Retail buyer’s new product adoption decisions:
The effect of the salesperson-retail buyer relationship on adoption success.
University of Groningen 2013 Faculty of Economics and Business
Marc Kloppers s1710273 Kleine Vosstraat 8
6971 KK Oeken 0638653862
Abstract
This study advances the growing body of literature on retail adoption by focusing on the objective variables of the relationship between a manufacturer’s salesperson and a retail buyer during the process of negotiating the listing of a new product. Furthermore, scales for measuring adoption success more precisely are developed. Based on an empirical study, with a sample of 100 adoption negotiations, conducted in the Dutch Do It Yourself industry, results indicate that higher relationship quality and a greater amount of prior adoption negotiations in a relationship between a manufacturer’s salesperson and a retail buyer are associated with higher adoption success. Through a rich explanation of relevant variables in the relationship between manufacturer salespersons and retail buyers and empirical assessment, this study contributes to a better understanding of how these relations should be managed in order to increase adoption success. The insights from this study also are relevant in the field of product innovation management because for product development processes to be gainful, often a successful adoption process with a retailer is imperative. Key words: retail adoption, innovation, new products, product development, buyer-supplier relationship, retail buyer, salesperson
Introduction
One of the critical factors for the long-term success of firms is that firms remain innovative and constantly introduce new products/innovations (Hultink and Robben, 1995). Introducing new products helps develop customer loyalty and rejuvenates old product portfolios (Lin and Chang, 2012). Today, many manufacturers don’t sell their products directly to the end user but sell them through (physical) retail channels. Therefore, their success depends largely on a retailer’s acceptance and support of new products (Kaufman et al., 2006). For a retailer, constantly adopting new products from manufacturers is beneficial because it creates increased customer traffic and differentiation of product offering and also signals that the retailer supplies the latest and best products (Lin and Chang 2012). However, for retailers it is not easy to adopt all new products that are presented to them. Two main reasons for this are the considerable risk faced by retailers to adopt new products because of their high failure rate (U.S. Federal Trade Commission, 2003) and the scarcity of shelf space (Bloom et al, 2000). To decide what new products, that are presented to a retailer, to adopt is considered to be critical for the retailer’s market success (Hultink et al., 1999) and indirectly influences the manufacturer’s success because if a retailer doesn’t adopt new products from the manufacturer, the manufacturer can’t sell its products through the (physical) retail channel. This makes the manufacturer and retailer interdependent on each other and therefore the issue of new product adoption at the retail level becomes more and more a topic of both managerial and academic interest.
In prior studies, researchers have investigated how retailers evaluate new consumer products by mainly focusing on product and market factors (Montgomery, 1975; Rao and McLaughlin, 1989). Despite the conclusion of Srivastava et al. (1998) that considering the role of relationships in new product acceptance is critical because these relationships are
market based assets with the potential to create long-term value to shareholders, successful new products and increased profits (Stump et al. 2002), only a few researchers have investigated the role of relationship variables in new product acceptance (Kaufman et al, 2006; van Everdingen et al., 2011; Lin and Chang, 2012). Kaufman et al. (2006) investigated the impact of the quality of buyer-salesperson relationships and firm-firm relationships on the likelihood of new product acceptance. Van Everdingen et al. (2011) built on these insights by adding the effects of the dependence of the retailer on the manufacturer and the length of the relationship between the retailer and manufacturer. Lin and Chang (2012) specifically investigated the effect of trust, dependence and effective communication on the retailer’s acceptance of a new product. Although these studies all contributed to the retail adoption literature, they ignored the contact frequency between a salesperson and retail buyer, the amount of prior adoption processes in a relationship, the experience and expertise of a retail buyer and they all looked at adoption success in terms of state rather than extent. Since the quality and degree of an adoption can vary, this concept should be defined more precisely. Also, attention should be given to the possible effects of type of
innovativeness, that is the innovativeness of a new product. In large multinationals often
local departments have little influence on what innovations are developed by company headquarters. However, local departments are accountable for introducing these innovations successfully in local markets. Therefore it would be quite helpful to know if and how the innovativeness of an innovation influences the objective measures of a relationship and how to manage the relationship accordingly. By gaining these insights, new product development processes (such as for instance Cooper’s (2008) stage-gate system) could be improved since the adoption of a new product by a retailer essentially is part of the launch-phase. Furthermore, this study focuses on the Dutch Do It Yourself (DIY) retailing industry instead of the grocery industry where most of the prior studies are focused on (White et al., 2000; Rao and McLaughlin, 1989; Montgomery, 1975; Kaufman et al., 2006; van Everdingen et al., 2011). This study attempts to fill in the noted gaps in the existing retail adoption literature. In order to achieve this, a quantitative research is conducted in the Dutch DIY industry. In total, quantitative data is collected from over 100 cases and several interviews are held to obtain qualitative data for the operationalization of variables. This data helps to further contribute to the existing literature on retailer adoption decisions and helps to provide managerial insights. The remainder of this paper is structured as follows. First, the theoretical background and concepts in the retail adoption literature and relationship marketing literature are reviewed and key constructs are derived. Then a conceptual framework is created followed by an empirical model and method to obtain data for this study. To conclude this paper, a discussion of the study’s results, implications, limitations and future research directions will follow. In this article some terminology will be used interchangeably. Because the term ‘salesperson’ often is used in current literature but the firm where empirical research is conducted uses the term ‘account manager’ these terms will be used interchangeably. Furthermore, if the term ‘new product’ is used also ‘innovation’ can be read and vice versa.
