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The role of market embeddedness in market scanning and marketing competence.

Smaller retail firms have been dramatically affected by the changing structure of retailing in the United States. Population shifts, demographic changes and current attitudes and lifestyles of U.S. consumers have led to retail activity becoming increasingly concentrated in large-scale retail formats (Brennan and Lundsten, 2000; Litz and Stewart, 2000). These changes have been especially devastating to independent retailers, and as a result, many small stores have closed, especially in smaller towns. The loss of independent retailers in small communities leaves towns with empty storefronts and residents with fewer shopping choices. While there is much written about structural changes in retailing in smaller communities, there is little empirical research to assist small retailers in these communities in developing appropriate strategies that will enable them to remain competitive.

The purpose of this paper is to examine the role of small retailers' local information networks in providing market information and developing marketing competence. Owners and/or primary managers of small retail stores employing less than 20 people in smaller communities (pop. 25,000 or less) were the focus of this study. To be successful, retailers must continually collect and synthesize information about consumer demand, competitor activity and economic changes in the local environment. Market scanning and market knowledge are critical in aligning a retailer's strategy with its customers' needs and wants (Darroch and McNaughton, 2002). Smaller retailers may not be able to rely on conventional information gathering strategies due to limited resources (Gilmore, Carson and Grant, 2001). Tapping into local information networks may be one way that small retailers can capitalize on their embeddedness in the market, giving them the ability to assess and respond to market demand more quickly and effectively than large-scale stores (Clow and Cole, 2004).

Prior to the current research, a qualitative study was conducted to learn more about how independent retailers in small communities use their networks to create competitive advantage (Frazier and Niehm, 2004). In depth interviews with highly successful small retail business owners revealed that small retailers rely heavily on informal social networks to access resources, especially information about local market conditions. The retailers in the qualitative study believed that they achieved greater success and competitiveness due to the constant flow of valuable market and customer information from multiple network sources. Based on the results of the qualitative study, this study goes on to propose that access to information-rich informal networks may allow retail decision makers to take advantage of emerging opportunities, meet customer needs in a unique manner, and develop distinctive operating capabilities. This study empirically tests three questions: first, do relationships matter in small retailers' information search; second, does access to high quality information build marketing competence; and third, do local relationships influence local marketing competence and lead to sustainable competitive advantage?

Literature Review and Hypotheses

Theoretical Framework

Much attention has been given in the literature to social factors that influence business success. This line of inquiry is framed in network theory, which suggests that social relationships provide valuable benefits that result in competitive advantage. This perspective has particular appeal for interpreting small retailer behaviors, which are best understood as embedded in the social construction of community (Cooke, Clifton and Oleaga, 2005). Local retailers may possess valuable market relationships that are available to them by virtue of their status and position in the community (Miller and Kean, 1997). Strong ties with local networks may allow independent retailers to react quickly to consumer demand by enabling them to access market information and synthesize it in a more responsive manner than their larger competitors (Litz and Stewart, 2000).

This study draws on Granovetter's (1985) embeddedness argument, which maintains that market exchange is affected by the values and norms of the social networks in which it takes place. Embeddedness suggests that individuals are motivated beyond purely economic goals to pursue the enrichment of relationships. Market embeddedness reflects the extent to which individuals are socially connected to the marketing environment in which they operate (Cooke, Clifton and Oleaga, 2005). Embedded relationships that are characterized by high levels of trust, friendship and support can produce benefits by providing access to more valuable information than can be attained by arms length relationships (Burt, 2004). Individuals who are well-connected in networks gain benefits by having access to relevant, timely information. The argument may be particularly useful in the context of small firm owners, as other studies have shown that information is often sought from informal and personal sources (Peters and Brush, 1996).

For small retailers, market information is a critical element in building knowledge and creating marketing competence (Arnett and Badrinarayanan, 2005; Li and Calantone, 1998). Marketing competence is a function of the fit between marketing strategy and com petitive advantage (Cavusgil and Zou, 1994). Competence is achieved by acquiring the information and skills that enable a retailer to develop and execute effective marketing strategies. To create competitive advantage, a retailer's competence must be superior to that of the competition, valuable to customers, and difficult to imitate. Possessing distinctive competencies has been positively associated with performance in small firm studies (Beaver and Jennings, 2005; McGee and Peterson, 2000). The ability to access and use information has been linked to competence in manufacturing (Real, Leal and Roldan, 2006) and production (Choe, Booth and Hue, 1997). The link between small retailers' market information and the development of marketing competence has not yet been investigated in the literature.

