An Exploratory Study of Brand Success: Evidence From the Food Industry

The goal of this research is to identify drivers that influence the brand success, in order to develop a more effective business strategy. An abductive theory approach is adopted and food managers from Italy and Sweden were questioned. The authors modeled the eight correlated first-order factors, using seven independent variables and the dependent variable brand success. The variance-based structural equation modeling approach (partial least square [PLS] algorithm) have been applied. This study provides insight and empirical evidence on brand success. The findings can be employed as more effective brand strategies in a sector that has been under-investigated in academic literature and practice.


INTRODUCTION
Organizational resources can boost an additional value to stakeholders if brand management is successfully used and exploited. Brand management has attracted considerable attention in the academic and business community in the last 2 decades. The reasons for this increased awareness are the high cost of launching new brands, the high failure rates of new products (Crawford, 1993;Pappu, Quester, & Cooksey, 2005), the rising cost of promotional activities, and the high costs of attracting and keeping new consumers.
If consumers perceive that a product with a specific brand name adds more value than the competitor's brand, this might develop sustainable and long-term competitive advantage for an organization (Aaker, 1989;Agres & Dubitsky, 1996;de Chernatony & Cottam, 2006). The importance of successful brand building has been discussed in academic literature (e.g., Agres & Dubitsky, 1996;Pappu et al., 2005), but there is no mutual agreement of what constitutes and guarantees success of a brand in the market (cf. Annacker & Hildebrandt, 2004). If a brand is successful, it can protect an organization in an unstable environment (King, 1991), it can develop a stronger negotiating position to its retailers (Park & Srinivasan, 1994), and can help defend market position against competition and increase market shares (Adams, 1995).
We can adapt the famous Jaworski and Kohli (1993) question: Why are some organizations' brands more favorable and expensive than others? This question has been raised by many managers every day, but there is no clear answer or explicit theory. Our motivation to conduct this study is based on the need to provide a better understanding of factors affecting brand effectiveness. The enriched-food industry has been chosen as a case because it has a strong growth rate and it is highly differentiated in a comparison with conventional foods (Boesso, Davčik, & Favotto, 2009). As a mainstream food industry segment, it includes high levels of innovativeness and comprehensive marketing efforts in maintaining differentiation strategies. The high costs for research and development in this segment of the food industry makes price premium and therefore branding important. The development and management of an effective brand strategy in the enriched-food industry has been underinvestigated in business practice and academic literature.
We have applied a variance-based structural equation modeling (SEM) approach (partial least square [PLS] algorithm) because (a) the nature of the study is exploratory rather than (theory) confirmatory; (b) this approach allows formative as well as mixed models, not only reflective models; (c) prerequisites of the data distribution and sample requirements are less stringent than in the covariance-based SEM, which allows studying a small sample size [<100] (cf. Chin, 1998b;Davčik, 2011;Fornell & Bookstein, 1982).
Our intention is to contribute to the existing literature on branding in three ways. First, a brand success scale has been developed and tested empirically in two countries. We applied an abductive theory approach that is based on abductive reasoning in which explanatory propositions (hypotheses) are formed using theoretical rationale and empirical experience of the subject and evaluated, that is, estimated with a statistical technique. Second, the characteristics of brand success drivers in a specific industry context were examined. The study is also a first attempt to determine the underlying factors of brand success in general. Third, this study has avoided the conventional focus on single-authored measures (e.g., Singh & Ranchhod, 2004) as well as added operationalization adjustments from an industry because a multifaceted approach to branding has been applied. Instead of focusing on a single perspective (e.g., financial, customer oriented), this approach includes internal, external, and interactive items in each variable.
A brief overview of the literature on successful brand building and its applicability is presented in the following section. Then background information on the industry context, measures, sample, and data collection are presented. Modeling procedure and varieties of statistical techniques are employed to confirm the reliability of the brand success drivers and are discussed. The study concludes with a discussion about the managerial implications of the findings and some suggestions for further research directions are examined.

