The Effect of Customer and Competitor Market Sensing Capability on Business Performance of SMEs: An Empirical Study in Indonesia

Abstract

The purpose of this study is to investigate and examine the role of specialized marketing capabilities (SMC) in mediating the relationship between customer sensing capability (CuSC) and competitor sensing capability (CoSC) with business performance (BP) at SMEs retail fashion in Indonesia. This study used 330 SMEs from ten regencies in Indonesia and examined the regression relationship between the four variables, SMC, CuSC, CoSC, and BP. Confirmatory factor analysis (CFA) was used to measure the validity and reliability of the construct used. For data analysis techniques, this study used structural equation modeling (SEM) with AMOS Version 22.0. This study found that SMC acts as a partial mediator in the relationship between CuSC and CoSC with SMC and BP. By examining the diverse literature on market sensing capability, marketing strategy, and BP, this study offers a unique analysis of market learning and its effects on SMC and BP in Retail Fashion SMEs in Indonesia. Furthermore, future research needs to broaden the findings and improve generalizations by conducting studies of SMEs in other industries, such as manufacturing, and services of small, medium and large scale. In addition, it needs to add some countries as research objects, not only Indonesia.

1. Introduction

The fashion industry is an industry characterized by changes in the style of clothing and accessories worn by groups of people for short periods of time (Christopher et al., 2004); reflect changes in aesthetic, economic, political, cultural and social life (Ciarniene, 2014); very global and dynamic (Čiarnienė &Vienažindienė, 2014), the demand is rarely stable or linear because it depends on the peculiarities of the weather, film industry , or even by pop artists and soccer players; and high impulse purchases, meaning the consumer makes a purchasing decision when he is confronted with the product at the point of purchase (Christopher et al., 2004). Therefore, the company’s success in this industry depends on being able to meet customer demand in shorter delivery time frames and ensuring that supplies can be synchronized to meet the peak and depth of demand (Barnes & Lea- Greenwood, 2006; Christopher et al., 2004; Nagurney & Yu, 2012; Runfola & Guercini, 2013). Furthermore, customer satisfaction is one of the most critical things in the company’s progress, showing how much influence product performance has in satisfying consumers (Sholiha et al., 2021). Moreover, to be profitable in this industry, fashion clothing retailers need to take a ‘speed to market’ approach to take advantage of fashions that do not exist in their competitors’ stores (Bhardwaj & Fairhurst, 2010). It has further been emphasized that market responsiveness and agility through the rapid incorporation of customer preferences into the design process in product development increases profit margins for retailers (Christopher et al., 2004). The very dynamic changes in the fashion industry have forced retailers to expect low costs and flexibility in design, quality, and ‘speed to market’, as a strategy of maintaining a favorable position in an increasingly demanding market (Bhardwaj & Fairhurst, 2010). The demands of a very dynamic environmental change require that fashion retail companies improve their market sensing capabilities in finding, analyzing and utilizing customer and competitor information that supports positional superiority and business performance.

Research related to the capabilities of customers and competitors has been collected to form a market sensing capability (MSC) construct. This approach seems to be able to hide the difference between customer sensing capability (CuSC) and competitor sensing capability (CoSC) by taking into account the subsequent impact. Therefore, as suggested by Noble et al. (2002) which requires that market orientation be studied in disagreement, this study sorts MSC into CuSC and CoSC. This research is not only managerially relevant but also interesting for marketing theorists, especially for researchers who focus on market sensing. Since market sensing is usually considered a major aspect of marketing capability, this research addresses this concern by making a clear distinction between sensing customers and sensing competitors. This may be displayed by its distinctiveness in its impact on specialized marketing capability and business performance, especially on SMEs. If the results show that there is a difference in the effect between CuSC and CoSC on specialized marketing capability (SMC) and business performance (BP) of SMEs, this would suggest that customer-oriented research and competitor sensing as separate constructs is indeed a more suitable approach to study MSC. This is identical to Homburg et al. (2007) which separates the concept of market responsiveness into customer responsiveness and competitor responsiveness.

