www.elsevier.com / locate / ijforecast
A
model of export sales forecasting behavior and performance:
development and testing
a b ,
*
Heidi Winklhofer , Adamantios Diamantopoulos
a
University of Nottingham, Nottingham, UK
b
Marketing and Business Research, Loughborough University Business School, Ashby Road, Leicestershire LE11 3TU, UK
Abstract
This paper presents and tests a path model of export sales forecasting behavior and performance, incorporating organizational and export-specific characteristics. The proposed links are based on insights provided by the literature on forecasting practices as well as the export literature. The model is tested on a sample of UK exporters and is shown to have a good fit. The results indicate that the key determinants of export sales forecasting performance are organizational commitment, in addition to the resources devoted to the forecasting function. Implications of the findings for forecasting practice and performance are discussed and future research avenues identified.
2001 International Institute of Forecasters. Published by Elsevier Science B.V. All rights reserved. Keywords: Forecasting practice; Exporting; Forecast performance
1
. Introduction for a number of purposes in the firm, including
production planning, budgeting, sales quota setting, The importance of sales forecasting for a firm has and personnel planning (Mentzer & Cox, 1984a; often been stressed (e.g., Makridakis & Wheelwright, White, 1986), the factors that contribute to sound 1989) and is best expressed by what happens when it forecasting practice have long been a concern in the is absent. ‘‘Without a sales forecast, in the short literature (Dalrymple, 1987; Fildes & Hastings, term, operations can only respond retroactively, 1994; Drury, 1990).
leading to lost orders, inadequate service and poorly While a large number of empirical studies have utilized production resources. In the longer term, focused on forecasting in general or sales forecasting financial and market decision making misallocate in particular (for a review, see Winklhofer, Diaman-resources so that the organization’s continuing exist- topoulos, & Witt, 1996), the role of forecasting for ence may be brought into question’’ (Fildes & export planning has received only scant attention to
Hastings, 1994, p. 1). Given that forecasts are used date. This is surprising for three reasons. Firstly, exporting is an important source of revenue for many companies (e.g., Gourlay, 1995; Cassell, 1996); in
*Corresponding author. Tel.: 144-1509-223-123; fax:
144-fact, export trade exceeds US $5 trillion per annum
1509-223-961.
(World Bank, 1996) and accounts for some 10% of
E-mail address: [email protected] (A.
Diaman-topoulos). world economic activity (International Monetary
0169-2070 / 01 / $ – see front matter 2001 International Institute of Forecasters. Published by Elsevier Science B.V. All rights reserved. doi:10.1016/S0169-2070(01)00146-7
Fund, 1996). Secondly, since the very purpose of 2 . Conceptual development sales forecasting is to reduce uncertainty
(McHughes, 1987) and since planning for export Broadly speaking, the central idea underlying our operations is typically characterized by a high degree model is that situational variables will determine the of uncertainty (Raven, McCullough, & Tansuhaj, importance of export sales forecasting, which, in
1994), the preparation of sound forecasts is essential turn, will impact upon the firm’s forecasting be-for firms depending upon exports be-for their survival havior and through it on overall forecasting
per-and growth. Thirdly, it has long been recognized that formance. The model is comprised of four
exogen-‘‘the export-sales forecaster is plagued by a number ous variables describing the firm and its export of problems unknown in domestic-market analysis’’ environment and four endogenous (i.e., model-de-(Anderson, 1960, p. 39); in this context, the distinct termined) variables describing the firm’s export sales nature of export sales forecasting was highlighted in forecasting behavior and performance (Fig. 1). In the a recent qualitative investigation which identified following sections, we discuss the model’s detailed several areas where additional problems compared to specification, according to the order of occurrence of domestic sales forecasting can arise (Winklhofer & the dependent variables.
Diamantopoulos, 1996).