SUCCESSFUL BRAND AND ITS DRIVERS: TOWARD A CONCEPTUAL FRAMEWORK
Brand management is considered, by theory and practice, a concept that can fully exploit the assets of an organization as well as generate an additional value from the brand investments (e.g., Pappu et al., 2005;Davčik, Boesso, & Favotto, 2010). Advantages of successful brand building have been widely discussed in the literature (e.g., Adams, 1995;King, 1991;Pappu et al., 2005;Park & Srinivasan, 1994), but only a few conceptual and quantitative studies on its antecedents have been conducted. Success factors studies in marketing should investigate the impact and consequences of success drivers (cf. Albers, 2010) because the goal is not to confirm specific hypothesis but to identify and investigate the different impacts and influences of the various factors that explain brand success. The discussion that follows presents a brief explanation of brand success factors and drivers employed in this study. The items constructs, depicted in the Appendix, reflect an attempt to offer a solution for a brand management application in a business framework as well as a starting point for discussion and further research.
Brand success is determined by the impact of the various success drivers, which are measured as market share, revenue, profit, premium price, return on investment (ROI), brand value, and so on (e.g., Aaker, 1991;Aaker, Kumar, & Day, 2004;Albers, 2010;Kotler, 1999;Davčik et al., 2010;cf. Annacker & Hildebrandt, 2004;Mamalis, 2009). An adequate assessment of brand success cannot be achieved without an appropriate marketing performance audit and activity measurement. Clark and Ambler (2001) have defined marketing performance measurement as the assessment of ''the relationship between marketing activities and business performance'' (p. 231). It is widely accepted that brand equity represents the value of the brand (e.g., Aaker, 1991;Aaker et al., 2004;Kotler, 1999;Kotler & Armstrong, 1999;cf. O'Sullivan & Abela, 2007), which is a cornerstone for brand success in the market.
Differentiation has been defined as ''. . . the act of designing a set of meaningful differences to distinguish the company's offering from competitors' offerings'' (Kotler, 1999, p. 287). Companies that employ differentiation strategy are more market oriented than those that employ cost-leadership strategy (Narver & Slater, 1990). This is the case because differentiators compete on brands that gain higher prices in the market, whereas cost leaders are more oriented on low price competition (Sandvik & Sandvik, 2003;Davčik et al., 2010). Agres & Dubitsky (1996) have concluded, in their study on successful brand-building strategy, that delivering differentiating brand benefits is more appealing for the successful strategy than improving product quality.
Knowledge is not a media consequence but the results of a communication process between an organization and its stakeholders (Agres & Dubitsky, 1996;Boesso et al., 2009). Examples of such stakeholders in the industry are suppliers, consumers, and competitors. In addition, knowledge sources, such as universities or consultancies, can be considered stakeholders too. It is important for the organization to find information from all these stakeholders in order to be able to load the brand with relevant content. In this study, such knowledge from stakeholders is termed brand knowledge.
Consumer orientation represents the capability to understand the consumer's needs (current and=or latent) that shall be satisfied in an efficient and timely manner. Deng and Dart (1994) have argued that consumer orientation represents the extent to which marketers succeed at increasing long-term consumer satisfaction. The marketing concept, and its operationalization side-market orientation-are based on a premise that all organizational activities must be focused on satisfaction of the consumer's needs (Deng & Dart, 1994;Kotler, 1999;cf. Bogue & Sorenson, 2009).
A value plays a crucial role in any marketing activity (Holbrook, 1999). In the literature (e.g., Holbrook, 1999;Kotler, 1999), it has been argued that marketing, as a managerial process, facilitates the exchange of interest as a transaction between two parties in the process where each of them offers some value in return for a greater value. In its essence, consumer value represents the evaluation of an object offered by an organization. The complexity of this concept lies in the fact that ''object'' is a multidimensional ''effect'' framed by shape, name, (in)tangibles, colors, promises, experiences, and so on-it has each of these constructs or a few of them.