Conceptual Framework
Adoption Success
To give an indication of shelf-space scarcity, between 1985 and 1992, the number of products in the consumer goods industry increased by 16% per year while shelf space increased by only 1,5% per year during this period (Quelch and Kenny, 1994). Implications of the persistent shelf-space scarcity are that retailers have to manage their shelf space. This process also is referred to as category management and it is a process used for managing entire product categories as business units (Kurtulus and Toktay, 2011). In implementing category management, the retailer usually first determines the category shelf space on the basis of category goals and then gives a category manager the responsibility to make within-category decisions such as what new products to adopt in his within-category. A product within-category can be defined as a group of products that consumers perceive to be interrelated and/ or substitutable. (Kurtulus and Toktay, 2011). Because many categories need to be placed in limited amounts of store space the relative profitability of each category is scrutinized by retailers to determine how much space to allocate to each category (Chen et al., 1999). Category management traditionally contains decisions made by the retailer and therefore is referred to as “retailer category management” (RCM). In today’s market however, some retailers are outsourcing category management to their leading manufacturers. This process is also known as “category captainship” (CC) (Kurtulus and Toktay, 2011). In CC, the retailer shares important information such as sales data, pricing, turnover and shelf placement of the brands with the category captain, which in return analyses this data and delivers a detailed plan including recommendations about which brands to include in the category, how to price single products, how much space to allocate to the brands that should be included in the category and where to position the products on the shelf. Subsequently the retailer decides what recommendations it adopts (Kurtulus and Toktay, 2011). Even when retailers use the more traditional RCM method, manufacturers could still provide the retailer with the kind of recommendations mentioned above and try to influence category managers’ decisions regarding category management. In this way they possibly increase sales volume at the expense of competitors. Therefore influencing the category manager could be of high importance. In the current literature about retail adoption success this is not taken into consideration. Until now, adoption success has been only looked at in terms of state; if a product is adopted by the retail buyer, the adoption process is called a success. If a product is not adopted by the retail buyer, the success is nil (Kaufman et al., 2006; van Everdingen et al., 2011; Montgomery, 1975; Heeler et al., 1973; Rao and McLaughlin, 1989; White et al., 2000). In this study, however, adoption success is determined by an assessment of the position of a newly adopted product on the shelf, the amount of shelf space that is reserved for the new product and whether or not the newly adopted product replaces a competitor’s product. Adoption success (from a manufacturer’s point of view) is thought to be high if a newly adopted product is positioned on the preferred position on the shelf, takes the right amount of space and replaces a competitor’s product instead of a product from the manufacturer itself. What the preferred position and the right amount of space will be depends on many factors and is quite subjective. However, if a manufacturer negotiates a new product launch with a retailer, often these factors are taken into consideration and preferences will exist.
Retail Buyer - Manufacturer Relationship Relationship Length
In prior research the relationship between a manufacturer and a retail buyer is highlighted because of its effect on the retail buyer’s evaluation and acceptance of a new product (Hultink et al., 1999; Kaufman et al., 2006). Because the retailer faces some risk when adopting new products (because of product’s high failure rates) and the manufacturer needs the retailer to sell its products, retailer and manufacturer are interdependent on each other. Therefore it is likely that both parties would be motivated to create close relationships (Lin and Chang, 2012). These relationships can be divided into two categories; firm-firm relationships and salesperson-retail buyer relationships. Whereas a firm-firm relationship may be of a longer duration and therefore can be seen as a long-term “shadow” of the future (Heide and Miner, 1992), the retail buyer - salesperson relationship is likely to be of shorter time duration because of turnover or reassignments (Kaufman et al., 2006). However, as relationships are of longer duration, one might expect a retail buyer to be more familiar with a salesperson and through developed common norms and stronger feelings of commitment will be more likely to adopt new products offered by the salesperson. Also, because of shared knowledge, manufacturers are expected to offer products that better fit the needs of retailers (van Everdingen et al., 2011). In various studies in the fields of psychology and marketing, the time dependent effects of relational constructs are investigated (Doney and Cannon, 1997; Swann and Gill, 1997; Verhoef et al., 2002). According to Berscheid et al. (1976) people in long-term relationships have more opportunities to gather information about one another. Furthermore Murray and Holmes (1993) state that people in long-term relationships have more motivation to integrate gathered information into coherent representations compared to people that are in the early stages of relationship development. In the field of marketing, the study of Anderson and Weitz (1989) shows that long-term relationships are more stable than younger relationships. The underlying reasoning for this is that time allows for unsatisfactory relationships to end and in surviving relationships adjustments can be made which leads to a higher degree of fit (Anderson and Weitz, 1989) or familiarity (Verhoef et al., 2002). As relationships continue, the greater will be the investments parties make in the relationship (Doney and Cannon, 1997). Relationship investments represent value to exchange partners and therefore as the length of a relationship increases, the relationship value is likely to increase too (Palmatier et al., 2006). When common norms and strong feelings of commitment are present and a retail buyer is well informed by a salesperson, retail buyers are expected to easier comply with preferences of a salesperson with respect to adopting a new product, shelf position, amount of shelf space and the replacement of another product. Also, complying with the preferences of a salesperson can be described as some form of investing in the relationship. Therefore it is hypothesized:
Frequency of Contact
Frequency of contact can be defined as the number of interactions per period between
exchange partners (Crosby et al., 1990; Doney and Cannon, 1997; Palmatier et al., 2006). Following Dagger et al. (2009) contact frequency will be measured as the estimated number of salesperson-buyer interactions over a period of one year. In this study interactions are not limited to face-to-face interactions but also include e-mail, video-conference and telephone interactions. Although frequency of contacts is an objective measurement it is closely related to factors such as trust, commitment and effort that are present in a relationship. In their study Nicholson et al. (2001) investigated the effect of frequency of personal interaction on trust, mediated by liking. They state that with an increase in the frequency of interaction, information can be exchanged between partners more easily. With an increase in the frequency of interaction, partners can also predict each other’s behavior more easily due to increased time spent together (Doney and Cannon, 1997). Furthermore Nicholson et al. (2001) state that the frequency of interaction reflects both commitment and effort to the relationship which are important because effort is found to be a key determinant of relationship continuity (Crosby et al., 1990). In studying business contacts mainly, Crosby et al. (1990) further found a positive relationship between contact intensity and trust, which is an important construct in relationship quality (Lin and Chang, 2012; Kaufman et al., 2006). If there is limited interaction between a salesperson and buyer it is not likely that a strong relationship will be developed (Dagger et al., 2009). Because with an increase of contact frequency information exchange will be easier, partners are able to make better predictions of each other’s behavior, and commitment and effort will increase, it is hypothesized:
Hypothesis 2: The higher the frequency of contact in a relationship, the higher will be the
adoption success.