Infbrmation Networks

Networks are commonly used sources of information for small firms (BarNir and Smith, 2002). Market knowledge is derived from the information that flows into and out of these networks at different rates and volumes, depending on the nature of relationships in the network (Borch and Arthur, 1995). Family, friends and acquaintances, suppliers, customers, competitors, professional advisors and stakeholders, members of trade associations, local business and government organizations may be part of the information networks of small retailers (Frazier and Niehm, 2004).

Small business managers often rely on local informants for gathering market information, especially when information is not codified, or changes frequently (Hansen, 1999). Many do not see the need for systematic market research, because they "know the mar ket" (Gilmore, Carson and Grant, 2001). Small retailers, who tend to be resource-poor, are not likely to have access to sophisticated market databases (or see the need for them), and thus rely on face-to-face interaction to gather information about the local market environment. Ties with network members can influence performance by providing managers with richer, timelier information than can be attained by arms length relationships (Valkokari and Helander, 2007).

Market Information

Success as a small retailer hinges on the ability to recognize market opportunities and to respond by creating strategies to meet marketplace needs. This activity requires access to information in a time frame that results in competitive advantage (Griffeth, Noble and Chen, 2005). Small retail managers consider information about the local market (customers, local competitors, economic and social trends) to be the most important type of information used in business planning (Samli, 1998). Market knowledge and marketing skills are developed as a result of analyzing, synthesizing and applying information from the market (Belich and Dubinsky, 1999; Slater and Narver, 1995). A great deal of market knowledge needed by small retailers derives from information that resides in the local environment, and includes data about customers, competitors and local regulatory and institutional activity (Samli, 1998), as well as more general information about the local culture, economy, political landscape and social structure (Lord and Ranft, 2000). Information about the local market often changes rapidly, is not easily documented or formalized, and thus is difficult to replicate by newcomers (McAuley, Russell and Sims, 1997). Local market information offers insights into opportunities for market development and provides a focus for the retailer's merchandising strategies.

The value of information depends on its accuracy, relevancy, reliability, specificity and timeliness (O'Reilly, 1982). Information sources vary in their perceived ability to provide higher quality information (Schafer, 1990). In situations where the environment is uncertain and ambiguous, face-to-face information is often considered to be richer because of its ability to provide immediate feedback and multiple cues to interpret complex, subjective messages (Johnson, 2007). Small retailers may be able to obtain and interpret higher quality information about the local market by cultivating personal and business relationships that provide access to useful information in a timely manner.

Market Embeddedness

Embedded relationships are created through repeated social interactions and are generated anywhere there are individuals interacting in dense, lateral networks involving voluntary engagement, trust, and mutual benefit. Social capital is the outcome of embedded ties, and can be useful for enhancing learning, social mobility, economic growth, political prominence, or community vitality (Lin, in press; Runyan, Huddleston and Swinney, 2006). In the aforementioned qualitative study, small retailers attributed their personal ties with business and personal acquaintances and influential community leaders to their ability to get market information that others did not have. In this study, we go beyond the structural perspective to examine the emotional intensity of the social relationships present in networks and their effect on market scanning. Emotional intensity measures the closeness of a relationship. Indicators of emotional intensity include feelings of friendship, self-disclosure, help and support, shared interest, expression, trust and acceptance (Granovetter, 1985; Marsden and Campbell, 1984).

Market embeddedness may be useful in explaining differences in access to local market information as managers of small retail firms create and execute competitive strategies (Lee and Trim, 2006). Uzzi (1996) found that small firm embeddedness in social networks influenced information and resource exchange, which in turn affected economies of time, integrative agreements, allocative efficiency, and complex adaptation. Market embeddedness has been used as a perspective in examining the customer-retailer exchange process in smaller communities (Miller and Kean, 1997; Subramanian, 1993), and word-of-mouth in direct sales situations (Frenzen and Davis, 1990). Embeddedness may be especially relevant in retailing in smaller communities, where personal interaction is an integral part of conducting transactions.