Against this background, the present paper seeks 2
.1. Forecasting risk to (a) develop a conceptual model of export sales
forecasting behavior and performance, (b)
empirical-ly identify the factors that impact on overall forecast Forecasting risk refers to the consequences of performance by estimating the proposed model, and obtaining poor forecasts (e.g., high inventory costs, (c) approach forecast performance from a broader loss of customers, production-scheduling problems). perspective than previous studies which have con- While poor forecasts have negative implications for centrated only on accuracy (e.g., Diamantopoulos & the firm’s effective functioning (for details see
Winklhofer, 1999). Gardner, 1990; Marino, 1991–92), such implications
The intended contribution is 3-fold. On the theo- are much more profound for some companies than retical front, the study’s findings should broaden our for others. The reason for this is that, depending understanding of the antecedents of forecasting upon the specific nature of the product involved, the behavior and performance in an export context; as (adverse) impact of forecast errors varies widely already noted, this is a neglected area in the litera- (Schultz, 1984). For example, the additional inven-ture. On the methodological front, our study de- tory cost resulting from overestimation by, say, 10% velops and tests several new measures of key is negligible in the case of a firm producing pencils forecasting constructs (e.g., forecasting risk and as compared to a firm producing computer equip-forecasting commitment); while such constructs have ment. Indeed, industry-specific differences in fore-been the subject of previous conceptual discussions casting behavior previously reported in the literature in the literature, operational measures have been (e.g., Sparkes & McHugh, 1984; Herbig, Milewiz, & lacking. Finally, on the managerial front, the study Golden, 1994) may partly reflect the fact that seeks to provide insights for practitioners by en- forecasting risk (i.e., the consequences of poor abling them to place their own export sales forecast- forecasts) is of different magnitude across industries. ing behavior in a wider context and understand the In an export setting, two key antecedents of factors affecting forecasting performance. forecasting risk can be identified. Firstly, the extent In the next section, we describe the key constructs to which a firm is dependent upon exporting (in comprising our model and provide a rationale for the terms of sales and / or profit) is expected to be proposed linkages. Next, we detail the data collection positively related to forecasting risk. Clearly, a sales
process and construct operationalization. This is estimate of a relatively unimportant part of a firm’s followed by testing the hypothesized linkages using a total business is unlikely to have the same adverse path-analytic approach. The paper concludes with a effects as an estimate relating to an activity from discussion of the implications of the findings and a which the firm derives a significant proportion of its consideration of future research directions. revenue and / or profits; thus, all other things being
Fig. 1. Model of export sales forecasting behavior and performance.
equal, forecasting risk is higher when export depen- to forecasting and there is some evidence that the 1
dence is high. same applies in an export setting (Winklhofer &
Secondly, the turbulence of the export environ- Diamantopoulos, 1996; Rice, 1997).
ment (as reflected in changes in customers, technolo- Commitment to the export sales forecasting task is gy and competition) is expected to be positively expected to be positively influenced by three antece-related to forecasting risk. Environmental instability dent variables: forecasting risk, export dependence, has often been mentioned as a factor that adversely and export experience. The first link is very much a impacts on forecasting (e.g., Dalrymple, 1975; ‘common sense’ relationship, since the importance White, 1986; Beckenstein, 1987). Firms operating in attached to export sales forecasting should be a turbulent export environments face a more uncertain function of the salience of sound forecasts for the decision-making context and, therefore, are likely to firm’s effective functioning (Fildes & Hastings, suffer more from the effects of poor forecasts; thus, 1994). Regarding the positive influence of export forecasting risk is higher when environmental turbu- dependence, firms which are heavily involved in
lence is high. exporting are more likely to value all export-related
activities (Cavusgil, 1984a; Koh, 1991), including 2
.2. Export sales forecasting commitment forecasting. Finally, with regard to export ex-perience, firms which have been exposed to export-The second dependent variable in our model is the ing for many years and / or serving a large number of firm’s commitment to the export sales forecasting possibly geographically dispersed export markets are task. Commitment reflects the firm’s overall disposi- more likely to appreciate the role and significance of tion towards export sales forecasting and is an the forecasting function than less experienced expor-indicator of the importance attached to the latter as ters (Winklhofer & Diamantopoulos, 1996).
an organizational activity (Fildes & Hastings, 1994).
Previous research in a domestic context, shows that 2 .3. Export sales forecasting resources different firms attach different levels of importance
These refer to the various resources (time, money,
1 information, expertise, computer support, etc.)
spe-This ceteris paribus assumption applies to all model linkages
prepara-tion and management. Resource availability has which resource commitment is a function of the level repeatedly been mentioned in the general forecasting of knowledge and experience attained in the foreign literature as a key influence enhancing (or constrain- market’’ (Walters, 1983, p. 35). Companies with ing) the firm’s forecasting capability (Cerullo & extensive export experience usually have employees Avila, 1975; Wheelwright & Clarke, 1976; Schultz, with high export skills (Madsen, 1989) and knowl-1984; Drumm, 1993; Fildes & Hastings, 1994). edge about the environmental system can be of