Currently, there are not many researchers who determine how the dimensions of MSC, which are CuSC and CoSC, are treated as a disaggregated part in relation to superior BP. Some studies have indeed tested the relationship between MSC and BP, and the results are still. Some studies explain that MSC is an important antecedent of BP (Ahmed et al., 2017; Hendar, 2019; Okwemba, 2018; Osakwe et al., 2016), while other studies indicate that MSC is not an important variable that it directly affects BP (Ardyan, 2016; Lindblom et al., 2008; Morgan et al., 2009). Some researchers then sought to investigate the potential mediating relationship between MSC and BP. Such research is indispensable for understanding the ways in which MSC influence BP. From a strategic perspective, MSC remains incomplete if practitioners do not understand the modus operandi that results in superior BP. By explaining the mediator of the relationship between MSC and BP, it will provide managers with more detailed insights on how MSC works and how it can be useful as a company’s strategic capability. Hence, this research tries to fill this knowledge gap by placing specialized marketing capabilities (SMC) as a mediating tool in the relationship between markets with BP.

SMC is a core element of marketing capability for three reasons. First, SMC determines the effectiveness of marketing strategy decisions and marketing strategy implementation (Morgan, 2012; Morgan et al., 2012). Second, SMC determines superior BP (Morgan et al., 2009). SMC is a source of company positional advantage (Morgan et al., 2004). Third, the increasing level of competition, technological developments in the market and shorter product life cycles, pressure companies to continue to increase their capacity in developing SMC.

Research on the impact of MSC on SMC has not been found. Sometimes SMC and market orientation are treated as independent variables that affect company performance (Morgan et al., 2009). At this time, opportunities exist to advance the understanding of the relationship between the dimensions of MSC, which are CuSC and CoSC with SMC, and BP.

2. Literature Review

2.1. Market Sensing Capability (MSC)

The strategic management literature has recognized that different organizational capabilities are a source of competitive advantage (Day, 1994). Competitive advantage must be built based on competencies adjusted to the tangible and intangible resources (Bambang et al., 2021). One important type of capability in developing a successful business is MSC (Day, 1994). MSC is a company’s ability to anticipate future market evolution and detect opportunities that arise based on information collected from its business ecosystem ( Mu et al., 2018;Teece, 2007) and potential impacts on competitors, customers, and other stakeholders (Day, 1994). This capability is needed to develop skills in learning, understanding, and responding to market dynamics. Companies that have MSC will have the ability to observe, assess, continue to monitor, and also make relevant decisions related to changes in technology and markets such as customers and competitors (Bayighomog et al., 2018). Lindblom et al. (2008) divide MSC into three dimensions: (i) sensing, (ii) sense-making, and (iii) response.

Sensing is related to the acquisition of information about consumers, competitors, and other members of the channel. This can be done by actively sensing events and trends in the market environment, collecting information systematically and regularly from various types of sources, and actively exchanging information with retailers. Sense-making refers to the interpretation of information collected against previous experience and knowledge. This can be done by interpreting information analytically, using a lot of time to analyze the information collected, and actively analyzing information before making marketing decisions. Response refers to the use of information collected and interpreted in decision making. This can be done by actively utilizing information about customer needs and intentions when making marketing decisions, utilizing information provided by sales and market share reports, using data provided by sales and market share reports, using data provided by profitability analysts, and utilizing information provided by company image studies (Ardyan, 2016; Lindblom et al., 2008).

Such sensing knowledge enables more accurate predictions about the market environment, the company’s commercial ability to influence its environment and the truth of planned actions taken by companies (Cohen & Levinthal, 1990 ; Alshanty et al., 2019) explained that market sensing is needed for active learning positions in SMEs. Information obtained from consumers, competitors, and most of the market, influences the way they explore, how they pay attention, and how they relate the results to the innovative and overall knowledge function. Two important components of MSC, such as CuSC and CoSC are integral parts that become the focus of this research. It is important to know the different roles of the two dimensions of market sensing in forming SMC and BP.