There are a great number of scientific approaches and theories on most effective and state-of-the-art organizational design, but the most intricate organizational paradigm of the last 2 decades is related to the contingency factors approach. This research approach is based on congruence hypothesis, for example, that effective organizational structuring requires a fit between contingency factor and design parameter (Mintzberg, 1980). Cyert and March (1963)  have suggested that a business organization is constrained by the uncertainty of its environment. Organizations have problems to maintain a viable coalition and have limitations with capacities for assembling, storing, and utilizing information. As a result, a company can be characterized as an adaptively rational system rather than an omnisciently rational system. Because of that, an organization must obtain information from its environment because it wants to reduce uncertainty with more information. New information from the environmentnew brands, market results, competitors' actions, and so on-gives new sense to an organization. Weick, Sutcliffe, and Obstfeld (2005) have advocated that an organization must develop its capacity to make sense of events in order to construct meaning and establish organizational actions. Knox (2000) has argued that brands are strategic management tools because they represent the company (e.g., its reputation and values). According to him, branding has proved to be the critical factor in all marketing processes as well as a common denominator to business success. Porter (1996) defines strategy as a process in which a unique and valuable position is created involving a different set of activities. In other words, strategic positioning represents ''. . . performing different activities from rivals' or performing similar activities in different ways'' (Porter, 1996, p. 62). Fuchs and Diamantopoulos (2010) have shown that positioning strategy affects the success of brands and benefit-based strategy (i.e., value oriented) is superior over feature-based strategy.
In order to reach brand success, new brand development is important (cf. Boesso et al., 2009;Bogue & Sorenson, 2009). This is valid for service industries (e.g., Berry, 2000) as well as for goods-producing industries (e.g., Wong & Merrilees, 2005). New brand development is highly relevant also in the food industry as Nevo (2001) has argued that a firm's growth in the cereal industry was driven by ''aggressive marketing, rapid introduction of new brands and fueled by vitamin fortification, pre-sweetening and the surge of interest in natural cereals'' (p. 310). John, Loken, and Joiner (1998) have shown that development of new brands is a strong complement to brand extension as brand extensions may dilute the original brand and decrease profit. New brands can create new markets that do not affect established brands and erode existing markets. It can therefore be assumed that new brand development is positively associated with brand success.
The preceding discussion leads us to this research proposition: P: Numerous factors contribute to brand success performance of which the most prominent are consumer orientation, brand differentiation, brand knowledge, new brand development process, values, organization, and creation of the brand strategy.
The proposition frames an overall structure of the possible relationships among the constructs (Wong & Merrilees, 2005) that can facilitate a quantitative, multivariate data analysis approach.

Sample and Data Collection
The data were collected from Italian and Swedish companies that manage enriched-food brands at their respective markets. The enriched-food brands (EFB) represent brands that have added value or characteristics different from the conventional food, such as vitamins and foods with functional and organic ingredients. Many categories of healthy products, such as dietary, organic, functional, integrative, and so on, might be embedded in this food group in which each of these specific subgroups have been characterized by a specific functional ingredient or trait (Boesso et al., 2009). For instance, health-enhancing food might be defined as natural or processed food or food ingredients that bear health-enhancing benefits beyond their primary nutritional functions (Bogue & Sorenson, 2001). Functional and organic foods can be included in this subgroup.
The questionnaire responses were collected through an online survey using Quicksearch software. The advantages of an online survey are (a) easy to use in the statistical software, such as SPSS, STATA, LISREL, and so on; Rundquist (in press) has advocated that (b) respondents fill out a form faster than with interviews or in return mail envelopes; (c) the results are directly stored in a database; (d) it is immediately registered when a respondent answers the survey; and (e) it allows respondents to answer in their spare time or at home in a less stressful situation. Each respondent could choose to answer in English, Italian, or Swedish.