Unlike the export sales forecasting commitment benefit in forecasting (Stewart & Lusk, 1994). construct which captures an attitudinal disposition Note that one could also argue for the inclusion of (i.e., what the firm thinks about export sales forecast- a direct (positive) link between export dependence ing), the resource deployment construct is action- and export sales forecasting resources. However, based (i.e., what the firm does about export sales after careful consideration, we felt that modeling the forecasting). Separating the two constructs is im- impact of export dependence as an indirect effect portant because a positive attitude towards export was preferable for two reasons. First, to allocate sales forecasting does not automatically result in the resources to the export sales forecasting task, a firm necessary resource allocation (e.g., the firm may be must first appreciate the importance of the latter. unable to afford to buy the necessary data or employ Thus, even if a firm is highly dependent on export-specialized personnel). While a positive link is ing, it is unlikely to make the necessary resources generally to be expected between commitment and available if it considers forecasting an unimportant resource deployment (since firms that attach greater activity; hence, in Fig. 1, export sales forecasting value to the export sales forecasting task are also commitment is modeled as a mediating variable likely to make more resources available), account has between dependence and resources. Secondly, given to be taken of the firm’s overall resource constraints. that resources allocated to any activity (including This is reflected in the expected positive link be- forecasting) are normally a function of ‘willingness’ tween firm size and export sales forecasting re- (captured in our model by the commitment variable) sources. In this context, empirical studies (albeit in a and ‘ability’ (captured here by the firm size and domestic setting) show that larger firms commit experience variables), adding yet another variable more resources to forecasting (Wheelwright & capturing willingness (particularly when that variable Clarke, 1976; Pan, Nichols, & Joy, 1977). Moreover, is already a predictor of the other ‘willingness’ in an export setting, larger companies have been variable, i.e., commitment) might well result in shown to engage in more extensive information unnecessary over-parameterization. In this context, search (e.g., Diamantopoulos, Schlegelmilch, & ‘‘models with lots of parameters and relatively low Allpress, 1990; Schlegelmilch, Diamantopoulos, & df ([degrees of freedom] tend to fit the data quite Tse, 1993; Hart, Webb, & Jones, 1994). Given that well and thus tend not to be very disconfirmable . . . forecasts are based on information (Simister & Therefore, in the model specification process, re-Turner, 1973; Wheelwright & Makridakis, 1980; searchers are very strongly encouraged to keep in Remus, O’Connor, & Griggs, 1995), larger firms are mind the principle of disconfirmability and to con-expected to afford better informational inputs for struct models that are not highly parameterized’’
forecasting purposes. (MacCallum, 1995, p. 30).
Finally, export experience is also expected to have
a positive influence on the resources dedicated to the 2 .4. Export sales forecasting performance export sales forecasting task. A firm’s forecasting
ability has been described as the outcome of an Forecasting performance, as conceptualized in this evolutionary process (e.g., Hubbard, 1992), since study, refers to the overall effectiveness of the firm’s ‘‘even when the resources have been committed, export sales forecasting process as perceived by its knowledge of good forecasting practice and available decision makers. Specifically, we focus explicitly on methods may be lacking during startup’’ (Makridakis the confidence attached to the forecasts produced and & Wheelwright, 1989, p. 446). Similarly, ‘‘entry into the evaluation of the firm’s forecasting performance foreign markets proceeds in a gradual manner, in relative to competition.
The handful of empirical studies on export sales mands have adverse effects on performance forecasting currently available, have only considered (Dalrymple, 1975; White, 1986; Beckenstein, 1987), forecast accuracy and bias as performance criteria the latter was only represented by forecast accuracy. (Winklhofer & Diamantopoulos, 1997; Diaman- In contrast, in our model, performance is conceptual-topoulos & Winklhofer, 1999). While these are ized as managerial evaluation of export sales fore-undoubtedly important dimensions of forecast per- casting effectiveness (see above); such evaluations formance (Mentzer & Kahn, 1995), perceived use- are more than likely to ‘factor in’ the turbulence of fulness and impact on decision making are also key the export environment (Sapienza, Smith, & Gannon, dimensions (Fildes & Hastings, 1994); indeed, the 1988). Any impact of the environment on forecasting ultimate test for a forecasting system is whether performance is thus expected to be of an indirect decision-makers trust the forecasts and actually use nature, i.e., initially filtered through forecasting risk them in their decision-making (McLaughlin, 1979; and subsequently mediated by commitment and Jenkins, 1982; Fildes & Hastings, 1994). Moreover, resources.
assessing performance solely in terms of accuracy and / or bias is only meaningful for a specific
fore-casting level (e.g., product item) and specific time 3 . Methodology
horizon (e.g., 3 months ahead). This is because all
forecast error measures involve a comparison of 3 .1. Data collection actual versus predicted sales (for relevant reviews,
see Mahmoud, 1984, 1987) and, therefore, both the Estimation of the model in Fig. 1 was based on level and the time horizon of the relevant sales survey data collected by a mail questionnaire. The values must be explicitly specified (Small, 1980; latter was developed and pretested using a three-Mentzer & Cox, 1984b; White, 1986). Given that our stage strategy (Reynolds, Diamantopoulos, & model seeks to describe a firm’s overall forecasting Schlegelmilchh, 1993). Stage one comprised of a behavior and performance on the export front (rather comprehensive review of both the exporting and than the development and performance of individual forecasting literatures as well as exploratory inter-forecasts), a broader view of forecast performance is views with local exporting companies; this lead to a
justified. first version of the instrument. In stage two, protocol
Overall forecasting performance is expected to be interviews were undertaken to pretest the
ques-positively influenced by the firm’s commitment and tionnaire. Further pretesting was undertaken during resources dedicated to the export sales forecasting stage three, which included two separate mail pilots task. A beneficial effect of commitment is postulated with 100 UK exporters each. Following the protocol because the greater the importance attached to export interviews and the mail pretests, a final version of sales forecasting as an organizational activity, the the instrument was developed and used in the main more care and attention is likely to be paid when survey.