2.2. Customer Sensing Capability (CuSC)

The main purpose of a business is to make a profit, and this is possible only by winning over and maintaining customers. Consumers are individuals who have purchase products and services for personal use (Al-Azzam & Al-Mizeed, 2020). Customers are the only reason for the existence of a company, because without them, the company will not survive. Businesses have no choice but to compete for customers and listen carefully to what they say. Every time a customer buys something from one seller, they send a message to competitors about their wrong sales message and the competitor will try harder to win them over. Therefore, satisfying customers’ needs and demands is the key to excellent customer service and customer focus. In this case, the company needs to have the ability to survey customer satisfaction, analyze the results and make improvements, seek feedback from customers and take appropriate action. These are becoming the main ingredients in customer focused behavior.

The marketing literature has long advocated the importance of responding to customer needs for the long-term sustainability of a company’s competitive advantage (Jayachandran et al., 2004). Changes that occur in the customer’s operating system, the forces that shape competition, and the demands of customers and suppliers will increase the company’s need to work more closely with its customers. These changes have caused companies to increase capabilities to create more responsive and adaptive organizations, such as customer sensing capabilities to get information about the situation they are responding to and projections about the future. Customer sensing capability is thus related to the ability to know, understand, and meet the expectations of its customers. Such capabilities are very important to support closer buyer-seller relationships and provide high-level customer service.

2.3. Competitor Sensing Capability (CoSC)

Marketing management is the art and science of choosing target markets and obtaining, retaining, and growing customers through the creation, delivery, and communication of superior customer value. Although the main objective of marketing is to create superior customer value, the creation of value alone with customer focus is not enough. Customer value will only be obtained when the company is able to answer who its main competitors are, what technology is used by the main competitor, and whether the competitor is an alternative purchase of the company’s customers. This requires the development of a strong market intelligence to obtain important competitor information, such as how big is the sales turnover of competitors, the number of potential customers, the number of partners in the distribution network, the number of personnel they have, the extent of sophistication in the marketing technology of competitors, and the marketing mix strategy used.

Rapidly changing competition stimulates strategic resource allocation to maintain positional advantages in the market. This requires companies to pay attention and respond to market stimuli such as competitive movements and technological changes. When a company changes its market position, it can maintain its competitive advantage by implementing alternative action programs that are aligned with new market requirements to provide better customer value. Competitor sensing capabilities also allows companies to imagine new opportunities and gather knowledge about emerging technologies to renew the portfolio of strategic options, such as targeting new segments or adopting new technologies for customer value creation (Teece, 2007). For example, a company with CoSC can utilize the power of technology to quickly respond to market changes with new product offerings (Bharadwaj & Dong, 2014).

2.4. Market Sensing Capability and Specialized Marketing Capability

MSC is part of a company’s outside-in marketing capability that provides the knowledge structure needed to adjust its functional marketing capabilities to better serve in changing markets (Mu et al., 2018). MSC allows a company to detect and anticipate changes in market conditions and reveal current shortcomings (Mu, 2015). MSC allows companies to harmonize internal processes such as pricing, marketing planning, and new product development with market requirements (Day, 2011, Day, 2013). Thus, superior market sensing can provide companies with marketing insights about competition, customer needs, and technological trends (Morgan et al., 2009). Furthermore, companies can plan which products will be developed to meet customer needs, determine what new marketing communication strategies will be adopted, and how to construct greater and different brand images (Wiles et al., 2012).

H1: There is a positive relationship between CuSC and SMC.

H2: There is a positive relationship between CoSC and SMC.