Prior Adoption Processes
In this study the frequency of contact is closely related to the amount of prior adoption
processes in the relationship between a salesperson and a retail buyer. An adoption process
in this study is the entire process from the first time a salesperson introduces a new product to a retail buyer till the adoption or non-adoption of the new product by the retail buyer. As Doney and Cannon (1997) found that one of the benefits of an increase in the frequency of contacts between a buyer and salesperson is that buyers have more opportunity to observe the representative’s behavior and therefore can better predict outcomes or behaviors in future interactions. This in turn has a significant effect on trust (Doney and Cannon, 1997). In this study it is expected that an increase in the amount of prior adoptions in a relationship between a salesperson and a retail buyer can help a salesperson to predict outcomes of future adoption processes and behavior of retail buyers better in the future. Besides the regular subjects that are dealt with during contacts between salespersons and retail buyers, the contacts that specifically are about adoption processes will also increase. This in turn, will help to make information exchange easier, better predict each other’s behavior and increase commitment and effort. Therefore it is expected that an increase in prior adoption processes will lead to a higher adoption success.
Hypothesis 3: The higher the amount of prior adoption processes, the higher the adoption
success will be.
Perceived Relationship Quality
When investigating relationship quality in the specific context of product adoption this relationship quality usually is measured at the retail buyer side of the relation (Kaufman et al., 2006; van Everdingen et al., 2011; Lin and Chang, 2012). In these studies the effect of relationship quality on adoption success is investigated. However, relationship quality is a very broad term that includes multiple elements. In the retail industry many deals are initially made verbally (Kaufman et al., 2006). One important aspect of relationship quality when making deals without relying on formal contracts is trust (McEvily et al., 2003). When a party has confidence in an exchange partner’s reliability and integrity this can be described as the party trusting the exchange partner (Kaufman et al., 2006). When salespersons and retail buyers develop trust, they are more likely to coordinate their efforts in new product sales, to reduce perceived risk and to take advantage of complementary skills in order to decrease transaction costs (Lin and Chang, 2012). Furthermore, when a retail buyer has memories of previous successful transactions and perceives the relationship quality as being high, this will likely diminish the perceptions of the amount of risk involved in making a deal (Lin and Chang, 2012). The quality of the relationship therefore could serve as a heuristic, which is an experience-based technique for problem solving (Cigerenzer, 1991), when making new product decisions (Kaufman et al., 2006). Besides trust, another important part of relationship quality is effective communication (Lin and Chang, 2012). It is a necessity for parties that want to create mutual support, respect and compliance between them (Mohr et al., 1996).
One of the findings of Lin and Chang (2012) in their study is that trust, commitment, and effective communication are positively associated with the retailer’s acceptance of a new product. Therefore if relationship quality increases it will have a positive effect on a retail buyer’s acceptance of a new product.
Hypothesis 4: The higher the relationship quality the higher the adoption success will be.
Type of Innovativeness
As mentioned earlier, in today’s competitive environment it is essential for firms to remain innovative. Innovation can be described as an iterative process that is initiated by the perception of a new market opportunity for a technology-based invention which leads to development, production, and marketing tasks striving for the commercial success of the invention (Garcia and Calantone, 2002). This definition implies that an innovation process combines the technological development of an invention with a market introduction of that invention to end-users through adoption and diffusion. Also, besides basic and applied research, innovation includes product development, manufacturing, marketing, distribution, servicing and in a later stage, product adaptation and upgrading.
If a discovery never leaves the laboratory it remains an invention. If a discovery, however, moves into production and provides economic value to a firm it becomes an innovation. An
innovation thus differs from an invention by providing economic value to a firm and by the diffusion to other parties than the discoverers (Garcia and Calantone, 2002). The term innovativeness usually is used to describe the degree of newness of an innovation. However, different authors have different views on whose perspective to use in order to determine the degree of newness and what exactly should be seen as new. Following Garcia and Calantone (2002) in this study product innovativeness is a measure of the potential discontinuity that a product can generate in the marketing and/or technological process. From a macro-perspective, this discontinuity can be a shift in the science and technology and/or market structure in an industry. From a micro-perspective, this discontinuity can be a change in the firm’s existing marketing resources, technological resources, skills, knowledge, capabilities or strategy. In the retail adoption literature several effects of product innovativeness on new-product acceptance are found. Most authors (Rao and Mclaughlin, 1989); Lin and Chang, 2012; van Everdingen et al., 2011; Gatignon and Xuereb, 1997) define product uniqueness as the extent to which a new product differs from existing products already present in a category. Rao and McLaughlin (1989) found that retailers seek unique products to be able to present a more innovative retail offering to their customers. Therefore product uniqueness is argued to positively influence a retail buyer’s decision to adopt a new product (Rao et al., 1995). Also Sloot, Fok and Verhoef (2006) come to this conclusion because if a new product is quite similar to other products in a category, when a retailer adopts this product, this would lead to assortment redundancy and duplication which ultimately could lead to lower category sales because for consumers the choice effort increases. Furthermore, Day (2007) states that only 14% of new-product launches are substantial innovations but they account for 61% of all profit from innovations. Therefore it seems logical for manufacturers to only launch really new innovations. However, Kleinschmidt and Cooper (1991) argue that adopting unique products carries higher levels of risk since it is uncertain how consumers will respond. Therefore, a salesperson that wants a retail buyer to adopt a really new innovation needs to convince this retail buyer of the innovation’s potential. One of the ways to convince a retail buyer of an innovation’s potential is to make use of a high quality relationship. In the process of convincing a retail buyer it is expected that under high product innovativeness, the effect of relationship length, relationship intensity, perceived relationship quality and the amount of prior adoption processes on adoption success will be stronger than under low product innovativeness. In other words, when product innovativeness is higher, the impact of the relationship variables on adoption success will be higher. Therefore it is hypothesized:
Hypothesis 5: The more innovative a product that is introduced to a retail buyer, the
stronger will be the relationship between (a) relationship length, (b) relationship intensity, (c)
number of prior adoptions, (d) perceived relationship quality and adoption success.