preparing and / or approving forecasts, i.e., the ‘qual- For the main survey, a stratified sample of 1330 ity’ of the forecasting process is likely to be higher UK exporters in the manufacturing field was (Winklhofer & Diamantopoulos, 1997). As far as the targeted, derived from the Dun & Bradstreet data-impact of resources is concerned, the latter capture base. The stratification criterion employed was firm aspects such as information acquisition, communica- size (number of employees) and the sample was split tion flow and computer support, all of which can be as follows: 40% had between 50 and 100 employees, expected to enhance forecasting performance (e.g., 40% over 100 up to 500 employees, and the remain-Peterson, 1990; LeVee, 1992–93; Fildes & Hastings, ing 20% comprised of large firms with staff of over
2
1994). 500. Personalized mailing was undertaken in order
Note that we do not postulate a direct link to stress the importance of the individual targeted between environmental turbulence and forecasting
2
performance. Although previous research indicates The specific size categories reflected the distribution of the firms
and to avoid ‘loosing the questionnaire in the consistency (Cronbach’s a 50.873). The unidimen-system’. A freepost return envelope was also in- sionality of the forecasting risk construct was further cluded and the respondents were promised a copy of confirmed by fitting a confirmatory factor analysis
2 the results. Two weeks after the initial mailing, a (CFA) model, which showed good fit (x 57.85, follow-up letter was sent to a stratified sample of 300 df55, P50.187; RMSEA50.057, GFI50.981, identified non-respondents (using the same strata CFI50.994).
proportions as in the original sample).
Altogether, 256 responses were obtained of which 3 .2.2. Export sales forecasting commitment
180 were usable; the distribution of the responses A four-item scale with items scored on a five-point across the three size categories was practically Likert format was used to capture the commitment
3
identical to that of the original sample. To gather construct. The unidimensionality of the commitment first-hand information with regard to reasons for scale was initially indicated by an EFA and
sub-2
non-response, a telephone follow-up of 100 non- sequently confirmed by a CFA (x 51.832, df52, respondents was undertaken. This showed that P50.400; RMSEA50.000, GFI50.994, CFI5 ineligibility (i.e., firm was no longer exporting, firm 1.000). This scale also showed good reliability (a 5 did no longer exist) was the main reason for non- 0.804).
response. Among eligible non-respondents, the most
common reasons were lack of time or company 3 .2.3. Export sales forecasting resources
policy prohibiting the completion of any ques- A nine-item index composed of formative in-tionnaire. After adjusting for ineligibles (Wiseman & dicators (Bollen & Lennox, 1991; MacCallum & Billington, 1984), the effective response rate came to Browne, 1993) was used to represent the resources
4 18.5%. The latter is comparable with other surveys dedicated to the export sales forecasting task. The conducted in an industrial setting (Jobber & Blaes- validity of the nine indicators was assessed by dale, 1987) and with surveys on forecasting practices correlating them to a ‘global’ item, external to the (e.g., White, 1986; McHugh & Sparkes, 1983). While index, which summarises the essence of the construct the telephone follow-ups gave no reason to suspect involved (Spector, 1992); the latter was a statement that non-response error was an issue of concern, reading ‘‘our firm devotes few resources (people, further analysis was undertaken by comparing early money, time) to export sales forecasting’’ and all and late responses as recommended by Armstrong nine items were found to be significantly correlated and Overton (1977). A series of t-tests for indepen- with it (albeit in various degrees). Moreover, to dent samples failed to identify significant differences ensure that there was no redundancy among the between early and late respondents, providing
addi-tional evidence that non-response bias is unlikely to be a major problem in this study.
4
The difference between reflective and formative indicators de-pends on the causal priority between the indicators and the
3
.2. Model operationalization—endogenous construct in question (Edwards & Bagozzi, 2000). If the construct
variables is viewed as a latent variable, that gives rise to something that is
observed, then the indicators are reflective. In contrast, ‘‘if the latent variable is conceived as being determined by a linear
3
.2.1. Forecasting risk
combination of observed variables, then the indicators are
forma-Five, five-point Likert statements were used to
tive’’ (Diamantopoulos & Siguaw, 2000, p. 21). Most multi-item
describe the consequences of poor export sales measures in the social sciences assume that the items are reflective forecasts. An exploratory factor analysis (EFA) indicators (Bollen & Lennox, 1991) and, consistent with classical
showed that all five items loaded on a single factor; test theory (see Nunnally & Bernstein, 1994), conventional assessments of dimensionality and reliability can be legitimately
moreover, the resulting scale showed good internal
performed on such measures. However, with multi-item measures comprised of formative indicators, such procedures are not
3
Specifically, 39% had between 50 and 100 employees, 43% appropriate and, instead, attention needs to be focused on the between 100 and 500 employees and the remaining 18% had staff external validity and multicollinearity of the indicators (for a full of over 500. discussion see Bollen, 1989; Bollen & Lennox, 1991).