2.5. Market Sensing Capability and Business Performance

Several studies have investigated the effect of MSC on company performance. Indicators of performance measurement with an internal business perspective consists of innovation, operations, and after sales service (Anggadini et. al., 2021). For example, MSC has a positive effect on the profitability of SMEs (Osakwe et al., 2016), and BP (Bayighomog et al., 2018; Hendar, 2019; Okwemba, 2018). These findings are based on the fact that companies entrenched with this capability can easily see changes in the market and take advantage of their competitors. Many scholars believe that MSC is an important determinant of performance, because of the inherent ability of MSC to choose and respond to certain market needs and preferences. This approach enables companies to satisfy their customers and achieve superior products, innovative and financial performance (Atuahene-Gima et al., 2005; Kohli et al., 1993; Narver et al., 2004).

H3: There is a positive relationship between CuSC and BP.

H4: There is a positive relationship between CoSC and BP.

2.6. Specialized Marketing Capabilities (SMC) and Business Performance (BP)

In general, the positive influence of marketing capabilities on BP has been well documented ( Ahmed et al., 2014; Kajalo & Lindblom, 2015; Morgan et al., 2009; Wu, 2013). Several dimensions of marketing capabilities, such as MSC (Day, 1994; Osakwe et al., 2016), brand management capabilities (Osakwe et al., 2016), CRM capabilities (Wang & Feng, 2012), and SMC (Morgan et al., 2009, Murray et al., 2011) are important variables that affect BP. Therefore, the potential relationship between SMC and BP is very likely considering that superior BP scenarios are only obtained when the company has SMC such as the ability to manage marketing mix of sales and market research. Thus, H₇ is proposed as:

H5: There is a positive relationship between SMC and BP.

2.7. Mediating Role of Specialized Marketing Capability (SMC)

The authors value the findings of Jifeng (2018) who explains that companies with greater capabilities to generate insights from market sensing, customer engagement, and partner relationships are more likely to develop superior inside-out marketing capabilities for improved performance. Inside-out marketing capabilities refer to a set of mixed marketing capabilities and interrelated organizational routines as used by companies to implement marketing strategies (Vorhies & Morgan, 2005). This is identical to specialized marketing capability according to Morgan (2009) and Trez and Fernando Bins (2012). Meanwhile, a key aspect of outside-in marketing capabilities is market sensing, which allows companies to detect and anticipate changes in market conditions and reveal current deficiencies (Mu, 2015). Superior market sensing can provide companies with marketing insights about competition, customer needs, and technological trends (Morgan et al., 2009). Thus, the company can design an increase in specialized marketing capability in the form of new products, pricing, determining marketing distribution and marketing strategies, increasing sales and building a more differentiated brand image (Wiles et al., 2012). Improvements in SMC will further improve business performance (Ahmed et al., 2014; Kajalo & Lindblom, 2015; Morgan et al., 2009; Wu, 2013).

Research conducted by Homburg et al., (2007), sort out market responses into customer and competitor responses. The authors need to sort MSC into CuSC and CoSC. CuSC relates to the company’s activities in detecting and anticipating changes in customer needs and desires, while CoSC relates to the company’s efforts to detect and anticipate changes in competitor’s strategy that considers the company’s position in the market. Thus, companies with greater capability in generating insights from CuSC and CoSC will more likely develop superior SMC to improve business performance. Thus, the authors hypothesize:

H6: SMC mediates the positive effects of CuSC on BP.

H7: SMC mediates the positive effects of CoSC on BP.

3. Research Methodology

3.1. Design and Sampling

A field study was conducted in Indonesia on all managers or owners of retail fashion SMEs. The authors did not find a harmonized population because the number of retail fashion SMEs keeps changing day by day. Hence, the authors tried to use as many samples as possible so that they could statistically represent the population. The number of samples was then determined based on the guideline of five times the number of parameters estimated (Hair et al. 2010), i.e. 250 respondents from 50 estimated parameters.