We identified 125 brands from this industry group: 45 in Italy and 80 in Sweden. The respondents returned 58 answers: 21 from Italy and 37 from Sweden (response rate 46.4% of total sample; 47% in Italy and 46% in Sweden). This response rate is satisfactory because (a) acceptable rates for cross-sectional samples have ranges between 12% and 20% (Churchill & Iacobucci, 2009;O'Sullivan & Abela, 2007), and (b) the variance-based SEM, PLS algorithm, has been applied, which allows using a small sample size [<100] (Chin, 1998b;Hair, Sarstedt, Ringle, & Mena, 2011).
The data were collected over a period of 6 weeks. After an initial e-mail request with a link to a questionnaire on the Internet, we sent two additional e-mail reminders on a 2-week basis with a possibility for a respondent to be removed from the list and the survey on request. The Quicksearch software allowed us to ''force'' the respondent to answer all questions from the questionnaire in a manner that they could not pass to the next question if they did not answer the previous one, which provided us with no missing values in the data set. In order to avoid the creation of common method variance that might defile construct relationships, respondents were not informed about specific goals of the research (Sousa & Bradley, 2009

Development of Instruments
The measures employed in this study are based on literature and adjusted according to industry feedback. Preliminary versions of the survey were tested with three senior academics in the marketing=management field. Their feedback was included in the final pretest version of the survey that was tested by four marketing executives in the food industry. The pretest respondents were precluded from participation in the final survey. Results of the pretest showed necessary adaptation of the constructs and clarification of the questions and items included. The factor constructs and items were revised accordingly. All items in the survey are statement style and measured on the Likert scale from 1 (strongly disagree) to 7 (strongly agree).
We employed a perceptual approach in this study, as opposed to objective financial data, due to the impossibility of obtaining objective data from the companies. An objective research approach could not be applied due to the proprietary data and confidentiality issues. The subjective approach is well known in the literature (e.g., Jaworski & Kohli, 1993;Singh & Ranchhod, 2004) and strong correlation between the two approaches has been reported. For instance, Venkatraman and Ramanujam (1987) have drawn the conclusion that ''perceptual data from senior managers . . . can be employed as acceptable operationalizations of business economic performance'' (p. 118). Their conclusion is based on the results from asking senior executives to rate their firms' performance relative to that of major competitors using a number of criteria, including sales growth, net income growth, and ROI and comparing their answers with objective performance statistics. In this study, marketing managers from each organization were questioned in order to obtain senior executive information.

Measures BRAND SUCCESS
We measured brand success with items adopted from various resources (e.g., O'Sullivan & Abela, 2007;Singh & Ranchhod, 2004). The item constructs have been developed within a branding framework using the success performance outcomes (e.g., profitability, market share, ROI) by O'Sullivan & Abela (2007) and higher brand loyalty as surrogate for the customer satisfaction idea by Singh & Ranchhod (2004). The Brand differentiation item constructs are inspired by Porter (1996Porter ( , 1998 and Sandvik & Sandvik (2003) but adapted according to the suggestions from the industry managers. The Brand knowledge item constructs have been inspired by Frishammar (2002) and McDonald, Killerby, Maplesden, and Rolland (2007) and have been improved with suggestions from the industry. The Consumer orientation item constructs are adapted from Deng and Dart (1994) and Singh and Ranchhod (2004). The idea for the new brand development item constructs came from Kotler's (1999) discussion on brand challenges and advantages and was refined with suggestions from industry managers. The values items were inspired by Holbrook (1999) and adapted to the study with suggestions from the industry. The organization item constructs were inspired by seminal works of Mintzberg (1980), Lawrence and Lorsch (1967), Cyert and March (1963), and Weick et al. (2005) with necessary item adaptation for this study. We measure the creation of the competitive strategy with items adopted from Singh and Ranchhod (2004) and Jaworski and Kohli (1993).

