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99 Seiten, Note: 1,7
1.1 The Purpose of the Study
1.2 Structure of the Thesis
1.3.1 Secondary Data
1.3.2 The Case Study Method
1.3.3 Research Limitations
2 The Concept of Innovativeness
2.1 Definitions of Innovation
2.2 Berlyne’s Theory of Collative Variables
2.3 Newness Perception across Countries
3 The Concept of Success Factors
3.1 Definitions of Success
3.2 Measurements of Success
3.3 Methods for Determining Key Success Factors
3.4 Key Success Factors
3.4.1 Exogenous Key Success Factors
3.4.2 Endogenous Key Success Factors
3.4.3 Consumer Perception Variables
3.4.4 The Model
4 The Innovation Process
4.1 The Idea Generation Process
4.2 Adoption and Diffusion of Innovations
4.3 Determining Key Success Factors of NP
5 Case Study: “Kess & Fit” at Kessler & Comp. GmbH & Co KG
5.1.1 Company Profile
5.1.2 The Kess & Fit Product Line
5.1.3 Situation Analysis
5.2 Analysis of Key Success Factors
5.2.1 Exogenous factors of success
188.8.131.52 Market Structure
184.108.40.206 Level of Market Development
220.127.116.11 The Political Situation
5.2.2 Endogenous Factors of Success
18.104.22.168 Business strategy
22.214.171.124 Market Intelligence
126.96.36.199 Human Resources
188.8.131.52 Value Innovation
184.108.40.206 Value-based Marketing
220.127.116.11 Financial Resources
5.2.3 Consumer perception variables
5.3 Conclusion and recommendations
Table of Figures
Figure 1. The three Level Innovation Process
Figure 2. Categorization of the newness concept according to a subjective and contextual dimension
Figure 3. The Wheel of Consumer Behaviour
Figure 4. Methods for Determining Key Success Factors
Figure 5. Fundamental interrelationships of KSFs as a result of empirical research
Figure 6. Endogenous Key Factors of Success
Figure 7. The Model
Figure 8. Predictors of New Product Performance
Figure 9. Kess & Fit Product Life Cycle
Figure 10. The Kess & Fit Communication Process
Figure 11. Smart Spenders
Figure 12. Example illustrating the hypothesized newness dimensions
Figure 13: Berlyne’s Wundt Curve
Figure 14. Mathematical Index of Success
Figure 15. Two Routes to Value Creation
Figure 16. Porter’s Value Chain
Figure 17. The Marketing Mix
Figure 18. Example of Mind-Map
Figure 19. Example of Morphological Forced Connections
Figure 20. Five Types of Perceived Risks
Figure 21. Rogger’s Adoption Curve
Figure 22. KESSKO’s Worldwide Network
Table 1. Key Data on the Macroeconomic Development in Germany
Table 2. Hofstede’s Cultural Dimensions
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One of the clearest challenges of the 21st century is to develop innovative products that support an economically vibrant and culturally diverse global society. Hence, companies that fail to develop new products are exposing themselves to great risk (Berenson, 1994). Existing products are vulnerable to changing consumer needs, expectations and tastes. Also, new technologies, shortening life cycles, and augmented domestic and foreign competition represent an increased risk. On the other hand, developing exclusively new products is not risk-free either. Moreover, it is difficult to get an accurate figure on new product success rates, as there is no universal definition of success within and between firms (Berenson, 1994). Evidence from research and recurring themes in literature suggest a variety of key factors of success as well as of that ilk doomed to failure. Most notably, the failure to listen to the voice of customers, defective pre-development research, blurry product definitions, insufficient quality of execution of determining new product development (NPD) tasks, and inefficient project teams have been identified as the major causes to flop (Cooper, 1993). However, Apple’s success story teaches us that sometimes, smart companies should ignore all the talk of “user-centric-innovation” in order to pave the way for new trends (The Economist, 2007). For instance, Steven Jobs’ iPod that was heavily ridiculed when it was launched in 2001 is a brilliant example for a successful non-user-centric innovation (The Economist, 2007). Similarly, Akio Morita, Sony’s co-founder, once stated “there was no need for market research. The public does not know what is possible. We do” (as cited in Kotler, 2003, p.85).
In the light of these facts, this study attempts to identify and explicate key success factors for a thriving development of innovative products. Based on past researches and studies, the concept of innovation and related key success factors will be reconsidered. Specifically, this research strives to identify key success factors of low-calorie products by analyzing explicitly the “Kess & Fit “ case study at Kessler & Comp. GmbH & Co KG.