3.2. Data Collection and Analysis

Data was obtained from questionnaires distributed to 558 managers or owners of retail fashion SMEs in ten regions in Indonesia during June and July 2019, using sampling techniques. Four hundred and thirty-two respondents participated in this study (77.42%). The final evaluation of the questionnaire received after an inspection was damaged, and outlier data was obtained 330 questionnaires (59.14%) that were eligible for data analysis. The data came from 190 respondents who responded in less than one month and the remaining 140 over one month. The selected respondents consisted of 76.7% women and 23.3% men aged between 25 years and 50 years. Most of them are owners and managers of retail fashion SMEs who are married and have worked for more than 3 years. Most of their education level (65.1%) are high school or more, 10.7% Diploma and 24.2% Bachelor. Valid data were then analyzed using a combination of AMOS version 23.0 and SPSS programs to assess the theoretical structure of the factors investigated such as factor loading, goodness fit index, convergent and divergent validity, correlation, multicollinearity, data normality, and hypothesis testing. Furthermore, to test the role of the mediator, the authors used the Sobel Test.

3.3. Instrument

The survey was developed in two parts; first, it contained a demographic picture of the participating SMEs owners or managers and second, it contained questions for the research variables categorized into four sub-sections. The CuSC and CoSC constructs were adopted from (Taipo et al., 2008), SMC from Morgan et al. (2012) and Trez and Fernando Bins (2012), and BP from Jogaratnam (2017) and Hendar et al. (2017). All of them were adapted for the survey of retail fashion SMEs in Indonesia. The results obtained six initial instrument items for CuSC, six for CoSC, seven for SMC, and five for BP. The survey instrument of this study used subjective interpretations that are self-reported by the manager or owner of SMEs. Previous studies provide strong support for the implementation of MSC, SMC, and BP measures. A 10-point scale was used to obtain managerial assessments of the five constructs, 01 indicating a scale of “strongly disagree” and 10 showing a scale of “strongly agree” for statements proposed (Hair et al., 2010). Respondents were then asked to indicate their perceptions of CuSC, CoSC, SMC and BP over the past three years (see Table 1).

Table 1: Items, Fit Indices, Composite Reliability (CR), Average Variance Extracted (AVE) and Standardized Loadings

*** Significant at p < 0.001 (two-sided).

Fit statistics: GFI: 0.927; AGFI: 0.908; TLI: 0.965; CFI: 0.969; RMSEA: 0.041.

4. Results

4.1. Non-Response Bias Test

This study used an independent sample t-test statistic for the non-response bias test. This test is carried out to see the differences in the characteristics of the initial stage respondents, i.e. who responded to the questionnaire in the first month; with the final stage respondent, i.e. who responded to the questionnaire in the second month. The results showed, 190 data collected in the first month, and 140 collected in the second month. The total score of each construct was determined and differences calculated in the perception of initial stage respondents and final stage respondents. The p-value of the t-test which was not significant at α 0.05 showed no response bias between the initial and final stage respondents. The test results show the p-value in the t-test exceeds 0.05 for all constructs used. The insignificant difference between initial and final stage respondents shows that non-response bias does not cause serious concern (Armstrong & Overton, 1977).

4.2. Assessment of Normality and Multicollinearity

The assumption of data normality is checked by the skewness value (Table 1). The results of data analysis showed that skewness values of all indicators ranged between −0.384 and 0.153. This shows a reasonable assumption of normality based on the recommendation that the two values do not exceed the absolute value of 3 (Hair et al., 2010). Meanwhile, the variance inflation factor (VIF) is used to test the multicollinearity between free constructs. The results of data analysis showed that all VIFs ranged between 1, 342 and 1, 345 values are below the general threshold of 10.0. This shows that multicollinearity is not a serious problem (Mason & William, 1991). Based on this test, it is reasonable to conclude that the data do not violate the assumptions of normality and multicollinearity (see Table 2).

Table 2: Construct Reliabilities, Correlations and AVE

^aFactor reliabilities are on the diagonal (italic bold).

^bCorrelation Coefficient of Exogenous Construct **p < 0.01; *p < 0.05.