indicators defining the resource index (Bollen, 3 .3.2. Export dependence
1989), variance inflation factors (VIFs) were com- Consistent with previous studies (e.g., Diaman-puted for each item; these showed that no multicol- topoulos & Inglis, 1988; Koh, 1991; Katsikeas, linearity existed among the nine items (the highest 1994), the export intensity measure was used to VIF came to 1.588 which is far below the common represent the dependence of the firm on exporting. cut-off threshold of 10; see, for example, Kleinbaum, This measure is simply the export-to-total sales ratio
Kuppler, & Muller, 1988). and shows the proportion of firm sales that are
derived from export activities. 3
.2.4. Export sales forecasting performance A three-item scale was used to measure the
3
.3.3. Export experience managerial evaluation of the firm’s overall
effective-Four variables were used to capture a firm’s ness with regards to export sales forecasting. The
experience on the exporting front. The first is an three items were shown by an EFA to load on a
indicator of the ‘quantity’ of its experience and single factor and produced an a value of 0.685.
shows the number of years the firm has been Although a formal dimensionality test by means of
exporting (Bodur & Cavusgil, 1985). The second CFA was not possible (a one-construct, three
in-variable captures a firm’s ‘depth’ of experience and dicator model is just-identified, with zero degrees of
is described by the stage in the export life cycle freedom), all factor loadings were substantial and all
(Cavusgil, 1984b). The last two variables capture a error variances were significant. The validity of the
firm’s ‘scope’ of experience (Erramilli, 1991) and performance scale was further assessed by
correlat-include the number of countries to which the firm 5
ing it to the number of different planning situations
exports and the number of distinct geographical in which export sales forecasts are used as input
regions served. An EFA showed that all four ex-(Makridakis & Wheelwright, 1989). As the variety
perience indicators loaded on the same factor and an of forecasting applications is an indication of the
internal consistency check on the combined items degree to which export forecasts affect decision
(after standardization) yielded an a value of 0.722. making in the firm (McLaughlin, 1979), a positive
The unidimensionality of the experience scale was relationship was expected with the forecasting
per-further tested by specifying a one-construct, four-formance scale; this indeed proved to be the case as
indicator CFA model, which also produced a good fit indicated by a positive significant correlation be- 2
(x 51.679, df52, P50.432; RMSEA50.000, tween the two variables (r50.181, P50.016). The
GFI50.995, CFI51.000). items comprising the scales of all dependent
vari-ables in the model are shown in Appendix A.
3
.3.4. Environmental turbulence 3
.3. Model operationalization—exogenous The turbulence of the export environment was
variables measured employing three scales developed by
Jaworski and Kohli (1993), capturing the dimensions 3
.3.1. Firm size of technological (five items), customer (five items)
The firm’s total employment level was chosen as a and competitor (seven items) turbulence. The three measure of company size, which is a good proxy of scales were subsequently combined into a measure of company resources (Bonaccorsi, 1992). As the em- overall turbulence. The application of the reliability ployment measure was positively skewed, a logarith- formula for linear composites (Nunnally & Berns-mic transformation was undertaken to improve its tein, 1994) produced a value of 0.816 which is very
6
distribution. satisfactory.
5
A choice of six distinct planning situations was specified based
6
upon the literature (Rothe, 1978; Mentzer & Cox, 1984a; White, Descriptive statistics on all model variables (i.e., means, standard 1986; Peterson, 1993) namely production, inventory, marketing, deviations, correlations) are available upon request from the sales quota, finance and personnel planning. authors.
4
. Model estimation Focussing initially on the LISREL results, the
2
model’s overall fit was very good (x 516.303, We used two approaches to estimate the path df512, P50.178; RMSEA50.049, GFI50.974, model in Fig. 1. First, using the covariance matrix of CFI50.980). Moreover, all but three of the hypoth-the eight variables as input, we used structural esized linkages turned out to be significant (at P, equation modeling (with the LISREL 8 program) to 0.05 or better) and consistent with expectations, simultaneously estimate all the proposed linkages lending support to the model’s conceptual soundness.