Marketing Competence, Information Search and Performance

Marketing competencies are those capabilities that a firm performs especially well. For retailers, these competencies can be converted to competitive advantage by offering superior product assortments, better service, and more satisfying shopping experiences (Conant, Smart and Solano-Mendez, 1993). The relationship between performance and information search has long been noted in the literature. Dollinger (1984) showed that intensity of search was related positively to performance for small retailers. Peters and Brush (1996) found that scanning the environment for information related to competitors and market share was related to financial growth in small firms. Scanning intensity was related to growth in new manufacturing firms (Box, White and Barr, 1993). In this study, we suggest that the quality of local market information obtained through personal networks can be used to develop superior levels of marketing competence, influences firm performance. Figure 1 illustrates the hypothesized relationships between market embeddedness, market information, marketing competence and performance.



Strong social ties mitigate uncertainty and promote adaptation by increasing communication and information sharing. Strong network ties, characterized by close, trusting relationships, facilitate the flow of sensitive, complex and rapidly changing information (Burt, 2004; Granovetter, 1985; Uzzi, 1996). The assumption that strong network ties enhance the quality of information about the market leads to the first hypothesis:

H1: A small retailer's embedded relationships in local information networks will positively influence the quality of market information derived from the network.

Day (1992) suggests that continuous learning about markets contributes fundamentally to the achievement of competitive advantage. The ability of an organization to generate intelligence that informs business decision makers about customers' needs is recognized as an important source of competitive advantage (Slater and Narver, 2000). A small retailer's ability to use social capital in personal networks to accumulate market information may contribute to the development and sustainability of marketing competencies. Richer and timelier information about the nature of consumer demand and other local market intelligence may be accessed by individuals who have strong social ties to potential customers and others in the immediate marketing environment. Other research has linked information gathering activities with marketing competence. Beat (2000) and Kumar, Subramanian and Strandholm (2001) presented evidence relating information gathering activities and competence in adapting strategies to the environment. The second hypothesis to be tested is:

H2: The quality of market information obtained from small retailers 'local information networks will be positively associated with local marketing competence.

Social learning theory suggests that in addition to the learning that results from active information search, network connections provide opportunities for observation and evaluation of behaviors and attributes of network peers (Pineda, Lerner and Miller, 1998). Well connected small business owners may also attract other resources, such as reputational and financial capital, which may have a positive influence on the development of marketing competence (Miller and Kean, 1997; Ostgaard and Birley, 1996). Social skills have also been shown to influence network activity, and may also be related to business success (Mehra, Kilduff and Brass, 2001). The third hypothesis to be tested suggests that market embeddedness may also influence local marketing competence in a direct fashion:

H3: Embeddedness in local information networks will be directly and positively related to marketing competence.

The goal of building and sustaining marketing competence is to achieve a sustainable competitive advantage and thereby enhance a business's performance (Gibbert, Golfetto and Zerbini, 2005). Conant, Smart and Solano-Mendez (1993) found that small retailers who reported high levels of marketing competence also reported better performance. Li and Calantone's (1998) study indicated that market knowledge competence led to new product advantage and new product performance in software firms. The model tested in this study hypothesizes that higher levels of marketing competence will lead to better performance in small retail firms.

H4: Marketing competence will be positively related to perceptions of performance among small retailers.

In summary, our model hypothesizes that the quality of information that small retailers receive from local network contacts will increase as market embeddedness increases. Further, perceived marketing competence will have a positive effect on perceived firm performance, and will increase with the level of market embeddedness and the quality of market information.