4.3. Reliability and Validity

The initial measurement model produced five items for CuSC, CoSC and BP, and six items for SMC (Table 1). The selected items are reviewed form each theoretical basis and are considered to adequately realize the theoretical constructs that represent the model. Reliability is assessed based on Cronbach’s alpha and composite reliability (CR) (Fornell & Larcker, 1981). All alpha coefficients exceed the threshold of 0.70 suggested by Nunnally (1978) and composite reliability that exceeds 0.6 so that it meets the level of acceptance for the reliability of each construct (Bagozzi & Yi, 1988). The measurement model is then checked and has an acceptable goodness of fit (Hair et al., 2010) with GFI, AGFI, NFI, TLI, and CFI above 0.90; and RMSEA that is not more than 0.05. The results show a goodness of fit model that represents good data because GFI = 0.927; AGFI = 0.908; TLI = 0.965; CFI = 0.969 are all above 0.90; and RMSEA = 0.041 is still less than 0.05 (Figure 1).

Figure 1: Structural Model

Convergent validity is determined by examining average variance extracted (AVE) of the correlation of each construct with other constructs. All standard loading factors for each item are also examined. All items were found to be significantly (p < 0.001) on a factor corresponding to a loading factor ranging from 0.644 to 0.842. AVE exceeding 0.50 indicates that the majority of variants are explained by constructs and not by measurement errors. This is in accordance with the recommended threshold of Bagozzi and Yi (1988) and is an indication of good construct convergent validity (see Table 2). Also, the square root of AVE for each construct is greater than the inter-construct correlation to confirm the validity of discriminant between constructs (Fornell & Larcker, 1981, Hair et al., 2010). In short, all tests used have supported the use of scale in this study.

4.3.1. Hypothesis Testing

Two types of regression analysis were used to estimate the impact of the dimensions of CuSC and CoSC on SMC and BP. The first regression describes the effect of CuSC and CoSC on SMC, used to test hypothesis 1 and 2. The second regression describes the relationship between CuSC, CoSC and SMC on BP used to test hypothesis 4, 5, 6 and 7. The test results showed that all hypotheses are accepted (Table 3).

Table 3: Parameter Estimate for the Path: Direct Effects

Note: *p < 0.05; **p < 0.01.

4.3.2. Post-Hoc Analysis: Mediation

The SMC mediation test in the relationship between the dimensions of CuSC and CoSC with BP follows the suggestion from Baron and Kenny (1986): first, the independent variable must influence the mediator, second, determine the effect of the independent variable on the dependent variable, and third, the mediator must influence the dependent variable. That means, the dimensions of CuSC and CoSC must influence SMC and SMC, must also affect BP. The Sobel test is then used to calculate the estimated indirect effect of the independent variable on the dependent variable through a mediator (Sobel, 1982). Mediation tests can describe the effects possessed by a set of independent and mediator variables on the dependent variable into direct and indirect effects (Jogaratnam, 2017). Mediation analysis involves partial mediation and full mediation. Partial mediation occurs when there is a direct relationship between the independent variable and the dependent variable, in addition to an indirect relationship through mediation variables. Full mediation occurs when there is no direct relationship between the independent variable and the dependent variable, while the indirect relationship through the mediating variable is significant (Jogaratnam, 2017; Rucker et al., 2011).

The mediation test procedure proposed by Sobel (1982) was adopted to examine the mediating effect of SMC (Table 4). Multiple regression analysis was carried out to assess each condition in relation to the proposed mediation model. P-value is determined as a measure of the significance of the relationship between the two variables. A p-value less than 0.05 indicates a significant relationship between the two variables. Then two regression models are set: first, SMC was found to be significantly affected by CuSC and CoSC and, second, BP is completely explained by CuSC, CoSC and SMC.

Table 4: Parameter Estimates for the Path: Indirect Effects (Sobel Test)

Note: *p < 0.05; **p < 0.01.

