as 65%, when these high-tech products are launched into the market. One way to ... customer orientation and new product performance in the group of small and ...
The Effects of Customer Orientation on the Product Performance of Technological Innovations: A Comparison between SMEs and Large Companies Kai-Ingo Voigt, Christian Baccarella, Andreas Wassmus, Oliver Meißner University of Erlangen-Nürnberg, Chair of Industrial Management, Nuremberg, Germany Abstract--In a high-technology marketplace, consumers and producers simultaneously face a high uncertainty concerning these technological advanced products. Companies have to anticipate costumer demands way in advance to satisfy the market adequately. Among other things, this is the reason why there are failure rates among technological innovations as high as 65%, when these high-tech products are launched into the market. One way to counteract this fatal development is to integrate customers as early as possible in the product development process in order to achieve an optimal ‘fit’ between market needs and technological possibilities. This study analyses customer orientation as part of marketorientation within the biggest German industrial markets. In this context, 108 managers were asked to answer a survey in order to find out about their companies’ customer-integration methods, which are used to let the users be part of the product development process. In addition, this paper analyses the relationship between customer orientation, new product performance and company size. Surprisingly, correlation analysis shows that there is no significant relationship between customer orientation and new product performance in the group of small and medium-sized enterprises (SMEs), whereas this correlation is highly significant in the group of big companies. This fact leads to the conclusion that customerorientation is not as important for SMEs as it is for larger companies. SMEs are by definition closer to the market. They have fewer customers and have to work side by side with them. In contrast, large companies have to compensate their lack of closeness by using customer orientation methods to boost product performance and, therefore, business performance. On the other side, SMEs have to concentrate on other ways to enhance their firm performance.
I. INTRODUCTION Companies in high-technology marketplaces are confronted with a very dynamic market environment, where consumers and producers simultaneously face a high uncertainty concerning technological advanced products [43]. Yet, technological innovations are crucial for organizations in helping companies to adapt to changes in turbulent markets, new technologies, and fierce competition [57; 62]. The emergence of more sophisticated customers and therefore a more distinctive and intense competitive environment highlights the importance of a more advanced market perspective [60; 40; 17; 48]. To successfully develop and manage technological innovations, companies have to analyze customers’ needs and wants in order to satisfy the market adequately [17]. However, especially high-tech companies often have problems in interacting with their customers [49] although “for technology-based companies, the need to be market or customer oriented is particularly
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important” [44, p. 87]. In the literature exists a disturbed relationship between customer wants and technological opportunities, because there has often been a rigorous distinction between the marketing and the innovation perspective [3]. Even though a mainly positive impact of market orientation on firm performance is suggested, there is still more need for research in the high-technology context [19]. Moreover, research has concentrated on the relationship of market orientation and firm performance in large companies [58]. Authors doubt that these findings can be generalized to both, small and large firms [e.g. 38; 48]. Our study builds on this foundation and focuses on the relationship between customer orientation and new product performance in technology-based companies. In addition, the differences of this relationship are elaborated with regard to company size. Hence, this work gives interesting insights into the effects of customer orientation on firm performance in the high-technology context. The following section reviews the existing literature about technological innovations and market orientation approaches. In that context the research hypotheses are derived, before we describe the research model and the data sample, on which our investigation is based. Next, correlation and regression analysis is conducted as basis for the following discussion of our results and the consequential implications. Finally, we discuss the limitations and possibilities for further research. II. THEORETICAL BACKGROUND AND HYPOTHESES A. Technological Innovations In the scientific literature the term ‘technology’ is controversially discussed and it acquired various interpretations over the years [e.g. 36; 5; 4; 50]. For example Gerhard [26, p. 6] states that “a technology is intangible application-based knowledge founded on a scientific theory”. Within this work we focus on the Anglo-American perspective, which defines technology as a “replicable artifact with practical application, and the knowledge that enables it to be deployed and used. Technology is manifested in new products, processes, and systems, including the knowledge and capabilities needed to deliver functionality that is reproducible” [20, p. 2]. In addition, we follow the idea that any scientific theory can be a basis for a technology, if it can be used to solve a practical problem [26]. Therefore, technologies for innovations might come out from any science field. Closely related to the term ‘technology’, the term ‘innovation’ has also acquired a range of meanings over the years and can be explored from various perspectives [e.g. 23].