The main body of this thesis is structured into six different chapters. The first chapter briefly elucidates the purpose of this study and gives insights on the research methodology and its limitations.
The second chapter introduces the concept of innovativeness. Based on secondary data this chapter reviews this concept from an economical, a socio-cultural, and a psychological perspective. Most notably, Berlyne’s theory of collative variables and Hofstede’s cultural dimensions, illustrate the perceptual process involved in the buying decision process of new products.
The third chapter introduces the concept of success factors and illustrates a comprehensive approach to identify key factors of success. Furthermore, a model displays the dependencies among these factors and categorizes them according to exogenous, endogenous and consumer perception variables.
The fourth chapter illustrates different stages in the innovation process, from idea generation, through adoption, to diffusion. In addition, this chapter summarizes determining factors of success for the development of new products. The fifth chapter applies the knowledge gained from previous chapter to the case study Kess & Fit, with regard to exogenous, endogenous and consumer perception variables. Concluding recommendations suggest essential steps to promote the Kess & Fit product line.
Finally, the sixth chapter summarizes overall insights gained from the study on key factors of success of new products/product innovation.
The groundwork for the first four chapter of this thesis was an extensive research on secondary data aimed at providing a conceptual framework to approach the Kess & Fit case study in chapter five. While secondary data is particularly useful to understand and formulate research problems concerned with key success factors of new products/product innovations, this is not necessarily the most adequate approach to solve the Kess & Fit case study. This is explained by the fact that “many phenomena cannot be understood if removed from their social context” (Ghauri, & Grønhaug, 2002, p.171). Hence, an exploratory approach, such as the case study method, which explains unique features of the Kess & Fit management situation by observing, collecting and interpreting primary data from within a company, seems to be more appropriate. In view of that, the following sub-chapters illustrate the advantages and disadvantages of both approaches.
Secondary data can be classified according to internal and external sources (Ghauri, & Grønhaug, 2002). Internal sources or within a company include information on company’s customers, suppliers, employees, marketing plans, reports from different departments, brochures, catalogues and the like. External sources encompass published journal articles, books, internet sources, governmental data as well as statistical data on consumers’ behaviour, demographics and all kind of market research information. The most important advantages of using secondary data can be ascribed to the saving in time and money (Ghauri, & Grønhaug, 2002). In fact, as opposed to primary data, often researchers can collect almost immediately information from various sources at low or no costs. In addition, secondary data often provide useful research approaches, methodologies and information that may help researchers to handle a particular research problem and to formulate and draw conclusions from a broader perspective. Although it is advisable to start research studies using secondary data, nonetheless some disadvantages should be considered. One of the main shortcomings of using secondary data is that the collection of these data were aimed at solving objectives of different studies that may not completely fit to researchers’ specific problem (Ghauri, & Grønhaug, 2002). Accordingly, units of measurements, specific definitions as well as findings may be not compatible and consistent with researchers’ one. In addition, findings from secondary data may be not accurate and reliable. In view of that, it is necessary to validate findings from secondary data with the original source or primary data to be reliable. Thus, if secondary data is not accurate and/or does not exist at all, the researcher must collect primary data to solve his research problem. Although primary data may provide useful information directly from field, nonetheless this approach would have gone beyond the scope of this study. In fact, to collect useful and significant primary data on key factors of success of new product / product innovation would have been time-consuming and costly as it would have been necessary to collect and compare data relevant to different companies operating in different sectors worldwide and thus affected by different cultural particularities.
According to Ghauri and Grønhaug (2002), “the case study method is used when we want to study a single organization and we want to identify factors involved in aspects or behaviour of an organization or smaller unit, such as marketing or finance department” (p.172). Accordingly, a case study is defined as the description of a specific management situation, which is unstructured because the factors causing certain phenomena are unclear. Most case studies are pursued through a review, collection and interpretation of data and interviews, such as historical data, financial statements, verbal reports, personal interviews and the like. Furthermore, data can be quantitative as well as qualitative. Usually, quantitative data refer to data that is statistically analyzed, measured and expressed in numbers. In contrast, qualitative data deal with data that cannot be statistically analyzed and is difficult to be measured in numbers (Ghauri, & Grønhaug, 2002). The latter provides a deep understanding of a certain phenomenon. Since, the researcher must collect the data personally, it is crucial that he fully understands the research objectives, to observe, identify and interpret unique features of a specific phenomenon. In line with this theorizing, the Kess & Fit case study will be analyzed by exploring qualitative data using this case study method. Thereby, personal interviews with KESSKO’s top management as well as company’s sales representatives will give insights into reasons behind the sluggish Kess & Fit sales volumes.