The study used a written survey to assess relationships among variables. Tentative measures were borrowed or developed from the social capital, network and embeddedness literature. Market embeddedness was defined as the level of trust, friendship and support perceived to exist between the respondent and their self-identified network members. Marketing competence was conceptualized as retailers' perceived skill in responding to and communicating with customers, assessing competitive and other environmental threats. Six items from a scale developed by Conant, Smart and Solano-Mendez (1993) were used to measure competence. Information quality was measured using five items drawn from a scale developed by O'Reilly (1982), which measured the accuracy, relevance, specificity, reliability and timeliness of information. Performance was measured using subjective measures of growth, profitability and overall performance compared to industry and competitors, which are generally consistent with secondary performance measures (Venkatraman and Ramanujam, 1986). Construct definitions and proposed scale items were evaluated by a panel of academicians to determine wording and appropriateness of scale items. A preliminary version of the questionnaire was tested with a small sample of retailers, and modifications were made as a result. Table 1 summarizes the final scale items.

Level of Analysis

When measuring characteristics of a network, reliability is greater when the network is explicitly defined (Lin, in press). To frame variables in the context of a retailer's local information network, the survey instrument first directed respondents to identify individuals with whom they obtained local market information about customers, competitors and local market conditions by listing their first names or initials in a box provided on the instrument. Respondents were asked to refer to this list of people when answering questions regarding market embeddedness and information quality. Previous network research has found the recall method to be effective in predicting attitudes and opinions about relationships in a network (Kilduff and Krackhardt, 1994).

Sampling Frame and Sample

The sample was drawn randomly from a commercial database of owner/managers of independently owned gift stores in Midwestern states. Questionnaires were mailed to 949 store owners, along with a return envelope and letter explaining the study. One hundred nineteen questionnaires were returned, yielding a response rate of 12.7%. The response rate achieved in this study is in the same range (10.1% to 13.8%) as those achieved by other studies where small retailers are the participants (for example, Conant and White, 1999; Pioch and Byrom, 2004).

Of respondents 71% were female, and 26% were male. Three-fourths were college educated, and 80% were over age 40. Nearly half (45.1 %) of the respondents had owned their current business for over 10 years, and over two-thirds had more than 10 years experience in retailing. Firms were quite small, with 42% reporting that they had no full-time employees.

Construct Development and Hypothesis Testing

Exploratory factor analysis of 37 items intended to measure the market embeddedness construct identified eighteen items which loaded on three unique factors. Cronbach's alpha coefficients for each factor ranged from .71 to .91, which meets criteria for reliability (Nunnally, 1978). Each construct was then subjected separately to principal components analysis. In each case, only the first eigenvalue was greater than 1.0, which provides support for the unidimensionality of these scales.

Five-item information quality (IQUAL), six-item marketing competence (MCOMP) and three-item performance (PERF) scales were analyzed for reliability using Cronbach's alpha, and subjected to principal component analysis. Reliabilities ranged from 81 to .86, and each consisted of a single factor with an eigenvalue greater than 1.0.

Confirmatory factor analysis (CFA) was used to assess convergent and discriminant validity of the measurement model comprised of the eighteen observed variables representing the four constructs measuring market embeddedness. The goal was to identify a model where all observed variables loaded uniquely and significantly on to a single latent variable called market embeddedness (MEMB). Fit of the model was assessed using chi square goodness of fit. Because chi square does not perform well under conditions of small sample size and non-normal distribution, both of which characterize these data, we also assessed fit with the non-normed fit index (NNFI) and comparative fit (CFI) index. A value of .90 or greater was considered acceptable fit of the data to the model. Finally, we used the root mean square error of approximation (RMSEA), which takes into account the error of approximation in the population to assess the discrepancy between the model and population covariance matrix. As suggested by Byme (1998), RMSEA values less than .05 were considered to represent good fit of the model to the data. Exploratory post hoc model fitting was used to evaluate cross loading variables and produce a revised model. The revised measurement model produced acceptable fit to the data (chi square = 77.31 (df'71), p <.284; NNFI = .97; CFI = .98; RMSEA = .028).

CFA techniques were used to test the hypothesis that market embeddedness (MEMB) is a second order latent construct explaining the level of friendship, commitment and trust in an information network. Because use of equally weighted scales developed from the results of the CFA would yield a more acceptable variable-to-sample ratio, (Calantone, Schmidt and Song, 1996) the goal of this step was to create summated scales measuring TRUST, FRND and SUPP. Results of the second order CFA confirmed the hypothesis. Fit indices indicated acceptable model fit (chi square = 33.68 (df32), p<.039; CF1= .99; NNFI = .99; RMSEA = .022). In addition, the [R.sup.2] ranged from.52 to.79, suggesting moderate to high explanatory power of the first order latent constructs.