Control Variables
Although this study is focused on the influence of relationship variables on adoption success, previous research suggests that several other variables affect adoption success. By controlling for such variables a stronger test of the theory, that is developed in the conceptual model, will be provided.
Innovation attractiveness. Considerable empirical evidence (van Everdingen et al., 2011;
not a retail buyer takes into consideration the attractiveness of an innovation. In reviewing the retail adoption literature, three aspects of innovation attractiveness consistently emerged as being central in making the decision to adopt or not. These aspects are relative advantage, which is the degree to which an innovation is perceived as being better than it’s precursor (Calantone et al., 2006; Moore and Benbaset, 1991; Montoya-Weiss and Calantone, 1994), compatibility, which is the degree to which an innovation is perceived as being consistent with existing needs, values and past experiences of potential adopters (in this case end-users of the innovations) (Moore and Benbaset, 1991) and category growth expectations, which is the expected effect of an innovation on the growth of volume and revenue of a category (van Everdingen et al., 2011).
Dependency of retailer. As Palmatier et al. (2006) found that dependency of a buyer has a
large, direct effect on seller objective performance, in this study the control variable
dependency of retailer was added.
Financial allowances. Both the use of slotting fees and introductory allowances have become
well established in grocery and other industries. They are important components of innovation adoption agreements between retailers and manufacturers (White et al. 2000). Gerlich et al. (1994) found that slotting fees and introductory allowances are significant determinants of a retailers’ decision on new-product acceptance. While slotting fees refer to up-front cash payments of manufacturers to retailers to accept newly introduced innovations, introductory allowances refer to any free or discounted orders that retailers receive for newly introduced innovations (White et al., 2000).
Retail buyer experience and expertise. If a retail buyer has no experience or expertise in
deciding what innovations to adopt and this retail buyer faces an account manager with lots of experience and expertise, it is quite imaginable that this retail buyer gets ‘blown away’ by the account manager and adopts innovations that are introduced to him quite easily. Therefore, the effects of retail buyer experience and retail buyer expertise on adoption success are controlled for.
Method
Setting and Data Collection
In order to obtain the necessary data for this study empirical research was conducted at a large multinational. Although this firm has been active in several industries all over the world the focus in this study is on the consumer adhesives department in the Benelux.
In this department all relevant salespersons (also called account managers) were asked to fill in questionnaires about the relations they have with retail buyers in the Benelux Do It Yourself (DIY) market. Since this study aims to add insights to the current literature on retail adoption decisions and current literature on retail adoptions is mainly based on studies in (fast moving) food markets this study was conducted at the Benelux DIY market. The Benelux DIY market can be described as highly competitive with only a few large competing firms. Due to the bad economic climate markets are shrinking. In their fight for maintaining revenue and market share, the firms constantly introduce new innovations.
Questionnaire Design
In this study most core variables of the theoretical model are measured using multi-item scales. If possible, existing scales from the literature were used. For the variable adoption
success no appropriate scales were available in the existing literature. Therefore several
qualitative interviews with multiple account managers were conducted, resulting in the scales to measure adoption success. After completing the ‘concept questionnaire’ the sales team manager pretested the questionnaire to make sure that all questions were clear and to increase commitment to the project. The results of this pretest led to adjustments in the questionnaire. More specifically, the variables retail buyer experience and expertise were added. In appendix A the questionnaire can be found.
Data collection process
After finalizing the questionnaire, appointments were made with each account manager. During these meetings account managers were asked to fill in several questionnaires about innovations that they offered to retail buyers during the last three years. Because the account managers had to complete several questionnaires, during each meeting a researcher was present to structure the filling in of the questionnaires and to answer questions that arose. Because the account managers were asked to recall events it was decided not to include innovations that were introduced more than three years ago. The researchers selected specific innovations that were introduced to the retail buyers in the last three years. In selecting these innovations specific attention was given to the level of innovativeness of the innovations, or type of innovativeness. In appendix B these innovations and their level of innovativeness can be found. To ensure confidentiality, it was agreed that the names of the account managers are not to be revealed. Furthermore, the account managers returned the questionnaires directly to the researcher.