Major research limitations are time and costs. In fact, the analysis of innovations and respective key factors of success would have been more extensive and accurate if conducted over a longer period that would have allowed screening and selecting the great amount of data available in literature more effectively. Although, significant savings in time could have been reached by consulting reliable and accurate data provided by professional research organisations, nonetheless affording such expensive reports would have gone beyond the financial capabilities of the researcher.
Innovation has been widely studied during the last centuries. Already in 1848, Marx and Engels, in their “Manifesto of the Communist Party”, expressed their view that capitalism cannot exist without constantly revolutionizing the instruments of production. In today’s business environment, innovation is a buzzword in many countries. Indeed, innovation is generally seen to be crucial for long-term economic growth (Fagerberg, Mowery, & Nelson, 2007). Hence, in 2000 the European Union (EU) formulated a development strategy in the Lisbon-programme with the main purpose to enhance the rise of productivity and to improve the EU’s competitive position on world markets. Higher productivity can be achieved by fostering innovation, entrepreneurship and knowledge (Lambooy, 2005). Innovation and cost-reduction are two strategies followed by firms to enhance their competitiveness in today’s globally interconnected business environment. Besides, most models of innovations within a company suggest R&D expenditures to have a positive impact on profitability (Michaut, 2004). However, Booz Allen Hamilton’s annual study of the world’s 1000 largest corporate R&D (Appendix A) recently revealed a small group of high-leverage innovators who outperform their industries by spending less on research and development than their competitors (Jaruzelski, Dehoff, & Bordia, 2006). ACNielsen (2005), the leading global provider of marketing research information services, recently observed that more than 500’000 new products are yearly introduced in the European market, although only 10% survive the first two years. In the light of these facts, the following chapter attempts to find an appropriate definition of innovation.
In literature, limited consensus exists in the typology and the terminology of innovation. Usually, the word innovation refers to both the product itself and the process yielding the product. The process is the conception of a creative thought, invention, behaviour, or anything that is then brought to reality with a concrete product or service (Robertson, 1967). Innovation usually follows from a desire to change. This change can either originate from within a company and/or from the external environment. Changes encompass socio-cultural, political, and economical aspects, altering consumer needs, internal strategic choices, as well as individual creativity (Harkema, 2002). Hereby, they permeate all the different stages an innovation goes through: from conceptualization and/or adoption, through implementation, to diffusion. Pavitt (2007) described these stages as “overlapping subprocesses” of innovation, where knowledge is first produced, then transformed into artifacts and finally continuously matched against market needs and demands. Accordingly, Fagerberg (2007) defined innovation as a variable and dynamic process over space and time.
There is a considerable time lag between the first occurrence of an idea and the earliest attempt to carry it out into practice (Fagerberg et al., 2007). Time lags may occur because of lacking conditions for commercialization. There may not be a sufficient need and/or it may be impossible to produce and/or market an innovation as complementary inputs or factors are not available yet. For instance, Leonardo da Vinci’s flying machines would have been developed a few centuries ago if there had been adequate materials, production skills and in particular a power source (Fagerberg et al., 2007). In view of that, Kline and Rosenberg (2007) described innovation as a dynamic and continuous process of interrelated inventions and innovations. Thereby, they used the concept of linear model to object, in their view, widespread erroneous interpretations of innovation as applied science. In view of that, Kline and Rosenberg (2007) suggested that firms, driven by assumed commercial needs, usually innovate first by reviewing and combining existing knowledge and then, if necessary, by investing in research and science.