Based on the H6 test, the SMC acts as a partial mediation in the relationship between CuSC and BP. The direct effect of CuSC on SMC was explained by Unstd β = 0.437, S.E = 0.084 and c.r = 5.204 so that it was significant at α = 0.01. The direct effect of SMC on BP is explained by Unstd β = 0.319, S.E = 0.074 and c.r = 3.401 so that it is significant at α = 0.01. The indirect effect of CuSC on BP through SMC is explained by the Unstd coefficient β = 0.139 (0.437 × 0.319). The Sobel Test results show the value of c.r = 3.319, S.E = 0.042 and p-value 0.001 so that it is significant at α 0.01. The total effect of CuSC influence on BP through SMC is 0.44 (0.301 + 0.139) which is greater than the direct effect (0.301) indicating that SMC has a very important role as a partial mediation in the relationship between CuSC with BP and becomes an important alternative in increasing BP. Therefore, this study accepts H6.

In the H7 test, the SMC acts as a partial mediation in the relationship between CoSC and BP. The direct effect of CoSC on SMC was explained by Unstd β = 0.353, S.E = 0.078 and c.r = 4, 536 so that it was significant at α = 0.01. The direct effect of SMC on BP is explained by Unstd β = 0.319, S.E = 0.074 and c.r = 3.401 so that it is significant at α 0.01. The indirect effect of CoSC on BP through SMC is explained by the Unstd coefficient β = 0.113 (0.353 × 0.319). The Sobel Test results show the value of c.r = 3.121, S.E = 0.036 and p-value 0.002 so that it is significant at α = 0.01. The total effect of CoSC influence on BP through SMC is 0.343 (0.230 + 0.113) which is greater than the direct effect (0.230) indicating that SMC has a very important role as a partial mediation in the relationship between CoSC with BP and becomes an important alternative in increasing BP. Therefore, this study accepts H7.

5. Discussion

The purpose of this study is to examine the role of SMC in the relationship between the dimensions of MSC (i.e. CuSC and CoSC) with BP in the context of retail fashion SMEs in Indonesia. Based on the supporting marketing research arguments adopted, we hypothesize that CuSC and CoSC owned by retail fashion SMEs will provide opportunities to increase SMC and thus will increase BP. The results of this study confirm that all dimensions of MSC are not only important drivers for SMC but also BP. MSC that provides a structure of knowledge about customers and competitors is needed to develop and align SMC functionalities with market changes that occur. Excellent CuSC and CoSC can provide marketing insights to retail fashion SMEs about competition and customer needs (Morgan et al., 2009). CuSC and CoSC allow companies to harmonize internal processes such as pricing, marketing planning, and new product development with market requirements (Day, 2011; Day, 2013). Hence, the company then plans which products will be developed to meet customer needs, determine what new marketing communication strategies will be adopted, and how to build a more diverse brand image (Wiles et al., 2012). Under the same conditions, SMC is an important driver for increasing BP. This is in line with the findings that emphasize the role of marketing capabilities in increasing BP (Takata, 2016). Guided by the resource based view (RBV) which states the unique resources and marketing capabilities as a source of superior performance (Fahy & Smithee, 1999); SMC is a distinctive feature that is a differentiator for retail fashion SMEs because it can improve business performance.

The finding of this study also confirms the difference in effectiveness of CuSC and CoSC on business performance, both directly and indirectly. Directly, standard regression coefficient of CuSC on performance (β = 0.250) which is greater than the relationship between CoSC and business performance (β = 0.199) shows the role of CuSC in forming BP more dominant than CoSC. Indirectly, the total effect of the relationship between CuSC and BP through SMC (0.139) which is greater than the relationship between CoSC and BP through SMC (0.113) also shows the role of CuSC in forming SMC and BP is more dominant than CoSC. This indicates that, although the role of CuSC and CoSC is equally important in forming superior business performance, attention to customers is prioritized over competitors. The owners or managers of retail fashion SMEs seem to have to prioritize the search for customer information before competitor information. This is highly likely, considering that in the retail market, sellers can manage their competition well through collaboration. For example, a seller who gets consumers’ demand but does not have the product offered, customers will get from other sellers.