We then tested the full measurement model consisting of all observed variables and their hypothesized relationships to the latent variables. We used summated scales for TRUST, SUPP and FRND developed in the previous step to represent observed variables measuring the latent construct MEMB. Modification indexes produced from this analysis showed that model fit could be improved by eliminating one variable which measured information quality and two variables which measured marketing competence. Following re-specification of the model without these parameters, fit statistics indicated good fit of the confirmatory measurement model (see Table 2). All items in the final measurement model load significantly on their respective constructs at a significance level of .05, demonstrating adequate convergent validity (see Table 3).

Finally, we tested the hypothesized structural relationships. Table 4 presents the results of the initial solution. Fit indices, chi square and RMSEA statistics indicated acceptable fit of data to the hypothesized model (chi square = 84.45 (df' 73) p = .169 CFI =.97; NNFI .96). There were no significant standardized residuals. The root mean square error of approximation (RMSEA) was .038, which falls within the acceptable range suggested by Byrne (1998).

The path from MEMB to IQUAL was positive and significant (7 =.50, p <.01), which supports H1, and suggests that embeddedness in local information networks influences the quality of business information obtained from personal networks. The [R.sup.2] coefficient indicated that 25% of the variance in information quality is explained by the market embeddedness construct. The path between MCOMP and PERF was positive and significant ([gamma] = .47, p <. 01), supporting H4. Competence, which was defined as skill in assessing prospective and current customers, delivering quality customer service, and creating a pleasant shopping atmosphere had a positive impact on perceived performance, suggesting that competitive advantage is created by these competencies. The [R.sup.2] coefficient revealed that 22% of the variance in PERF could be explained by MCOMP.

The path from 1QUAL to MCOMP was positive, but not significant, therefore [H.sub.2] was not supported. In addition, the relationship between MEMB and MCOMP was not significant; therefore [H.sub.3] was not supported.


This study contributes to the small business literature in several ways. First, we measured social capital in small business networks using a unique perspective. In many social capital studies, networks are operationalized as inter-firm alliances, joint ventures, collaborative relationships with competitors or suppliers, or membership in trade organizations (Shaw, 2006). This view limits our understanding of small retailer networks and their benefits to the structural features of social ties. We took the view that social capital is more than a just the presence of network connections, and examined the effects of the social aspects of friendship, trust and support on access to resources. We allowed retailers to define their network contacts, and were then able to examine more specifically how the social aspects of their relationships with these people affected the flow of information about the local market.

The second contribution relates to the perceived value of information obtained from small retailers' social networks. The study found that strong personal ties between small retailers and their network contacts produced local market information that was perceived to be more relevant, accurate and timely. This finding provides some insight into one way in which small retailers scan the local market for business information. The study confirmed that retailers rely upon their close personal networks to gather critical market information. Small business managers in this study place a great deal of confidence in the market information obtained from their local information networks. As social capital increases in these networks, small retailers' belief that the information obtained from these contacts was of good quality also increases. This supports findings from other small business information search studies that have found that small business owners rely on their personal networks for advice and information (Ostgaard and Birley, 1994).

Our findings support studies in other exchange contexts that have linked individual elements of embeddedness to the flow of information in a network. For example, Tsai and Ghoshal (1998) found that trustworthiness was positively associated with resource exchange among business units within a firm. Currall and Judge (1995) found that trusting work relationships were associated with information sharing among network members. In another study, Halpern (1996) found that real estate agents relied on friendly relationships to help them understand and use information that they obtained in a business context. Results also support other research that suggests that exchange is influenced by reciprocal intentions of network members, providing better access to tacit knowledge (Portes and Sensenbrenner, 1993). Many network studies which are focused on small firms define networks in the context of inter-organizational networking, such as supplierbuyer networks, competitor networks, or trade association membership. This study expands on the previous ones by focusing on embeddedness as a multidimensional concept in network relationships, and demonstrating that embeddedness has an impact on the quality of information obtained from network sources.