MODELING PROCEDURE AND RESULTS
In assessing the model, we have applied the two-step approach suggested by Hair, Black, Babin, and Anderson (2010) and Anderson and Gerbing (1988) estimating a measurement model before the assessment of the multivariate model construction. The justification of this approach needs an accurate assessment of the reliability of indicators. Hair et al. (2010) have advocated that the best approach is a single-step analysis with simultaneous analysis of measurement models. However, in practical applications, researchers are always faced with less reliable measures and the two-step approach is necessary to increase the interpretability of the models and to avoid possible bias interaction among estimated models.
One may ask why we employed the variance-based SEM (VBSEM) instead of the much more known and applied the covariance-based SEM (CBSEM). We decided to apply this technique because of different research perspectives that are based on theory, specification of the measurement model, sample characteristics, and assessment of the goodness-of-fit (Davčik, 2011). The nature of this study is to predict and explore the relationships among different constructs rather than to confirm specific theoretical rationale. We applied the VBSEM approach because it intends to predict the performance of the construct relationships (Fornell & Bookstein, 1982;Hair et al., 2010;Davčik, 2011), that is, to explain the variance. However, CBSEM is based on estimation of the covariance matrices, that is, to confirm the theoretical rationale that was specified by the model (Davčik, 2011). Specification of the measurement model under the VBSEM approach allows reflective, formative, and mixed models. The CBSEM approach is theoretically and empirically embedded in reflective paradigm (e.g., Fornell & Bookstein, 1982;Edwards & Bagozzi 2000;Davčik, 2011). The data distribution assumptions and sample size requirements are less stringent in the VBSEM than in the CBSEM approach (Chin, 1998a(Chin, , 1998bFornell & Bookstein, 1982;Hair et al., 2010;Hair et al., 2011;Davčik, 2011;cf. Henseler, Ringle, & Sinkovics, 2009;Marcoulides & Saunders, 2006).
In order to assess the fit of the model and its estimates, we have applied the variance-based SEM using the PLS path analysis. We have used SPSS 18 to analyze the data and SmartPLS for SEM.

Unidimensionality, Reliability, and Common Method Bias
Because the data were collected in the same period of time and all measures were based on a similar construction, there is a risk of common method bias. We used Harman's test (Harman, 1967;Podsakoff & Organ, 1986) to test, and an unrotated solution in the exploratory factor analysis gave 11 components with an eigenvalue above 1.000. The first factor accounted for 25.03% of the variance and 11 factors together accounted for 76.64% of the variance. Therefore, the common method bias is not likely to have a significant impact on this study. Anderson and Gerbing (1988) have recommended examination of the item scales for unidimensionality and reliability as a first step in survey data analysis. The corrected item-item and item-total correlations for each factor were examined in order to reach unidimensionality. This process used one scale at a time within each multiitem construct. If the coefficients alpha (Cronbach, 1951) and Guttman's k (Guttman, 1945) were lower than 0.7 within multiitem scale, the item(s) with the lowest item-total correlation were eliminated from the scale until reaching the preferred 0.7 level for the multiitem scale. Some evidence on unidimensionality and composite reliability are presented in Table 1.
The evidence of internal model consistency provided by composite reliability measured by coefficients a and k have been presented in Table 1. The alpha measure of reliability is the most commonly used for a set of multiconstruct indicators (Hair et al., 2010) and widely used in the multivariate data research. However, we report Guttman's lambda along with the alpha because it is upper bond to alpha. Table 1 indicates that all reflective constructs, except new brand development, satisfy reliability minimum level  (Fornell & Larcker, 1981). In addition, the shared variance among constructs in the model, measured by the square of their intercorrelations, is smaller than the AVE for each construct (Fornell & Larcker, 1981), which means that discriminant validity is not a concern. After the composite reliability analysis, we have examined results for offending estimates. No offending estimates were found in constructs-for example, negative variances, nonsignificant error variances, and not positive defined correlation matrices as well as correlations larger than 1.