2 and test the fit of the overall model. Such a confir- Lastly, the squared multiple correlations (R ) ob-matory approach was deemed most appropriate given tained showed that non-trivial amounts of variance in the fact that the model relationships were specified the endogenous variables were accounted for by the on the basis of prior theory and research. In this joint explanatory power of their hypothesized antece-context, ‘‘structural equation modeling, including dents (ranging from 16.4% for forecasting risk to classical path analysis, may be used to help bridge almost 60% for export sales forecasting commit-the gap between empirical and commit-theoretical research; it ment).
is a multivariate statistical technique that uses em- The regression results were very similar to those
pirical evidence to estimate the strengths of a priori discussed above, painting an almost identical picture. hypothesized structural relationships within a par- Although each dependent variable was individually ticular theory-derived model’’ (Mueller, 1996, pp. regressed on all causally prior variables (i.e., no
9
57–58, emphasis in the original). Second, we sup- paths were restricted to zero), the only notable plemented the above analysis with a series of difference in the parameter estimates versus the conventional multivariate regressions, focusing on LISREL results was that the coefficient of
environ-7 10
each dependent variable in turn. Although we mental turbulence was significant. The other co-recognize that ‘‘path analysis has distinct advantages efficients were of similar relative magnitude and
2 over multiple regression, [as] it affords the ability to significance and the same applies to the R values. establish a causal relationship among independent Fig. 2 shows the (standardized) parameter estimates variables, specify the specific relationship among the for the various linkages in the model; the figures in independent variables, and model the complex nature brackets refer to the corresponding regression results. of variable relationships posited by theory’’
(Schumacker & Lomax, 1996, p. 55), we neverthe-4
.1. Forecasting risk less felt that the less structured nature of multiple
regression would provide a useful check on the 8
stability of the LISREL results. As hypothesized, export dependence had a
posi-tive influence on forecasting risk indicating that the consequences of poor forecasts for firms that are
7
For both the LISREL and regression analyses, we used listwise
highly dependent on exporting are greater than those
deletion of missing cases, which reduced the total sample size to
for less dependent firms. However, the proposed
153. The latter easily satisfies the minimum variables-to-cases and
variables-to-free parameter requirement recommended in the positive link between environmental turbulence and
literature (e.g., Bentler & Chou, 1987).
8
A reason for this is that OLS regression is a limited-information estimation method, whereby each parameter equation is estimated
9
separately; hence, it is relatively robust against mis-specification. For example, export sales forecasting commitment was regressed In LISREL, on the other hand, full-information techniques against export dependence, export sales forecasting risk and (typically maximum likelihood) are used which have the advan- environmental turbulence; similarly, export sales forecasting per-tage that ‘‘the estimation of each parameter utilizes the infor- formance was not only regressed against commitment and re-mation provided by the entire system’’ (Long, 1983, p. 43). Their sources (as hypothesised in Fig. 1) but also against all other downside is that estimation of each parameter can be affected by variables.
10
mis-specification in other parts of the model. We would like to This is most likely the result of the different estimation thank an anonymous referee for encouraging us to use regression procedures used in the two analyses (i.e., maximum likelihood analysis in addition to structural equation modeling. versus OLS); see Footnote 9 above.
Fig. 2. Parameter estimates. * P.10; all other coefficients significant at P,0.5 or better; figures in brackets refer to results derived from regression analysis.
forecasting risk was not consistently supported by 4 .4. Export sales forecasting performance the results; despite its correct sign, the relevant path
coefficient failed to reach significance in the LISREL As hypothesized, both commitment and resources
analysis. had positive and significant effects on forecasting
performance. To test whether our omission of a 4
.2. Export sales forecasting commitment direct link between environmental turbulence and forecasting performance was justified, the model was All three proposed antecedents of export sales re-estimated by freeing the relative parameter. The
2
forecasting commitment returned significant linkages new model also produced a good fit (x 514.774, in the correct direction; thus, consistent with ex- df511, P50.193; RMSEA50.048, GFI50.976, pectations, commitment is positively affected by the CFI50.983). However, the path coefficient between firm’s export dependence and experience as well as turbulence and performance was very small in
the degree of forecasting risk. magnitude (it came to 0.096) and non-significant.
2
Furthermore, a x difference test between the origi-4
.3. Export sales forecasting resources nal and the less-constrained model failed to show an 2
improvement in fit (x difference51.53, df51, As expected, commitment to the export sales P.0.10). Taken together, these results support our forecasting task had a positive, significant relation- decision to exclude a direct link between environ-ship to the resources dedicated to this task. However, mental turbulence and forecast performance from our contrary to expectations, neither the firm’s overall model.
resource availability (as proxied by company size) Finally, to calculate the total effects of the various nor the firm’s export experience were found to antecedent variables on forecast performance, we significantly impact upon the resources specifically first eliminated the non-significant paths (one at a
¨ ¨
allocated to export sales forecasting; these findings time, as recommended by Joreskog & Sorbom, 1989) are quite surprising and will be revisited in the and re-estimated the model based only on the
only marginally from those shown in Fig. 2) were preciate more the importance of export sales fore-then used to compute the values of compound paths. casting and, in turn, are likely to dedicate more Note that elimination of non-significant linkages did resources to this activity.