Respondents' assessment of their marketing competence relative to competitors was positively related to perceptions of performance. This finding confirms other studies' findings that marketing competence is associated with better performance (McGee and Peterson, 2000). It suggests that developing marketing competence in these areas is a valuable activity, and should be a central focus of a local retailer's strategy.

Other studies have found a link between market knowledge and marketing competence (Griffeth, Noble and Chen, 2006; Li and Calentone, 1998). Although there was a positive link between information quality and marketing competence, the relationship was not significant; therefore we could not confirm that better information leads to the development of marketing competence. Several possible explanations may exist for the lack of relationship between information quality and marketing competence. First, although retailers place high value on the information they receive, they may not be effectively transforming the market information they obtain from personal networks into marketing competence. As noted in Li and Calantone (1998), there is a difference between market knowledge (stock of information about the market) and market knowledge competence (processes that generate and integrate market knowledge). Developing competence in knowledge of a market may require more than accumulation of market information, but also skill in integrating marketing information into marketing activities. Day (1992) suggests that being able to acquire market information is a key component of a successful business, but that being merely being a recipient of information is not enough. Business managers must effectively interpret the information they receive, and develop structured plans for using it. Because small retailers must constantly juggle all of the functions of their businesses, they may not have the time, skill or resources to transform market information into market competence.

Network theory could explain the weak relationship between information and competence. Granovetter's (1985) classic argument is that while close relationships may facilitate the flow of information, the information contained in strong tie networks may not be valuable for capitalizing on opportunities in the market. Network members who are too dependent on embedded relationships may fail to recognize new trends and opportunities in the market. Burt's (2004) structural holes theory contends that information found in close, dense networks is not valuable in providing high returns in the market due to its redundancy. Although small retailers in this study perceived that the information from their networks is of high quality, it could be that it is not the "right" information needed to develop marketing strategies. Perhaps the information that is obtained from the network is the same information that is available to competitors, and therefore not valuable in creating distinctive competence. Resource theory suggests that competitive advantage can only be achieved when a skill or asset is unique and inimitable (Barney, 1991). Small retailers may also need to look outside of their strong network ties for market knowledge that will provide an advantage in the market. They may need to approach market information gathering in a more systematic and thorough manner to achieve the benefits of being local. They may also benefit from further training that would give direction on collecting information from multiple sources and converting market knowledge into effective marketing strategies and tactics.

Practical Implications

Small retailers face a multitude of challenges in an environment populated by big-box and powerful chain stores with buying power and name recognition. To compete against more powerful competitors, small retailers are advised to tailor merchandise assortments to the local market and focus on providing personalized customer service (Thall, 2007). Effective execution of these strategies requires a deep knowledge of customers' preferences, and the ability to react more nimbly than their larger competitors. However, small businesses often lack the time and resources to conduct formal market research to guide their marketing plans. Building strong community relationships may be one way that small retailers may be able to tap into valuable intelligence about market conditions, customer preferences and competitor activities. Small retailers who are socially active in the community may be able to better meet the needs of local consumers. By focusing on information that is available in local networks, small retailers may be able to react more quickly and directly to customers' needs. This means that small retailers must recognize the value of information embedded in local network relationships, and develop mechanisms that synthesize knowledge into actionable marketing plans. Operationalizing this advice may be as simple as keeping a daily log of observations and information obtained through network interactions, and then regularly analyzing the information to identify key issues and action items. Market information is only valuable if it is used to improve an organization's ability to meet customers' needs; therefore small retailers need to focus not only on acquiring information, but using it in a way that will lead to increased performance.

Limitations and Direction for Future Research

This study focused on business information networks for specific types of information, thus, it would not be appropriate to extend these findings beyond these network definitions. As the study examined a single sector of the retail industry with respect to product lines and geographic location, these results cannot be generalized to other types of small retailers, or to retailers operating in larger communities. Small retailers in smaller communities may have very different network structures than their counterparts in urban areas. Implications may also be limited only to retail firms, as patterns of information search may be unique to the retail environment. The small sample size, low response rate and narrow scope of the sample that characterized this study also limit the ability to generalize results to other settings.