In the second step of the model assessment, we have reported the composite reliability (q c ), depicted in Table 1, for the reflective constructs in the outer model. This reliability indicator is reported for all constructs except for the new brand development because it was excluded from the analysis in the first step of the model assessment. The q c is applied in the variance-based SEM as a control for the internal consistency of the model. This coefficient can be interpreted in the same way as the alpha coefficient (Chin, 1998b;Henseler et al., 2009). The composite reliability coefficient does not assume tau equivalency among the constructs and has a more accurate parameter estimates in comparison with alpha and lambda coefficients (Davčik, 2011;cf. Werts, Linn, & Jöreskog, 1974).
We have controlled the latent constructs for multicollinearity by the assessment of the variance inflation factors (VIF). All variance inflation factors, presented in Table 2, have a value of less than 3, which is below the stringent threshold value of 3.33 and especially far below the standard critical value of 10 (e.g., Cenfetelli & Bassellier, 2009;Diamantopoulos & Siguaw, 2006;Davčik, 2011). We conclude that multicollinearity is not likely to influence the model performance. We present descriptive statistics (i.e., mean and standard deviation); see Table 2 for the employed factors in order to underline their relative importance. The Wald test for the Italian and Swedish factors is presented in Table 2. The important differences between the national samples exist within the four factors: success, brand knowledge, organization, and values. There is no significant difference between the national samples regarding the three factors: consumer orientation, differentiation, and strategy. The strategic dimension of the study reveals that there is no difference between Italian and Swedish companies. In other words, as it is presented 100 N. St. Davcˇik and J. Rundquist in Table 2, 85.56% of Italian and Swedish companies have the same consumer orientation and 38.08% of the companies in both countries apply the same branding strategy. Brand differentiation is similar to 51.51% of the analyzed companies. These results are somewhat intuitive because they reveal that managers in these two countries apply the same marketing logic that leads to the similar strategy and consumer orientation in two countries. The results of control variables attract some attention. We have found that number of employees (t ¼ 4.1562, mean I ¼ 3.47, mean S ¼ 2.46) has significant difference between the two groups but, on the contrary, brand annual turnover (t ¼ 0.6309, mean I ¼ 3.47, mean S ¼ 3.33) has no significant difference between two countries. There is a significant difference between the two countries regarding size (number of employees) as the Italian companies are mostly medium size or large players at the enriched-food market. On the contrary, the Swedish companies are dominantly small or medium-size enterprises that specialize in ''health'' business. Because of this finding, it is not surprising that a brand annual turnover, as a control variable, has no statistical difference. This is the case due to the lack of specialization and focus on a healthy market segment by Italian companies.

Structural Equation Model
The structural model criteria are presented in Table 3. The predictive relevance of the model is assessed by the examination of the Q 2 indicator, which represents a fit between observed values and values reconstructed by the model (Davčik, 2011). The model under study has predictive relevance and observed variables are well reconstructed because all Q 2 values are above zero (Fornell & Bookstein, 1982;Davčik, 2011). The relative impact of the predictive relevance can be assessed by the q 2 indicator. A model has a small impact of the predictive relevance if q 2 .02, a medium impact of the predictive relevance if q 2 has the value between .02 and .35, and a strong impact of the predictive relevance if q 2 ! .35 (Davčik, 2011). The assessed variables of the model reveal a medium impact of the predictive relevance for brand success and brand differentiation and a strong impact of the predictive relevance for all other variables. The coefficient of determination has a substantial value R 2 of .464, which suggests well explanatory power of the model (cf. Chin, 1998b). The effect size, which represents the proportion of variance of the dependent construct that remains unexplained, has a value of f 2 ¼ .866. This signifies large effects of the endogenous variable in the model. Performing variance-based SEM on mixed model is equal to redundancy analysis (Fornell, Barclay, & Rhee, 1988;Davčik, 2011). Redundancy refers to the mean variance in the dependent construct, which is predicted by the independent constructs of the model (cf. Chin, 1998b;Davčik, 2011). Redundancy analysis in the model shows a good fit of the brand success factor because it reveals a low level of redundancy (.092).