Garcia and Calantone [25] find in their literature review no less than 15 constructs and 51 distinct scale items, which have been used to model product innovativeness. In a common sense, they all understand innovation as something new for a company, a process of developing and adopting a new item [58]. Martin [41, p. 4] states that “a scientific invention may be viewed as a new idea or concept generated by research and development (R&D), but this invention only becomes an innovation when it is transformed into a socially usable product”. Our study is based on the innovation process of industrial companies in Germany. Hence, our focus is on companies, which develop technological innovations, because “technological innovations are those innovations that embody inventions from the industrial arts, engineering, applied sciences and/or pure sciences” [25, p. 112]. B. Market Orientation and Customer Orientation There exists a long history of theoretical and empirical work in the context of academic marketing research on market orientation [e.g. 39; 45; 52; 37; 33; 18; 31]. Marketoriented companies align their actions and products on the latent and manifested wants and needs of their customers, instead of following a product-oriented perspective [28]. In this context, the frameworks of Narver and Slater [45] and Kohli and Jaworski [37] have been widely the basis of the research in the market orientation field [27]. In their work, Narver and Slater [45, p. 21] define market orientation as an “organization culture that most effectively and efficiently creates the necessary behaviors for the creation of superior value for the buyers and, thus, continuous superior performance for the business”. They follow a cultural approach and assume that market orientation consists of three behavioral components and two decision criteria. The behavioral components are customer orientation, competitor orientation and inter-functional coordination, at which the first two components describe the activities for acquiring information about customers and competitors and the latter component the realization of an adequate coordination of the supply chain within the business, based on the customer and competitor information. In the context of the decision criteria they refer to long term success and profitability. By using a “theories-in-use” approach, Kohli and Jarworski [37] define market orientation from a behavioral perspective and specify their behavioral components in a more market intelligence orientated way into three sets of activities: “(1) organizationwide generation of market intelligence pertaining to current and further customer needs, (2) dissemination of the intelligence across departments, and (3) organization-wide responsiveness to it” [33, p. 54]. Regarding the effects of market orientation, there is common approval that market orientation has a positive influence on firm performance [e.g. 33; 45]. Especially marketing academics suggest that firms that adapt market oriented behavioral components and convert thoughts of this perspective into actions, can expect superior performance [e.g. 39; 52; 10].
Following the above mentioned approaches, which both refer to customer value and needs as an important artifact of market orientation, it can be assumed, that a customer focused perspective has a positive influence on the adoption of technical innovations. While Jaworski and Kohli [33] have a wider focus and see market intelligence as a central element of market orientation, Narver and Slater [45] highlight the customer and competitor orientation perspective as central elements of market orientation. This study bases on the effects of customer orientation on firm performance, as one part of the Narver and Slater’s [45] market orientation approach. Customer oriented firms perceive their customers as the most decisive element of their company’s strategy and they utilize this relationship in the pursuit of long-term success [56]. Gatignon and Xuereb [24, p. 78] define a customer orientated company as “a firm with the ability and the will to identify, analyze, understand, and answer user needs”. On the other side, there has been a debate about how customer orientation can suppress innovative accomplishments. Authors argue that existing customers are not able to articulate latent needs beyond their current scope of mind. Companies, which have a too narrow customer orientated perspective are at risk of losing their innovativeness in the long-term [7], because these companies often “listen too carefully to their customers” [6, p. 198]. However, the mentioned definitions show that customer orientation is a key element in the market orientation approach and that it is supposed to have a critical effect on the firm’s performance. In this context, Cooper [12, p. 56] suggests to integrate the “voice of the customer” as early as possible in the innovation process of a new product. Following his argumentation, this will lead to a more advantageous product, which fits customers’ needs better than a comparable competitor’s product [11]. Hence, a customer oriented company can, by intensively analyzing customers’ needs and wants [17; 24], gain a superior understanding of customer desires and can, thus, enhance firm performance [14]. This may especially be the case for technological innovations, where market uncertainty is high and being close to the market can lead to a decisive competitive advantage [43]. Interestingly, Drucker [21] notes that particularly technological innovations are in need of a strong market orientation and require a detailed analysis of customers’ needs and wants. Consistent with our theoretical findings above, we posit: H1a: Customer orientation has a positive influence on new product performance in innovative firms. Narver and Slater [45] analyzed the effects of market orientation on firm performance with a sample of 140 strategic business units (SBUs) consisting of commodity businesses and non-commodity businesses. In their study, they confute a linear market orientation/profitability relationship. However, in line with their findings they suggest a U-shaped relationship with a poor “stuck in the middle” position on the market orientation/profitability continuum.