Dependent variable
For the variable adoption success no definition existed besides the mere determination if an innovation is adopted by the retailer or not. In this study adoption success was measured using four items. The first item (AS1) was developed as a critical condition for the other items to be relevant. It measured whether or not an innovation that a salesperson introduced to a retail buyer was adopted or not. By carefully analyzing interviews with several account managers three other items on a five-point scale were developed. These items helped to determine whether or not an innovation was placed on the position on a shelf that the account manager preferred (AS2), whether or not the innovation was placed on the preferred amount of ‘facings’ (AS3) and whether or not the innovation substituted a firm’s own product facings or product facings of a competitor (AS4). Conducting a factor analysis resulted in AS2 and AS3 to be a single factor with a Cronbach’s Alpha of 0.85 and AS4 to be a factor consisting of only one item.
Independent and moderating variables
Following Dagger et al. (2009) relationship length is defined as the length of time that the relationship between the exchange partners has been existing. Relationship in this context represents all phases of a relationship from functional beginnings through a more evolved stage of relationship development. The single-item measure of relationship length asked account managers “Please indicate how many years you already have been doing business with this retail buyer.” This measure was adapted from the existing scale that already has been used in the study of van Everdingen et al. (2011). Although the variable of relationship
intensity previously has not been used in the field of retail adoption, Dagger et al. (2009)
used a scale in their study on Customer Reported Relationship Strength. Following their definition of contact frequency, relationship intensity in this study is defined as the number of interactions per unit of time between exchange partners. The single-item measure in the questionnaire asked account managers “ Please indicate how frequent you have had contact with this retail buyer” Three response options were used; the number of contacts per week, month, or year. After the completion of the questionnaire these responses were standardized to be the number of contacts per year. Also the variable number of prior
adoptions didn’t already exist in the retail adoption literature. It is defined as the amount of
adoption processes that already have been completed in a specific relationship between an account manager and a retail buyer. An adoption process in this definition is the entire process from an introduction of an innovation (by an account manager to a retail buyer) till the adoption or rejection of that innovation. The single-item measure in the questionnaire asked account managers “Please indicate how many adoption processes already have been completed in this relationship.”. In this study relationship quality is defined as an overall assessment of the strength of a relationship (Palmatier et al., 2007). The variable perceived
relationship quality is the strength of a relationship as perceived by a respondent (account
manager). To develop measures for this variable items on a five-point scale were generated by examining existing literature (van Everdingen et al., 2011). After conducting a factor analysis all five items formed a single component with a Cronbach’s Alpha of 0.85. The variable type of innovativeness refers to product innovativeness and is a measure of the potential discontinuity that a product can generate in the marketing and/or technological process. From a macro-perspective, this discontinuity can be a shift in the science and
technology and/or market structure in an industry. From a micro-perspective, this discontinuity can be a change in the firm’s existing marketing resources, technological resources, skills, knowledge, capabilities or strategy (Garcia and Calantone, 2002). Because measuring how innovative a product is can be very subjective, the researchers assessed the innovativeness of the products that were subject of this study. The measures used for this assessment and the results of this assessment can be found in Appendix B.
Control variables
In the empirical study several control variables were included to control for possible confounding effects. These control variables were included by carefully examining existing literature and analyzing interviews with account managers. Because the focus of this study is on the relationship variables, other relevant variables that have a possible effect on the retail adoption decision are integrated in the model as control variables. In prior studies (Rogers, 1983; van Everdingen et al., 2011) both in the field of retail adoption and diffusion several attributes of innovations were found to consistently influence adoption. In this study the most important attributes were combined to create the variable innovation
attractiveness. Five-point scale items were included to measure relative advantage,
compatibility and category growth expectations. Results of a factor analysis indicate that only four items should be combined into a single factor, namely IA1, IA2, IA3 and IA4. These items showed a Cronbach’s Alpha of 0.93. Following van Everdingen et al. (2011) two five-point items were included in the questionnaire to measure the dependence of the retailer on the manufacturer. A factor analysis indicated that these two items should be considered to be one factor. These items showed a Cronbach’s Alpha of 0.95. Furthermore, two items were included in the questionnaire to measure possible financial allowances that could be offered to retailers. For measuring retail buyer experience one item (ordinal scale) was added to the questionnaire. For measuring retail buyer expertise three five-point scale items were added to the questionnaire. After conducting a factor analysis RBExpertise1 and RBExpertise3 were combined into a single factor with a Cronbach’s Alpha of 0.86.
Rotated Component Matrix Component 1 2 3 4 Innovation Attractiveness 3 Innovation Attractiveness 2 Innovation Attractiveness 4 Innovation Attractiveness 1 Perceived Relationship Quality 3 Perceived Relationship Quality 2 Perceived Relationship Quality 4 Perceived Relationship Quality 1 Perceived Relationship Quality 5 Dependency of Retailer 1
Dependency of Retailer 2 Retail Buyer Expertise 3 Retail Buyer Expertise 1
0.895 0.893 0.863 0.853 0.867 0.768 0.748 0.678 0.642 0.937 0.919 0.885 0.871 Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalization
Table 1: Output explorative factor analysis of independent and control variables
Rotated Component Matrix
Component 1 2 Adoption Success 3 Adoption Success 2 Adoption Success 4 0.931 0.916 0.990
Extraction Method: Principal Component Analysis Rotation Method: Varimax with Kaiser Normalization
Analysis and Results
After all questionnaires were completed the dataset consisted of 100 observations. By analyzing this data, first some observations were deleted from the dataset. In case an observation included a non-adoption (a no-response to question 13 of the questionnaire) this observation was deleted. Also for questions one, two and three the standard deviations were calculated. In case an observation included a Z-score higher than 3,3 this observation was deleted. After these deletions, 94 observations were left. In Table 1 the descriptive statistics and correlations of the variables included in this empirical study are presented.