Other researchers presented different approaches in the description and classification of innovations either focusing on a company perspective and/or relying on the consumer perception. A large amount of empirical studies described innovation as the capacity of a new product to influence the firm's existing resources, capabilities, and strategy (Garcia, & Calantone, 2002). Thereby, researchers distinguished several types of innovation, the most common including technical versus administrative, radical versus incremental, and product versus process (Fagerberg et al., 2007). For instance, Damanpour (1991, p. 556) defined innovation as the “adoption of an internally generated or purchased device, system, policy, program, process, product, or service that is new to the adopting organization”. Schumpeter (1911) defined innovation as the first introduction of new products, new production processes, new markets, new organizations, and new inputs. Drucker (1985, p. 19-33) described innovation as “the specific tool of entrepreneurs, the means by which they exploit change as an opportunity for a different business or different service … innovation involves, changing the value and satisfaction obtained from resources by the customer”. Further studies considered both newness and the capacity to generate as the key elements of innovation, describing it as the act that endows resources with a new capacity to create wealth (Drucker, 1985). Mark Francis (1999), a Senior Research Associate at the Lean Enterprise Research Centre at Cardiff Business School, described classically innovative products as breakthrough products that appear to the consumer to bring true innovation to a category, or alternatively create a new category. Similarly, for Veryzer (1998) really new products were referred to as radically new, discontinuous, or revolutionary products, dislocations and breakthroughs, as opposed to continuous or evolutionary innovations. In the same way, Robertson (1971) proposed a three level continuum for classifying new products by their impact on consumption patterns as follows:
Most innovation...is of a continuous nature and, especially in the consumer sector, is the result of an attempt to differentiate products in order to increase market share. Few and far between are innovations of a discontinuous nature, which significantly alter or create new consumption patterns. (p.7)
Hereby, as shown in Figure 1, for Robertson (1967) discontinuous innovation can significantly change our lives, by establishing a new category, whereas dynamically continuous and continuous innovations are simply new brands in an existing category and readily fit established patterns of consumption behaviour. On the one hand, radical newness can be based on the absolute uniqueness of the technology applied, not only in a particular company but also in the whole industry (Nyström, 1985). In this case, discontinuous innovation is the “creation of a line of business new to both the firm and the customers” (O’Connor, 1998, p.152). On the other hand, radical newness can also be based on the absolute uniqueness of the product or service itself that may result in altering consumption patterns.
illustration not visible in this excerpt
Figure 1. The three Level Innovation Process
1Adapted from: Robertson, T.S. (1967). The process of innovation and diffusion of innovation. Journal of Marketing [Electronic Version]. 31, 14-19
The above mentioned stream of research focused mainly on innovativeness per se and ways of classifying new products. However, past studies already acknowledged the complexity of newness conceptualization (Michaut, 2004). Hence, various studies analyzed innovation from a multidimensional point of view by taking into consideration more elements. More specifically, a few studies defined the dimensions of problem versus solution or product versus technological capability in their conceptualization of innovation (Veryzer, 1998). Others distinguished between newness to the firm and newness to the market or to the consumer (Michaut, 2004). For instance, Wiedemann (1999) suggested a subjective and a contextual dimension of newness perception. The subjective dimension answers the question of new for whom, either the producer or the consumer, whereas the contextual dimension answers the question of new for how many. As a result, Wiedeman (1999) categorize innovation into four areas: technological innovation, newness to the firm, newness to the market and newness to the individual, as shown below in Figure 2. Newness to the market and newness to the individual essentially focuses on consumers’ newness perceptions without considering an assessment of novelty, which is based on company skills and resources. These aspects are rather captured by technological innovation that is perceived as such by the whole industry, and newness to the firm, which is exclusively innovative for a company (Wiedeman, 1999).
illustration not visible in this excerpt
Figure 2. Categorization of the newness concept according to a subjective and contextual dimension
2Adapted from: Wiedemann, C. (1999). Neuronale Netze und Fuzzy-logik in der Neuprodukterfolgsfaktorenforschung. Wiesbaden: Gabler.
These approaches in the conceptualization of newness highlighted also the fact that innovation can be perceived in many different ways (new technology, new attributes, new benefits, and the like) more or less difficult to grasp for consumers, as opposed to firms’ perspective. Indeed, firms are directly involved in the NPD process. Accordingly, firm’s evaluation of product newness may not reflect consumers’ perception of innovation. In fact, the perception of radical newness may depend on how familiar someone is with a particular innovation. Hence, distinct types of innovations have different meanings to consumers, are not developed for the same purpose, and, most importantly, requires different marketing strategies. Therefore, other studies emphasized the importance of the beholder in newness perception (White, & Smith, 2001). Indeed, the newness perception of the beholder may depend on factors such as education, culture, social status, age and the like. This implies that the way innovation is perceived has a relevant impact to new products' acceptance or rejection, which could differ according to the degree and nature of product newness (White, & Smith, 2001).
Integrating both the degree and nature of innovativeness in the newness operationalisation should give better insights into new product characteristics leading to consumers’ acceptance (White, & Smith, 2001). Consequently, beyond any doubt it is likewise important to analyze the concept of innovation from a psychological point of view. Berlyne (1963) made use of cognitive psychology to analyze the conception of innovation by formulating the theory of collative variables and suggesting a consumer-based measure of product newness (Berlyne, 1963). Based on Berlyne’s theory, the following chapter aims at developing an appropriate consumer-based measure grounded in basic psychology, and reflecting consumers’ understanding and perception of innovation.