Atuahene-Gima et al. [2] go a step further and analyze the relationship of market orientation and new product performance by differentiating between responsive and proactive market orientation [see also 46]. Responsive market orientation means that a company focuses on the current customer needs, whereas the proactive view tries to explore customers’ latent needs, which they may not know yet [35]. The study of Atuahene-Gima et al. [2] reveals that responsive market orientation supports a U-shaped relationship with new product performance, while proactive market orientation indicates to an inverse U-shaped relationship. Our study focuses on the effects of responsive customer orientation, because only current needs and wants can be captured with the research design. Hence, due to the prior literature review we propose: H1b: There is a U-shaped relationship between customer orientation and new product performance in innovative firms.
relationship between market orientation and small enterprises in recent marketing and management literature. In that context they assume that market orientation plays not a critical role concerning firm performance in small enterprises. These mixed opinions and results reveal that the impact of customer orientation on firm performance in small companies needs further investigation and that a comparison between small and large companies can give interesting insights into this topic. Even though it is expected that there are differences between small and large companies, in total we anticipate a positive impact of customer orientation on firm performance, likewise for small and large firms. This leads to the assumptions that: H2a: There is a positive and significant correlation between new product performance and customer orientation in small firms and large firms. H2b: Customer orientation has a positive impact on new product performance in small firms and large firms.
C. Customer Orientation and Company Size It was mentioned before that several authors elaborated the relationship between market orientation and firm performance [e.g. 34; 9; 53; 54, 55; 29; 32; 1]. However, Verhees and Meulenberg [58] state that these studies focus on large firms. They come to the conclusion that these findings cannot be generalized in terms of firm size, because there are essential differences between the innovation process of large and small firms. Ledwith and O’Dwyer [38] conclude that customer orientation is not suitable for determining new product performance in small firms. However, their sample contains only small firms, and therefore no comparison of small and large firms can be made. Pelham [47] argues that small firms are often product-oriented and that they put more emphasis on competitive, rather than on customer information. Yet, Pelham [48] find that for small and medium-sized manufacturing firms “understanding customer needs and competitor capabilities can be very important determinants of performance” (p. 58). Dauda and Akingbade [15] note that there was only little interest placed on the
III. RESEARCH DESIGN AND SAMPLE This study focuses on the specific effects of customer orientation on new product performance, rather than on the whole concept of market orientation. Therefore, we adopt a multidimensional construct used by Ledwith and O’Dwyer [38] in order to test the relationship between customer orientation and new product performance. In this context, one part of the above introduced three component model of Narver and Slater [45] (customer orientation, competitor orientation and interfunctional coordination) is used to measure customer orientation. In line with Ledwith and O’Dwyer’s [38] study, new product performance is calculated by using five measures, namely market-level measures, financial measures, customer acceptance measures, productlevel measures and timing measures. In addition, we examine the differences of the relationship between customer orientation and new product performance of small and large firms. The following Figure 1 illustrates the underlying research model on which this paper is based.