Mean Std. Dev. 1 2 3 4 5 6 7 8 9 10 11 Relationship Length 1.82 1.33 Relationship Intensity 27.49 30.38 -0.169 Number of Prior Adoptions 4.26 3.45 0.605** 0.110 Perceived Relationship Quality 3.59 0.62 0.143 0.169 0.155 Adoption Success 2+3 3.99 0.71 -0.123 0.121 -0.089 0.306 Adoption Success 4 2.64 1.51 -0.117 0.113 0.059 0.213 0.283** Retail Buyer Expertise 3.86 0.93 0.139 0.010 0.106 0.359** -0.038 0.009 Innovation Attractiveness 3.79 0.77 0.024 0.003 -0.023 0.492** 0.095 0.280** 0.053 Dependency Of Retailer 3.31 1.06 0.100 -0.153 0.080 -0.197 -0.187 -0.181 -0.419** -0.214 Financial Allowances 3.40 0.66 -0.228* -0.042 -0.230* -0.073 -0.109 0.062 0.061 -0.312** 0.178 Retail Buyer Experience 3.11 1.11 0.323** 0.112 0.156 0.205* -0.040 -0.227* 0.712** -0.062 -0.440 -0.074 Type of Innovativeness 1.54 0.50 0.155 -0.045 0.012 -0.072 -0.052 -0.022 -0.027 -0.150 0.000 -0.215 0.030
** Correlation is significant at the 0.01 level * Correlation is significant at the 0.05 level n = 90
Dependent Variable Adoption Success 2+3 Adoption Success 4
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Control Variables
Retail Buyer Expertise Innovation Attractiveness Dependency of Retailer Financial Allowances Retail Buyer Experience
Independent Variables
Relationship Length Relationship Intensity Number of Prior Adoptions Perceived Relationship Quality Type of Innovativeness
Interaction Effects
Relationship Length * Type of Innovativeness
Relationship Intensity * Type of Innovativeness
Number of Prior Adoptions * Type of Innovativeness
Perceived Relationship Quality * Type of Innovativeness R square ΔR square 0.119 0.009 -0.101 -0.021 -0.137 0.048 0.004 -0.149 -0.094 -0.064 -0.102 -0.075 0.001 0.013 0.418** -0.016 0.174 0.126 0.005 -0.139 -0.095 -0.080 -0.101 -0.059 0.001 0.010 0.408** -0.021 -0.098 0.000 0.041 0.007 0.181 0.007 0.423 0.419* -0.387* 0.298 -0.701*** 0.246 0.477 0.502* -0.401* 0.542* -0.789*** -0.059 0.006 0.121* 0.012 0.349 0.327 0.082 0.455 0.500 -0.359* 0.660* -0.765*** -0.145 0.006 0.145* 0.023 0.419 0.498 0.004 -0.113 -0.756 0.365 0.038 *ρ < 0.05, **ρ < 0.01, ***ρ < 0.001
Table 4 Results of Regression Analyses
In table 2 the results of the linear regression analyses are presented. When creating the interaction terms first the independent variables were mean centered to reduce multicollinearity (Aiken and West, 1991). During the examination of multicollinearity, variance inflation factors (VIF) were calculated and a maximum VIF of 3.05 was found. Therefore the VIF-values are well below the rule-of-thumb cut-off of 10 (Neter et al., 1990). Models 1 and 4 contain control variables. In models 2 and 5 the independent variables are added. In model 3 and 6 the relational characteristics and moderator variable are examined. Model 3 shows that the coefficient for relationship length is negative and not significant (β = -0.059, p > 0.05). Also in Model 6 a negative coefficient for relationship length is shown that is not significant (β = -0.145, p > 0.05). Therefore Hypothesis 1 is not supported. These findings indicate that relationship length does not have a positive effect on adoption success which was expected. The coefficients for relationship intensity are both very low and not significant in both models 3 and 6 (β = 0.001, p > 0.05; β = 0.006, p > 0.05), thus not supporting H2. Although the coefficient is positive, it is very low and insignificant, therefore
relationship intensity has a negligible effect on adoption success according to these findings.
In model 3 the coefficient for number of prior adoptions is low and not significant (β = 0.010, p > 0.05). In model 4 however, a significant higher positive coefficient is found that also is significant (β = 0.145, p < 0.05). Therefore, H3 is partially supported. These findings show mixed results of the effect of number of prior adoptions on adoption success. Regarding
perceived relationship quality both models show different findings. In model 3 the coefficient
is quite high, positive and significant (β = 0.408, p < 0.01). In model 6 the coefficient has a lower value that is not significant (β = 0.023, p > 0.05). H4, therefore is partially supported.
Regarding the moderating effect of the type of innovativeness on relationship length (β = -0.098, p > 0.05; β = 0.498, p > 0.05), relationship intensity (β = 0.000, p > 0.05; β = 0.004, p > 0.05), number of prior adoptions (β = 0.041, p > 0.05; β = -0.113, p > 0.05), and perceived
relationship quality (β = 0.007, p > 0.05; β = -0.756, p > 0.05) the findings are quite different
between model 3 and model 6, however none of the expected effects is significant. Therefore H5 is not supported at all.
