much needed new light on the relationship between market orientation and performance. However, we do not discount the possibility that additional factors may play a role in moderating the link between market-oriented behavior and various aspects of performance. For example, Gatignon and Xuereb’s (1997) study suggested that inter-departmental coordination may influence the strength of the relationship between aspects of strategic orientation (e.g., customer, competitor) and new product innovation outcomes, such as the strength of the innovation’s relative advantage, the degree to which the innovation is considered radical, the relative cost of innovating, and the ability to market the innovation successfully. As a consequence, we suggest that by considering broader conceptualizations of performance, researchers may be able to identify a wider selection of moderating variables.

A related issue concerns the fact that although we used a wide selection of export performance indicators (a total of 15), covering export sales, profits, growth as well as more subjective satisfaction criteria, other important aspects of performance will need to be included in future studies. Perhaps most important is an examination of the long-term effects of an export market orientation on the main performance indicators (sales, profits, growth). It is possible that while aspects of the environment may act to moderate short-run export profitability, sales and growth, over time, such moderating effects may become diluted. It is likely that in order to examine the long-term impact of market orientation on performance, and the moderating effects of the environment, a longitudinal study will be required so that time-series analysis can be undertaken.

Finally, although support for H1 was obtained for both samples in this study, only the larger Finnish sample provided support for H2. Had we been able to take a census of both countries’ exporters, then we would have been in a position to determine whether such differences are of substantive interest. As it is, on the basis of the analysis we were able to undertake, we could not determine whether the observed differences were due to relatively low power in the New Zealand sample or were due to some other factors, such as national or cultural differences. Consequently, future researchers may wish to determine whether national differences do influence the stability of the relationship between export market-oriented behavior and export success. For example, it may be that a country’s domestic market potential acts to moderate this relationship, as might the general experience of a nation’s exporters or their export dependence. Alternatively, country-of-origin effects may play an important role in determining the success of a nation’s exporters. Clearly, then, there is still much work to done before a full understanding of the market orientation-performance relationship is obtained, and we urge researchers to focus their efforts on this important subject.

ENDNOTES

  1. The use of structural equation modeling (SEM) to identify interactions is far more problematic and needs to be used with caution when testing for interaction terms. As Ping (1996, p. 54) demonstrates:
  2. "if the unobserved variables X and Z have the observed variables x1, x2, … , xn, z1, z2, … , and zm, the indicators of the interaction XZ would be x1z1, x1z2, … , x1zm, x2z1, x2z2, … , x2zm, … , xnzm. As a result, adding interactions to a larger structural equation model can introduce model fit, convergence and improper solution problems because of the scores of additional indicators required."

  3. The power for equation (2), used to test H1, could also be estimated. Assuming an ES of .05, a total R-squared at equation (2) of .20, and using an " = .05, the power for equation (2) was never less than .95 or .99 for the New Zealand and Finnish samples respectively.
  4. When " = .05, and assuming independence of tests, the probability of returning at least one significant R-squared change out of fifteen = 1 – .9515 = .54. Although the tests are not independent, the actual probability is likely to be about this size.
  5. Again, the population ES needed to be estimated. For each environment dimension under investigation, approximate power estimates were made using Cohen’s (1977) tables. Specifically, given our interest in relatively small ESs, q (the effect size index) was set at .20, " was set at .05, and sample sizes were determined by the number of responses classified as falling in the upper and lower quartiles.
  6. Interestingly, our findings show that out of the 30 possible samples ESs over the two samples, all were less than or equal to .04, and on a total of 23 occasions, sample ESs of .01 or .02 were observed. Future researchers should be aware of this when designing their studies.
  7. Alternatively, different ways could be used to split the data up, such as a median split. The latter, however, while increasing power due to a more cases in the analysis, is likely to reduce power due to increased noise.

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