Company Size
Customer Orientation
New Product Performance 1. 2. 3. 4. 5. Figure 1: Research Model
Market-Level Measures Financial Measures Customer Acceptance Measures Product-Level Measures Timing Measures
For the purpose of this study, an online survey was conducted. A partly standardized online questionnaire was designed to fulfill requirements like clarity, clearness, and simplicity of the questions asked [51]. To guarantee these requirements, a preliminary assessment of the questionnaire and a pre-test was conducted, before sending out the questionnaire to potential participants. The questionnaire is comprised of two parts. The first part of the survey collects general information on the participants, the organization, and the use of specific customer orientation methods within the particular company. To make sure that innovative companies take part, companies had to disclose their annually revenue/R&D-investment ratio. The second part of the questionnaire is mainly concerned with the retrieval of the above mentioned constructs of customer orientation and new product performance. These constructs are comprised of a total of 24 items, which already have been adopted by numerous scientists in previous studies [e.g. 38]. In line with researchers, who suggest to use five- or seven-point Likert scales [e.g. 16], the participants had to choose their answers on a seven-point Likert scale ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (7) in order to agree or disagree with a specific statement (see also Appendix A for an overview of all scale points and items). The study was conducted in August and September 2010. Altogether, this study is based on the questionnaires of 108 participants. The focus of this study lies on German manufacturing companies. Managers of the biggest German industries (e.g. automotive, engineering, and electrical engineering) were asked to participate in the survey. To ensure a high quality of responses, especially regarding technological innovations, a popular online social business network was chosen to randomly contact potential key informants, e.g. R&D managers or general managers. Overall, 205 questionnaires were sent out, and thereof 112 completed the survey, which corresponds to a response rate of 54.63%. However, after revising the completed questionnaires, only 108 of the initial 112 questionnaires could be analyzed. The participants are mainly heads of R&D department (47%), followed by project leaders R&D (20%), and general managers (7%). Almost all participants are part
of the management team and, thus, can make statements about relevant company key figures and internal innovation processes. The following Figure 2 shows the breakdown of positions and industries of the sample distributed by percentage. Regarding size, the companies are classified due to their turnover in the last fiscal year and people employment-level. Small and large firms are distinguished according to the SME definition of the German Institute of SME Research (for further information see: http://www.ifm-bonn.org), where small firms are defined as those having maximum 499 employees and less than 50 Mio € annual turnover. In our sample, 55% of the participating companies had at least 50 Mio € revenue in the last fiscal year. In addition, 49% of the companies employed more than 500 people. In line with the small enterprise definition, our sample contains 45 small and 51 large companies. The remaining companies fulfill either the requirement for maximum employee level, or the requirement for annual revenue, and can therefore not be taken into consideration for the comparison of small and large firms. To analyze the innovation strength of the companies, the participants had to state their annual R&D investment in relation to the company’s annual revenues. Here, three categories were defined [59]. With less than 3.5% investment in R&D, companies are classified as not technology intensive. With a value of more than 3.5% and a maximum of 8.5%, companies are defined as hightechnology. With an investment in R&D over 8.5%, companies are categorized as firms with advanced technology. The last two categories can be combined to the group of technology-intensive companies. Due to that, 71% of our sample can be definitely characterized as technologyintensive companies and can therefore make meaningful statements about the innovation process of technological innovations. However, because the participating managers, respectively companies, have a research intensive background and were selected due to their position, the whole sample can be used as a basis to give insights into the effects of customer orientation on new product performance in a high-technology context.