The standard method to test the significance of variance-based SEM results is the application of nonparametric bootstrapping technique (Henseler et al., 2009;Davčik, 2011). The inner path coefficients and corresponding statistics (t statistics, standard deviations, and standard errors) are presented in Table 4. We have found that consumer orientation and strategy are highly related to brand success (p .01). The differentiation and knowledge variables have a medium effect on the brand success (p .05). The relation between the two factors (organization and values) and brand success have no statistical significance. This empirically suggests that the overall influence of brand success is primarily explained by consumer orientation, differentiation, knowledge, and strategy.

CONCLUSIONS
We have made a first attempt to determine the underlying factors of brand success as well as to make the connection between them. The study makes a conceptual contribution, based on empirical analysis, in further refinement of the eight constructs that have not been well specified and investigated in marketing literature. In order to achieve such a comprehensive goal, several aims and results have arisen.
The first aim of this study was to develop a brand success scale. This scale was developed in an interactive process combining theory and empirical frameworks from other industries with opinions and Delphi studies among marketing managers. The scale developed contributes as a benchmark for future studies on brand success.
The second aim was to investigate basic factors that can constitute successful brand development. The research findings suggest that there are four underlying factors, which are significantly related to the brand success. These factors were labeled as consumer orientation, differentiation, brand knowledge, and strategy. The results, presented in Table 3, suggest that the model has predictive relevance and all variables are well reconstructed. We have shown that differentiation, based on brand value and consumers' perception about the quality of the brand (cf. Davčik et al., 2010), is a tool that drives brand success in the market. An applied strategy plays an important role in the success of brands. We have successfully tested the importance of different indicators that may influence the applied brand strategy, testing both strategic orientations: value-based and feature-based strategy (cf. Fuchs & Diamantopoulos, 2010). The finding that brand knowledge is of the highest importance for brand success brings new theoretical challenges. Knowledge needed to develop the brand value is resident in many sources such as suppliers, institutes, or just general trend or demographic knowledge. On the other hand, as Hunt and Derozier (2004) emphasize, the capability of anticipating and envisioning future trends in customer needs and wants is crucial for successful brand management. It is well known where such knowledge can be found, but previous studies have pointed out the problem of bringing acquired knowledge to use in development processes. These issues have been addressed in the context of product development, but the transformation of models to a brand development context and the testing in industry-specific context would contribute to theoretical understanding as well as offer important implications for managers. The Wald test has been employed to analyze the difference and significance between Italian and Swedish companies. The test findings are consistent with the expectation that there is a significant difference among factors in two countries, namely, with brand success, importance in brand knowledge sources, company values, and a brand influence on organization. However, we have also found consumer orientation, brand differentiation, and strategy priorities have no statistical significance across the groups. At first glance this result can be rather strange and confusing as it could be expected that there are significant differences across markets and business practices. We believe that these results indicate an important message. These three factors are related to the strategic side of the brand management process, rather than tactical and operational, and can represent the strategic orientation of the companies. This finding indicates similar marketing logic, applied marketing strategies, and consumer orientation between Italian and Swedish companies.
Finally, this study has contributed with a multifaceted scale to measure factors with effect on brand success. In contrast to other measuring scales, measures in this study take a wider grip and are therefore more applicable in practical situations as well as useful for research. The influence of the scales from the Delphi group of managers has also contributed to the usefulness of the scales.
A limitation of the study is the relatively small respondent sample, even though it satisfies statistical and research standards. This is the case due to a relatively narrow industry segment of the enriched-food industry. It will be valuable to expand this research on other food segments, to replicate this study in other countries, and to adjust it for other industries with the attempt to validate current findings and to draw conclusions that are more general on brand success and its antecedents.