In marketing literature, the psychological approach of newness has received little attention. Berlyne’s theory of collative variables provides a psychology-based approach, particularly valuable in the attempt to get insight into newness perceived by consumers and its consequences on product evaluation (1960). ACNielsen (2005) emphasized the importance of such an approach as follows:
Cognitive psychology, and particularly the research dealing with heuristic decision-making, tells us that consumers use rule sets or heuristics to cope with the vast array of information they are presented with. Once these rules are developed, they turn into “habits” or “autopilot” responses. For consumers, the reasons for initial brand choice are often forgotten; reasons for the habit are also forgotten. The moments of change when key rules get reviewed are infrequent, and often a result of external factors. (p.101)
Recurring themes in literature defined cognitive psychology as an approach to psychology that emphasizes mental processes. Cognitive psychologists are interested in how people understand, diagnose, and solve problems concerning themselves with the mental processes that mediate between stimulus and response. Cognitive theory contends that solutions to problems take the form of algorithms rules that are not necessarily understood but promise a solution, or heuristics rules that are understood but that do not always guarantee solutions (Lam, 2007).
As highlighted previously, a major difficulty in newness definition comes from the fact that the term new is a very common word, used in everyday language. However, having certain, familiarity with this word does not necessarily imply that defining and explaining what a new stimulus exactly means is easy (Berlyne, 1963). To overcome this problem, Berlyne (1960) observed that “if all novel stimuli have certain effects on the organism that stimuli lacking in novelty do not have, they must have some properties in common to produce this effect” (p.20). Thus, instead of trying to define newness as such, it seems more feasible to define what common properties actually make all new stimuli new. Accordingly, beside newness itself, Berlyne (1963) identified six properties of novel stimuli, falling into two homogenous groups. The first group is predominantly sensory whereas the second one is carried out at the cognitive level.
The first group encompasses change, surprise or incongruity as common properties of novel stimuli (Berlyne, 1960). Change occurs when a new instance differs from something already known. This implies that consumers assess new products or innovations according to their initial knowledge. Surprise follows from change and occurs when an expectation based on a previous stimulus is not satisfied by the present stimulus. Incongruity is defined as a special case of surprise. This occurs when expectations based on experiences are contradicted by a new stimulus (Berlyne, 1963). More specifically, there is a certain difficulty to directly assimilate a new product (Appendix B). For instance, the refreshing colours of Apple’s iMac are incongruent with the usual grey colour of other computers.
Uncertainty, conflict, and complexity form the second group of novel stimuli properties (Berlyne, 1960). Uncertainty rises from the impossibility to foresee what will follow from novel stimuli (Berlyne, 1963). Conflict arises from multiple contending reactions to a new stimulus, which are different in strength, quality and occurrences. It can be experienced at different levels and can result in neurosis, emotion and an increasing reaction time (Berlyne, 1960; Keller, 1987). Moreover, conflict is closely related to uncertainty. In fact, the resolution of a conflict yields a reduction of uncertainty. Finally, complexity is defined as the difficulty to comprehend and make sense of a new product and it is closely related to the similarity and unity among elements of an innovation. For instance, the newness of Windows Vista compared to Windows 95 illustrates this dimension as it relies on the technical complexity of the product.