not result in any deterioration in the model’s fit; the difference in overall fit between the original model
and the new model in which the three non-significant 5 . Discussion and conclusions paths in Fig. 2 were set to zero was not significant
2
(x difference58.33, df55, P.0.10). Table 1 The present study sought to contribute to the shows the direct and indirect effects of the model limited literature on export sales forecasting practice variables on export sales forecasting performance. by developing and testing a model of the firm’s The most important influence on forecasting per- overall export sales forecasting behavior and per-formance is a firm’s commitment to the export sales formance. The model’s fit on survey data provided forecasting task, followed by resources, export de- by UK exporters was very satisfactory and the pendence, forecasting risk and export experience. estimated linkages supported most of the hypoth-The large impact of commitment on performance esized relationships. Moreover, rigorous dimen-indicates that firms which value export sales fore- sionality and reliability tests revealed sound psycho-casting as an organizational activity obtain good metric properties for all measures used to results. As hypothesized, commitment has both a operationalize the model. Although, as with any direct as well as an indirect influence on forecasting model, independent replication of the findings is performance (the latter by facilitating the allocation clearly needed (Cudeck & Browne, 1983; MacCal-of sufficient resources to the export sales forecasting lum, Rozonwski, & Necowitz, 1992), the results task). These results support the view that ‘‘a firm’s obtained suggest that quite a bit of confidence can be export sales forecasting commitment could act as an attached to both the measures utilized and the important mediator between firm and export charac- substantive relationships identified.
teristics on the one hand and forecast performance on On the methodological front, to our best knowl-the oknowl-ther’’ (Diamantopoulos & Winklhofer, 1999, p. edge, this is the first time that properly tested multi-78). Specifically, the influences of export depen- item measures have been used to capture the con-dence, experience and forecasting risk on perform- structs of forecasting risk, commitment, resources, ance are all channeled through the commitment and performance in either a domestic or an export construct. Thus firms for which sound forecasts are context. While these constructs have been the subject essential due to their dependence on exporting and / of conceptual discussions in the forecasting litera-or because of the negative consequences of politera-or ture, previous operationalizations have been too forecasting, also obtain better export sales forecasts; simplistic (typically single items) and not subjected however, they do so because such firms are commit- to any tests of their psychometric properties (for ted to the export sales forecasting task. The same example, Cerullo and Avila (1975) used the number applies to the impact of export experience; firms of full-time employees working in forecasting as a which are more experienced exporters also obtain measure of forecasting commitment, while Wheel-higher forecasting performance because they ap- wright and Clarke (1976) employed the forecasting
Table 1
Direct and indirect effects on export sales forecasting performance
Variable Direct Indirect Total Rank
effect effect effect
Export dependence – 0.240 0.240 3
Export experience – 0.073 0.073 5
Export sales forecasting risk – 0.208 0.208 4
Export sales forecasting commitment 0.331 0.108 0.439 1
budget as a commitment indicator). Moreover, this is 1997; Hamill & Gregory, 1997). Such developments the first time that a comprehensive measure of export may imply that smaller firms may no longer be at a experience capturing different facets of the latter has great disadvantage against their larger counterparts been employed; previous operationalizations in the when it comes to forecasting. Thirdly, given that the exporting literature have only considered individual measure of forecasting performance is one of per-aspects of experience such as ‘quantity’ (years ceived effectiveness, it could be the case that size
exporting) or ‘scope’ (number of export destina- may already be taken into account (i.e., factored in) tions). Thus, the forecasting-related measures de- when responding to the performance measure (i.e., 11 veloped in this study could be fruitfully used in ‘‘for a firm of our size, we are doing quite well’’). future investigations of export forecasting practices, Needless to say, all three explanations are specula-while the export experience measure could be tive and in need of empirical investigation in future adopted in any study of export behavior. studies.
Shifting attention to the substantive implications Export experience was also not found to directly of the study’s findings, the latter show that exporters affect the resources made available for export sales follow a fairly pragmatic approach to export sales forecasting purposes; its effect is only indirect, i.e., forecasting. Those who are faced with high forecast- mediated by the commitment variable. This finding ing risk (and, therefore, need good export sales suggests that a firm’s export experience is important forecasts), display a greater commitment to the for appreciating the importance of forecasting as an export sales forecasting task and dedicate more organizational activity but does not act as a direct resources to it. Such commitment and resource driver of resource allocation; the role of the latter is allocation seem to pay off since such firms also performed by the commitment construct which is obtain better forecasting performance. influenced not only by the firm’s export experience Contrary to expectations, firm size does not have but also its export dependence and, most importantly, any impact when it comes to allocating resources for the degree of forecasting risk.