Using techniques that allow comparison of social relationship patterns of small retailers can generate interesting questions, but, as no previously established scales exist to measure market embeddedness, further refinement of measures is warranted. The method used to define networks in a survey research setting was also previously untested and needs replication in other business environments. Identifying other types of resource networks and their benefits would also be a valuable line of inquiry. For example, networks that offer personal support and advice may also be instrumental in the firm's success by providing the emotional support necessary to sustain entrepreneurial activity, especially in start-up stages. The link between access to information and being able to use it to become more competent in important marketing skills also needs further investigation. Experience, education, cognitive ability and motivation may contribute to the transformation of market information into skill.


Independent retailers are an integral part of the economy, and research needs to continue to focus on ways to better understand their performance. Business consultants can use information about small business market scanning behaviors to better understand how small retailers gather information for strategic information. Many small business training programs encourage small business owners to network, but this advice is usually in the context of joining the local chamber of commerce or trade organization (Small Business Administration, 2007). Small retailers may also need more guidance relating to ways of using the information received from more informal connections. Working on ways to systematize market information gathered from informal sources may be needed to take full advantage of the rich information available through personal contacts. The importance of informal networking in building strong retail sectors in small communities should continue to be emphasized. As one forecaster put it, large firms are like boulders that are dropped in a hole, and entrepreneurial opportunities are the spaces created between the boulders (Williams 1999). As independent retailers continue to confront a more challenging competitive environment, they may need to use their social networks to more effectively mine the spaces between the boulders.


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Contact Information

For further information on this article, contact:

Barbara J. Frazier, Western Michigan University, Department of Family & Consumer Sciences,

1903 West Michigan Avenue, Kalamazoo, Michigan 49008-5322

Telephone: 269-387-3719/FAX: 269-387-3353

Barbara J. Frazier, Western Michigan University, Kalamazoo, Michigan

Patricia Huddleston, Michigan State University, East Lansing, Michigan.
Table 1. Final measures

                                                Factor    Cronbach's
                                                loading     alpha

Friendship (FRND) 5 = strongly agree;                        .91
1 = strongly disagree

V1    I do things socially with these people.    .651
V2    If I had the chance, I would spend a       .643
        free afternoon with these people.
V3    These people would trust me with           .687
        personal information about
V4    It is likely that you might ask them       .857
        for advice about a personal matter.

Trustworthiness (TRST) 5= strongly agree;                    .71
1 = strongly disagree

V7    I am considered to be dependable by        .662
        these people.
V8    These people would say that I am           .829
V9    They would say that I am a trustworthy     .828

Support (SUPP) 5= strongly agree;                            .88
1 = strongly disagree

V10   If any [people in my network] had          .798
        information that would help me in
        my business, they could tell me
V11   I aim one of the first to hear about       .751
        new things from [people in my
V12   I frequently talk to [people in my         .854
        network] about business topics.

Information Quality (IQUAL)                                  .86

V20   Sometimes the information we get may       .877
        get right to the heart of the problem
        we are facing. Other times the
        information may not be very specific
        to our needs. In general, how
        relevant is the information from
        the people you named above? (1 = not
        at all relevant; 7 =very relevant)
V21   At times we must gather a lot of           .915
        information, which isn't very
        relevant in order to get enough to
        make a good decision. Other times we
        need only a small amount of
        information because the information
        is very specific and allows us to
        make a decision. How specific is the
        information you get from the people
        you named above? (1 = not at all
        specific; 7 = very specific)
V22   Some information may be exactly what we    .865
        require. How often is this the case
        for information obtained from the
        people you named above? (1 = not
        often; 7 =very often)
V23   To be useful, information must often be    .860
        available when we need it, not at
        some later time. How timely would you
        estimate information to be from the
        people you named above? 1 = not very
        timely; 7 = very timely)

Marketing Competence (MCOMP) 1 = not as strong               .81
as competition; 7 = much strong than

V24   Assessment of current customers' needs     .793
        and wants.
V25   Assessment of prospective customers'        819
        needs and wants.
V26   Quality of customer service.                804
V28   Creating a pleasant shopping                810

Performance (PERF) a = .81
(Door; 5 = excellent)

V30   How would you describe the overall         .824
        performance of your store(s)
        last year?
V31   How would you describe your performance    .825
        relative to your major competitors?
V32   How would you describe your performance    .767
        relative to other stores like yours
        in the industry?