Theoretically, this research contributes to the development of strategic marketing science through examining the direct and indirect effects of the dimensions of MSC, which are CuSC and CoSC on BP that are transformed through SMC. This study found SMC as partial mediation in the relationship between CuSC and CoSC with BP. Furthermore, it also found different effects between CuSc and CoSC on BP directly and indirectly. According to RBV, positional superiority results from the capability of the organization to increase resources. This study is based on this perspective and found that CuSC and CoSC as marketing capabilities can be used to improve SMC in Retail Fashion SMEs. This is very possible considering the fashion industry is related to the elements of style and tends to survive in the short term (Christopher et al., 2004). Popular culture has a major influence on the formation of fashion trends, so the success of SMEs depends on the ability to respond to rapid changes in fashion trends and interpret them into products sold in stores in the shortest possible time (Bruce & Daly, 2006). This study confirms the statement of Mu et al. (2018) Murray et al. (2011) that SMC mediates the effect of MSC on BP. Therefore, the dimensions of CuSc and CoSC are an important part of improving SMC and hence have a positive impact on BP.

Based on empirical findings, the authors offer some insights on market-oriented SMEs fashion retail activities in Indonesia. First, Indonesian retail fashion SMEs use MSC development strategies in the form of CuSC and CoSC to encourage SMC and increase BP. Second, market sensing priorities focus more on finding information about the needs and demands of consumers rather than finding information about competitors’ strategies. Third, western marketing ideas, such as the dimensions of MSC (CuSC and CoSC), provide an opportunity for retail fashion SMEs in Indonesia to create a clear road map in developing marketing capabilities and continuing to improve business performance.

The CuSC culture can be built by actively following customer events and trends, collecting customer information systematically and analytically, spending a lot of time analyzing customer information, and utilizing customer needs and intentions. The CoSC culture can be built by continuously anticipating competitor actions, collecting competitor information systematically and analytically, spending a lot of time analyzing competitor information, and utilizing competitor information for marketing decisions. Cultivating such a culture can inspire the initiative of owners, managers and employees in increasing the capability of managing products, prices, distribution channels, marketing communications, sales, and market research.

6. Managerial Implications

The findings of this study, show that to build a strong SMC, retail fashion SMEs must be aggressive in developing CuSC and CoSC, with a priority on developing CuSC. This is necessary because demand patterns in the fashion industry are rarely stable or linear, tend to be short-lived, and the level of impulse buying is high. Customer and competitor knowledge gained from these activities can be used to reconstruct resources and carry out cross-functional processes in product development and various price, channel management, marketing communications, sales, market research, and customer relations activities. In other words, SMEs owners or managers must increase the integrated marketing mix, manage sales and carry out continuous market research to grow and survive in a very competitive market (Takata, 2016). Because the application of CuSC and CoSC leads to an increase in SMC and BP, the awareness of SMEs owners or managers of changing customer needs and demands and competitor strategies is very important. They must build a culture to ensure that elements of MSC are applied effectively.

7. Limitation and Future Research

Like many other studies, this study also has its limitations. First, the research model was tested in one country only, Indonesia, so that future research can expand the generalization of findings through examining the relationship of hypotheses with samples based in other countries. Second, this research model used SMC mediation in the relationship between CuSC and CoSC with BP, thus future research can examine the mediating effects of other capabilities such as architectural marketing capabilities, brand management capabilities, CRM capabilities, and NPD capabilities. Third, although this research has explained the role of the dimensions of MSC in the relationship between SMC and BP, it has not yet involved the role of other capabilities, such as the capability of utilizing technology, customer engagement or partner linking. The involvement of these three constructs in the development of this research model is likely to be needed in the future. Studying the effects of technology utilization capabilities, customer engagement or partner linking on SMC and BP is needed to see how these three capabilities will deliver business performance. Fourth, thes research focus is on retail fashion SMEs operating in highly fragmented and mature industries. Future research can broaden these findings and improve generalizations by conducting studies of SMEs in other industries, such as manufacturing, and services of small, medium and large scale.