export sales forecasting purposes; moreover, since In terms of future research, an obvious issue resources are directly linked to forecasting perform- deserving detailed attention concerns the integration ance, it appears that firm size also does not indirectly of forecasting activity into the overall export plan-affect the effectiveness of export sales forecasting. ning process and the impact that (good) forecasting Three explanations can be offered for the insig- practice has on the firm’s export performance. A set nificant influence of firm size. Firstly, it could be the of related issues centers around the relationship case that findings in a domestic setting (as reported between the firm’s forecasting activities and per-by, for example, White, 1986; Dalrymple, 1987) formance in its domestic versus export markets (e.g., simply do not hold in an export setting; there are to what extent does a firm’s forecasting capability in precedents for this in, for example, the area of the home market enhance its capability on the export information utilization by industrial firms where front? What is the relative perception of the accura-conflicting results in domestic and export settings cy / bias / usefulness of domestic versus export sales have been reported (see Deshpande & Zaltman, 1987 forecasts for decision making? How are resources versus Diamantopoulos & Horncastle, 1997). Sec- allocated between domestic and export sales fore-ondly, previous studies indicating an impact of firm casting and what are the relative costs for producing size on forecasting resources are now quite dated. In forecasts?). A third avenue for future research con-the meantime, developments in information technol- cerns the characteristics (e.g., education, training, ogy and reduction in the cost of the latter have made previous experience, management style) of the deci-at least one important resource for forecasting sion makers involved in forecast preparation, approv-(namely computer support) more accessible and al, and / or use; at present very little is known affordable (Fildes & Hastings, 1994); in addition, the
advent of the Internet has enhanced the firm’s 11
The authors would like to thank an anonymous reviewer for
capability to search for and collect export-related suggesting this interpretation of the non-significant effect of
whether and, if so, how such characteristics impact 4. A major barrier to developing sound export sales upon a firm’s export sales forecasting capability. forecasts is lack of relevant information.
Finally, the forecasting implications of alternative 5. The costs of obtaining data useful for export sales export strategies (e.g., emphasizing standardization forecasting purposes are often prohibitive. versus adaptation) need to be investigated; currently, 6. There is a lot of support from top management both the forecasting and the exporting literatures are when it comes to export sales forecasting. practically silent on how a firm’s strategic choices in 7. Export sales forecasts are always prepared in a the export arena affect the organization, manage- hurry as our personnel are busy with other work. ment, and effectiveness of the forecasting function. 8. We have all the computer support we need for
developing export sales forecasts.
9. The people involved in export sales forecasting A
ppendix A have a lot of knowledge about our export markets.
Export sales forecasting risk: item pool Export sales forecast performance: item pool 1. Accurate forecasting of export sales is crucial for 1. Overall, we are as good in forecasting export
the success of our export operations. sales as any firm in our industry.
2. If we under-estimate our export sales, we may 2. Our export decision-makers have a lot of
confi-lose some of our customers. dence in our export sales forecasts.
3. Getting our export forecasts right is essential if 3. Compared to our competitors in export markets, delays in fulfilling customer orders are to be our export sales forecasting capability is superior. avoided.
4. Without good export sales forecasts our export planning would suffer.
R
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Industrial Marketing Management,7, 114–118. PhD, FCIM) is Professor of Marketing and Business Research at Sapienza, H. J., Smith, K. G., & Gannon, M. J. (1988). Using Loughborough University Business School, UK. He was previous-subjective evaluations of organizational performance in small ly Professor of International Marketing at the European Business business research. American Journal of Small Business, 12, Management School, University of Wales Swansea, where he 45–53. headed the Marketing Group. Other past academic posts include Schlegelmilch, B. B., Diamantopoulos, A., & Tse, K. (1993). full-time appointments at the University of Edinburgh and the Determinants of export marketing research usage: Testing University of Strathclyde, and Visiting Professorships at the some hypotheses on UK exporters. In Baker, M. J. (Ed.), University of Miami, Vienna University of Economics and Busi-Perspectives on Marketing Management. Wiley. ness, Lund University (Sweden), Universite Robert Schuman´ Schultz, R. L. (1984). The implementation of forecasting models. (France) and University of Dortmund (Germany). His main Journal of Forecasting,3(1), 43–55. research interests are sales forecasting, marketing research and Schumacker, R. E., & Lomax, R. G. (1996). In A Beginner’s international marketing. His work has appeared, among others, in Guide to Structural Equation Modeling. Mahwah, NJ: Law- the Journal of Marketing Research, International Journal of rence Erlbaum Associates. Research in Marketing, Journal of International Marketing,
International Journal of Forecasting and Journal of Business Lecturer in Management Science at Loughborough University Research. He sits on the editorial review boards of 10 marketing Business School. She received her PhD in International Marketing journals, is a founder member of the Consortium for International from the European Business Management School, University of Marketing Research (CIMaR), Associate Editor of the Internation- Wales Swansea where she also worked as a Research Assistant. al Journal of Research in Marketing and International Journal of Her research interests are in forecasting, quantitative methods, Forecasting and a referee for several academic journals, profes- international marketing and e-commerce adoption, and her work sional associations and funding bodies. has been published, among others, in the Journal of Marketing Research, International Journal of Forecasting, Journal of Strategic Marketing, the International Marketing Review and Dr. Heidi WINKLHOFER is Senior Lecturer in Marketing at
several conference proceedings. Nottingham University Business School. She was previously