Discussion and Conclusion
One of the critical factors for the long-term success of firms is that firms remain innovative and constantly introduce new products. In today’s markets, manufacturers often sell their products through retail channels to the end-user. Therefore part of the manufacturer’s success of being innovative depends on the capacity to get it’s innovations adopted by the retailer. Although the first studies in the field of retail adoption appeared in the 1970s, there haven’t been a lot of follow-up articles published. The objective of this study was to contribute to the existing literature by focusing on the objective characteristics of the relation between a manufacturer’s salesperson and a retail buyer, often also referred to as category manager and by defining adoption success more specifically. In the field of retail adoption, relationship length is found to have a positive effect on adoption success (van Everdingen et al., 2011). In this study, findings do not support this effect of relationship
length on adoption success, since the coefficients in both model 3 and 6 are negative and
not significant. A possible explanation for finding other results than in prior studies could be that in this study adoption success is measured quite differently. Furthermore relationship
length is an objective variable when measuring characteristics of a relationship. Quite
possibly these objective variables are far less important than the subjective characteristics of a relationship such as relationship quality which is an important variable in most studies in the field of retail adoption since it is found to have a significant effect on adoption intension (Kaufman et al., 2006; van Everdingen et al., 2011; Lin and Chang, 2012). Relationship
intensity, a newly developed variable in the field of retail adoption, was expected to
positively influence adoption success. Although findings indicate a positive coefficient, the effect is negligible because of its insignificance. This variable, also is a objective measurement of a relationship and possibly is much less important than the subjective variables in a relationship. The number of prior adoptions in a relationship also is a newly developed variable that was expected to positively influence adoption success. Findings on these coefficients differ across the models used in this study. In both models the coefficient is positive, however in model 3 the coefficient is a lot smaller and insignificant while in model 6 the coefficient is quite large and significant. Examining the main difference between the models, that is the dependent variable (AS2+3 vs. AS4) it is clear that the number of prior
adoptions is relevant when an innovation is preferred to substitute for a listed product of a
competitor. A possible explanation for this finding could be that a retail buyer, who because of the interdependency between manufacturers and retailers also benefits from a highly innovative manufacturer, prefers innovative products and therefore chooses to delist a product from a less innovative manufacturer when a new innovation is presented to him. This study partly underscores previous findings (Kaufman et al., 2006; van Everdingen et al., 2011) that relationship quality positively influences adoption success. In model 2 the coefficient is quite large and significant, while in model 4 the coefficient is substantially
smaller and insignificant. Also here, the difference between the models lays in the dependent variable. AS2+3 are about positions of an innovation on the shelf and the amount of facings an innovation is presented on. An account manager from the manufacturer often tries to influence these decisions, which are ultimately made by the retail buyer, by presenting his vision on the most effective shelf layout. However, for AS4, where a retail buyer has to decide if a competitor’s product needs to be delisted in order to create space for the new product, this decision may possibly be harder for a retail buyer to make since it has a lot more consequences. If other manufacturers have contracts about being listed (for example for a certain period of time), no matter how high the relationship quality is, then a competitor’s product cannot be delisted. The expected moderating effect of type of
innovativeness is not supported by the findings in this study. No prior studies in the field of
retail adoption have placed attention on a possible moderating effect of type of
innovativeness on any other variable. A possible explanation for not finding any moderating
effects of type of innovativeness could be that the difference in innovativeness between the innovations included in this study are not big enough. As can be seen in Appendix B, only really new innovations and incremental innovations are included in this study but no radical innovations.
Theoretical Implications
The findings of this study suggest several important avenues for the role of relationship variables in theories about retail adoption and adds to the theory of product innovation management. First, the framework gives insight in how perceived relationship quality and number of prior adoptions effect different aspects of adoption success. Where perceived relationship quality positively effects the position of an innovation on the retailer’s shelf and the amount of space to be created for the innovation, the number of prior adoptions positively effects the delisting of a competitor’s product to create space for the adoption of the innovation. A second contribution of this study emerges from the creation of new measurements for adoption success. Previous empirical research (Kaufman et al, 2006; van Everdingen et al., 2011; Lin and Chang, 2012) only explored adoption success in terms of state and not in terms of extent. This study clearly shows that there is more to adoption success than just the mere fact whether or not an innovation is adopted or not. A third contribution of this study is the extension of other research in the area of adoption success by exploring the role of relationship variables in a new context, namely the Dutch DIY market. Finally, this study contributes to the field of product innovation management literature. It shows that for organizations to benefit from driving new products to market, besides organizing for developing new products, for instance by applying Cooper’s (2008) stage-gate model, also the role of retailers should be assessed thoroughly in every new product development process, especially in the launch-phase. One possibility to prevent innovation projects from failing could be to include the retailer in the innovation process early and create mutual commitment.
Managerial Implications
Following this research, some implications are to be considered. Results of this study show that different independent variables have effect on different aspects of adoption success.
Perceived relationship quality is found to have a positive effect on the positioning of a new
product on the shelf and the amount of space to be created for the new product. The
number of prior adoptions is found to have a positive effect on the decision to replace a
competitor’s product. For account managers that are in the process of negotiating the listing of a new product it pays off to clearly think about delivering a detailed “category plan” with lots of information and arguments on how to best organize a category to a retail buyer. In doing this, they should pay special attention to the relationship aspects mentioned above. If, for instance, a new account manager is recently hired, it could be beneficial to train him in the process of negotiating the listing of a new product by sending him with a more experienced account manager. In this way he could learn and start to develop a relationship with a retail buyer. Also, although the results of this study do not show a moderating effect of type of innovativeness, a direct effect is found in other studies (van Everdingen et al., 2011; Montgomery, 1975). Therefore, it would be useful for account managers to clearly communicate with R&D and Marketing departments about the innovation pipeline and create strategies that align the development of innovations with capabilities and resources of the sales department. Lastly, results indicate that the use of financial allowances has a positive effect on adoption success. Although financial allowances already are widely used in the grocery industry the use of it in the DIY industry is starting to escalate. Account managers in the DIY industry should take the use of it into consideration in order to negotiate the most successful product adoption.