Position R&D Development Engineer 7%
Industry
Other 9% Electrical Engineering 24%
General Manager 16%
Project Leader R&D 20%
Other Industries 18%
Head of R&D Department 47% Engineering 24%
Automotive 34%
n = 108 Figure 2: Position and industry of study participants
IV. RESULTS To test the hypotheses, correlation and regression analyses are carried out. All variables were checked by Q-Q-diagrams to guarantee normal distribution. The reliability of the analysis was tested with the use of Cronbach’s alpha. Statistic data for our constructs is presented in Table 1, which contains information about the arithmetic mean, standard derivation and Cronbach’s alpha. Several authors have considered values of 0.70 and above to be reliable [e.g. 8, 22]. However, product-level measures show a Cronbach’s alpha of 0.597. Cortina [13] suggests that if constructs with only few items (two in our case) are embedded in a multidimensional research design and construct, a Cronbach’s alpha value of less than 0.70 can be seen as sufficient. In addition, Ledwith and O’Dwyer [38] receive in their study a Cronbach’s alpha value of 0.712 within the same construct. Hence, for the purpose of this study the reliability of all constructs is granted. The correlation analysis of the sample gives insights into the linkage of customer orientation and new product performance. Table 2 shows the correlation coefficients between the constructs. The data illustrates significant
associations between all the performance measures and customer orientation. Hence, the aggregated construct of new product performance shows a positively correlated relationship with customer orientation (0.422) and suggests that a higher customer orientation involves a higher new product performance and vice versa. To further investigate the degree of the relationship between customer orientation and new product performance, a regression analysis is conducted. In our model, no autocorrelation (Durbin-Watson-Test) and almost no collinearity can be found. Table 3 illustrates that customer orientation has a direct influence on new product performance. However, the results also show that the relationship of customer orientation is not very strong (R2=0.178). This relatively low model fit makes sense, considering that the initial model of market orientation consists of three dimensions. In addition, it is obvious that customer orientation is not the only variable explaining new product performance and that those other factors are supposed to have an impact on firm performance too. Nevertheless, the results show that customer orientation is an important influencing factor and has a positive impact on new product performance. Hence, these results are supporting hypothesis H1a.
TABLE 1: DESCRIPTIVE STATISTICS FOR RESEARCH CONSTRUCTS Mean Customer orientation, 7 Items (Cronbach’s alpha = 0,765) 5,60 New product performance, 17 Items (Cronbach’s alpha = 0,891) 5,06 Market-level measures, 4 Items (Cronbach’s alpha = 0,848) 5,10 Financial measures, 4 Items (Cronbach’s alpha = 0,718) 4,86 Customer acceptance measures, 4 Items (Cronbach’s alpha = 0,746) 5,44 Product-level measures, 2 Items (Cronbach’s alpha = 0,597) 5,58 Timing measures, 3 Items (Cronbach’s alpha = 0,859) 4,46
SD 0,784 0,679 0,911 0,858 0,783 0,802 1,128
TABLE 2: CORRELATION OF CUSTOMER ORIENTATION CONSTRUCTS AND NEW PRODUCT PERFORMANCE Customer Market-Level Financial Customer ProductTiming New Product Orientation Measures Measures Acceptance Level Measures Performance Measures Measures Customer Orientation Market-Level Measures
1.000
0.308**
0.311**
0.354**
0.329**
0.319**
0.422**
1.000
0.642**
0.430**
0.127
0.572**
0.805**
1.000
0.379**
0.277**
0.601**
0.814**
1.000
0.353**
0.433**
0.709**
1.000
0.315**
0.448**
1.000
0.823**
Financial Measures Customer Acceptance Measures Product-Level Measures Timing Measures New Product Performance ** Correlation is significant at 0.01-level (2-sided)
1.000
TABLE 3: REGRESSION ANALYSIS OF NEW PRODUCT PERFORMANCE AND CUSTOMER ORIENTATION Dependent Variable New Product Performance Independent Variable Coeff. Stand coeff. (std. error) Customer Orientation 0.365 0.422 (0.076) R-square Model Significance
0.178