Table 2. Summary of specifications and fit for tested final
measurement model

                  deleted     [chi square]   df   p value   CFI

Initial Model       --           136.7       98    .002     .93
Revised         V19->IQUAL
Model           V27->MCOMP       84.14       71     .14     .96

                NNFI   RMSEA   [chi square]   [DELTA] df

Initial Model   .94     .06         --            --
Model           .97    .041       52.56           27

Table 3. Parameter estimates for final measurement model

Construct   Path label     estimate    t-value

MEMB        SUPP-MEMB        1.00
            TRST-MEMB         .51        4.83
            FRND-MEMB        2.03        5.01
IQUAL       V20-IQUAL        1.00
            V21->IQUAL       1.14       10.05
            V22->IQUAL       1.20        9.33
            V23->IQUAL        .91        7.76
MCOMP       V24->MCOMP       1.00
            V25->MCOMP       1.00       10.98
            V26->MCOMP        .44        5.27
            V28>MCOMP         .21        2.39
PERF        V30->PERF        1.00
            V31->PERF        1.35        8.11
            V32->PERF        1.17        7.90
            MEMB, IQUAL
            MEMB, MCOMP
            MEMB, PERF
            IQUAL, MCOMP
            IQUAL, PERF
            MCOMP, PERF

                             Std        Std
Construct   Path label     estimate   residual

MEMB        SUPP-MEMB        .67        .55
            TRST-MEMB        .63        .61
            FRND-MEMB        .72        .55
IQUAL       V20-IQUAL        .82        .33
            V21->IQUAL       .82        .25
            V22->IQUAL       .81        .35
            V23->IQUAL       .70        .51
MCOMP       V24->MCOMP       .95        .10
            V25->MCOMP       .89        .19
            V26->MCOMP       .48        .77
            V28>MCOMP        .24        .95
PERF        V30->PERF        .74        .45
            V31->PERF        .86        .25
            V32->PERF        .80        .35
            MEMB, IQUAL      .50
            MEMB, MCOMP      .10
            MEMB, PERF       .10
            IQUAL, MCOMP     .12
            IQUAL, PERF      .15
            MCOMP, PERF      .47

[chi square] = 84.14, (N=112, df 71), p = .14 CFI =
.97 NNFI = .96 RMSEA = .041

Table 4. Parameter estimates for structural model

               Parameter               Std        Std
Path label     estimate    t-value   estimate   residual    R2

SUPP->MEMB       1.00                  .72        .55      .52
TRST->MEMB        .51       4.83       .63        .61      .39
FRND->MEMB       2.02       5.01       .67        .48      .45
V20->IQUAL       1.00                  .81        .34      .66
V21->IQUAL       1.15      10.05       .87        .25      .75
V22->IQUAL       1.21       9.33       .81        .34      .66
V23->IQUAL        .91       7.33       .70        .51      .49
V24->MCOMP       1.00                  .95        .11      .89
V25->MCOMP       1.00      11.07       .89        .18      .82
V26->MCOMP        .44       5.28       .48        .77      .23
V28->MCOMP        .21       2.40       .24        .95      .05
V30->PERF        1.00                  .74        .45      .55
V31->PERF        1.36       8.08       .87        .25      .75
V32->PERF        1.17       7.86       .80        .36      .64
MEMB->IQUAL       .32       3.81       .50                 .25
MEMB->MCOMP       .04        .39       .06                 .02
IQUAL->MCOMP      .13        .78       .10
MCOMP->PERF       .25       4.31       .47

[chi square] 84.45 (n=112, df 73) p = .169 CFI =.97
NNFI = .96 RMSEA = .038
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Author:Frazier, Barbara J.; Huddleston, Patricia
Publication:Journal of Small Business and Entrepreneurship
Article Type:Survey
Geographic Code:1USA
Date:Mar 22, 2009
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