Limitations an Future Research Directions
This study contains several limitations that need to be discussed and provide meaningful directions for future research. First, data collected during this study was collected at only one company and by using a limited number of respondents. Empirical studies in a wider variety of organizations and in other markets are necessary to generalize findings further. Second, respondents were asked to call back events that happened earlier in time, with a maximum of three years. Although most respondents seemed to have no problem recalling events it nevertheless is less accurate than filling in questions while such events are happening. Because of these limitations, the reliability of this study is limited. Future research may consider a more longitudinal research design that could help to minimize these limitations. Third, during this study only one party of a relationship was asked to assess the relationship, namely; the account manager. Although in previous research in the field of retail adoption also only one party in the relationship (the retail buyer) provides the data, future research may consider to collect data from both parties of a relationship. Especially when subjective variables are measured, such as relationship quality, it would be interesting to compare how both parties asses such variables and possibly try to search for explanations when they assess variables differently. Fourth, new scales for adoption success were developed. Although these scales were developed by carefully analyzing interviews from multiple account managers, factor analysis and regression results indicate that the four scales that were developed cannot be combined into one variable. Also, findings indicate
that the independent variables in this study have different effects on the separated factors that adoption success consists of. Therefore, in future research, it would be useful to create complementary scales and measurements of adoption success and relate these to the scales and measurements used in this study.
Acknowledgements
The author is very grateful for the guidance of Master Thesis supervisor Hans van der Bij and for the mentoring of Senior Brand Manager Stijn Thielen during a six month internship at Henkel. Suggestions and comments from different people for improving earlier versions of this paper were very helpful. Also, the support from friends and family during the process of writing this paper are very much appreciated.
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Appendix A
A study of the relationship between salesperson and
retail buyer during the innovation-adoption process
Marc Kloppers
Economische en Bedrijfskundige Faculteit Rijksuniversiteit Groningen
Definition
In this survey several questions are asked about the relation between a salesperson from Henkel and a retail buyer (sometimes also referred to as category manager). The questions are specifically about the relation between the salesperson and retail buyer prior and during the process of ‘selling’ an innovation to the retail buyer in order to make the retail buyer adopt the innovation and list it. In some cases it could be possible that instead of a single retail buyer, a buying team is responsible for adopting an innovation. In that case questions should be answered using best judgment.
Instruction
This survey will take approximately 10 minutes to complete. Please answer all questions. In case you can not answer a question please use your best judgment and give the best answer possible.
Objective
The objective of this survey is to gain deeper insight in the relation between a salesperson from a manufacturer and a buyer from a retailer, specifically focused on the innovation adoption process. These insights are used to further develop the existing theory and ultimately serve as input for developing recommendations to AC on how to improve the innovation adoption process.
Confidentiality
All responses will be strictly confidential. Data will be analyzed at the aggregate level and therefore the respondents will remain anonymous. The raw data will only be accessed by the academic researchers.
DIY
Innovation Retailer Retail buyer
... ………. ……….
Relationship Length
1 Please indicate how many years you already do business with this retail buyer. … years
Relationship Intensity
2 Approximately how frequently did you have contact with the retail buyer? …… x per week
…… x per month …… x per year
Number of Prior Adoptions
3 Please indicate how many adoption processes already have been completed in this relation … adoption processes
Please indicate to what extent you do agree with the following statements:
Perceived Relationship Quality
Stongly disagree
Disagree Don’t agree/ don’t disagree
Agree Strongly agree
PRQ1 We can trust the sincerity of the retail buyer
PRQ2 If this retail person gives us advice, we know he tries to give the best advice
PRQ3 When making important
decisions, this retail buyer keeps in mind our interests
Stongly disagree
Disagree Don’t agree/ don’t disagree
Agree Strongly agree
PRQ4 We can count on the fact that this retail buyer takes into account how his future decisions will affect us
PRQ5 If we present our problems to this retail buyer, we know that he will react understandingly
Retail Buyer Experience 2 years or less
2-4 years 5-9 years More than 9 years
9 Please indicate how many years this retail buyer has experience buying
Retail Buyer Expertise Very unknowledge able Unknowledgeable Not unknowledgeabl e/ not knowledgeable Knowledgeable Very knowledgeabl e
RBE1 Please indicate how
knowledgeable this retail buyer is Very unqualified Unqualified Not unqualified/not qualified
Qualified Very qualified
RBE2 Please indicate how well
educated this retail buyer is
Very unskilled
Unskilled Not unskilled/not skilled
Skilled Very skilled
RBE3 Please indicate how skillful this retail buyer is
Adoption Success Yes No
AS1 Did the retail buyer adopt the innovation?
Stongly disagree
Disagree Don’t agree/ don’t disagree
Agree Strongly agree
AS2 The location of the innovation on the shelve is my preferred location
AS3 The amount of shelve space to display the innovation is what I preferred
AS4 This innovation replaces a competitor’s product
Innovation Attractiveness Stongly disagree
Disagree Don’t agree/ don’t disagree
Agree Strongly agree
IA1 Using the innovation enables the customer (end user) to accomplish DIY jobs more quickly
Stongly disagree
Disagree Don’t agree/ don’t disagree
Agree Strongly agree
IA2 Using the innovation improves the quality of a customer’s DIY job
IA3 Using the innovation makes it easier for a customer to do a DIY job
IA4 Using the innovation enhances a customer’s effectiveness on a DIY job
IA5 Using the innovation is compatible with all aspects of a DIY job
IA6 The value of the category increases when this innovation enters the category
Product Attractiveness Very unattractive
Unattractive Not unattractive/ not attractive
Attractive Very attractive
IA7 Compared to other products in this product category, how attractive is this innovation?
Dependence of Retailer Stongly disagree
Disagree Don’t agree/ don’t disagree
Agree Strongly agree
DOR1 The replacement of Henkel
would entail additional costs for the retailer
DOR2 The turnover and profit
generated by products of Henkel are difficult for the retailer to replace
Financial Allowances
FA1: Are there any up-front cash payments to the retailer to accept the innovation? yes/no
FA2: Are there any free or discounted orders for the innovation? yes/no