To reduce the degree of complexity, uncertainty and conflict it is necessary to, equip “the organism with knowledge” (Berlyne, 1960, p. 296). Berlyne defined this as information processing or epistemic behaviour (Berlyne, 1963). Accordingly, the more a new product is perceived as being complex, the more a subject tends to increase the knowledge about this new product. However, the more consumers will have to look for information to make sense of a given stimulus, the less positively they will evaluate this stimulus (Berlyne, 1978). Accordingly, consumers will evaluate new products or innovations positively for low levels of complexity and vice versa. This is in line with an experimental study in aesthetics conducted by Lindgaard (2006) with the objective to discover criteria by which people judge visual appear or user satisfaction and trustworthiness, when surfing the web. Thereby, Lindgaard (2006) proved that beyond a certain level of complexity, these stimuli affect consumer behaviour by having the capability to increase arousal, at a point at which the web experience become unpleasant. This aspect is essential in understanding the impact of product newness on liking. In fact, the presence of collative variables in a novel stimulus often yields curiosity (Piccone, 1999; Berlyne, 1960), yet in some circumstances, they evoke extreme fear. Frequently, fear leads to withdraw from buying the product. Evidence from studies in cognitive psychology showed that a few seconds are enough for the end-user to detect these novel stimuli and decide whether to like or dislike it (Lindgaard, 2006). Hence, Berlyne (1978) suggested that consumers need an appropriate inflow of novel stimuli to remain interested without becoming confused and worried. Berlyne (1978) described this appropriate inflow of novel stimuli as the optimal stimulation level (OSL). Thereby, consumers consider various forms of stimulation pleasant at medium intensity and unpleasant at higher intensities (Appendix C). For instance, too little saltiness may be unpleasant for pizza but excessive saltiness may result in disgust. Thus it appears that the nature of newness has large influence on product liking and therefore also on market success. This theorizing is in line with Cox and Cox (2002) who showed that liking is lower for complex products compared to simple ones. Also, evidence from research showed, that consumers largely minimize cognitive activity in decision making, even in the case of first time or risky purchases (Michaut, 2004). Therefore, complexity could be considered as a potential barrier to product adoption, as suggested by Solomon (2004) and by Rogers’ (1995) theory on innovation diffusion.
To summarize, Berlyne’s theory of collative variables gives valuable insights into newness perception. The main message is that consumers perceive newness more or less intense according to its nature and degree. Most notably, Berlyne’s theory shows that consumers usually tend to avoid complexity. This suggests, that the success of new products highly depends on how simple or complex it is perceived. Thus, conceptualizing and designing products that are largely perceived as simple may lead to higher acceptance and consequently to their successful diffusion. This theorizing is perfectly in line with Philips’ slogan Sense and Simplicity.
It’s by now a truism that cultural differences largely influence consumer behaviour across countries. Solomon (2004) summarized these factors in a model, as shown in Figure 3, suggesting that marketers should consider them, in order to identify consumers’ wants and needs. Hence, marketers are keen “to spot cultural shifts in order to discover new products that might be wanted” (Armstrong, & Kotler, 2007, p. 129). Understanding consumer needs and expectations is a crucial aspect to gain a competitive and comparative advantage. Indeed, the perception of newness may vary from country to country. In the light of these facts, this chapter aims at identifying relevant factors to address cross-border newness evaluation attempting to answer the question if given new products would be perceived equivalently across countries.
illustration not visible in this excerpt
Figure 3. The Wheel of Consumer Behaviour
4 Adapted from: Solomon, M. R. (2004). Consumer behaviour. Upper Saddle River, New Jersey: Pearson Education, Inc.
Cultural differences have been identified as major factors influencing consumer behaviour and consequently the perception of new products and its market success (Hofstede, 1991; Solomon, 2004). This is explained by the fact that culture is a “collective phenomenon, because it is at least partly shared with people who live or lived within the same social environment” (Hofstede, 1991, p.5). Although there is no standard definition of culture, a working definition is given by Bates and Plog (1976), who described it as a “system of shared beliefs, values, customs, behaviours, and artefacts that the members of society use to cope with their world and with one another, and that are transmitted from generation to generation through learning” (p.7). In line with this, Hofstede (1991) suggested a multi-dimensional model that analyzes the differences among national cultures and clusters common basic problems, which affect the functioning of societies or individuals all over the world. Most notably, Hofstede’s model illustrated valuable insights on cultural attitudes towards innovation an new product liking. The model is the result of a worldwide study conducted by IBM over a period of six years (1963-1973). Thereby, Hofstede (1991) analyzed a large data base of employee value scores collected in more than seventy countries around the globe (Hofstede, 2001). A score on each of five dimensions characterizes each country, namely: power distance, collectivism versus individualism, femininity versus masculinity, uncertainty avoidance and long-term orientation. Power distance is the dimension, which analyzes how power is distributed and accepted within a society, an institution, an organization and the like (Solomon, 2004; Hofstede, 2001). Power distance implies unequal power distribution and exists in each relationship, which involves a leader and a less-powerful follower, for instance managers and employers, teachers and students, parents and children, and so forth (Hofstede, 2001). A high score suggests that there is an expectation that some individuals wield larger amounts of power than others do. Accordingly, a low score reflects the view that all people should have equal rights (Hofstede, 2001). The second dimension refers to individualism as opposed to collectivism. Individualism defines a society that attaches main importance on the individual and in the virtues of self-reliance and personal independence (Hofstede, 2001). Consumers in individualistic cultures ascribe more importance on personal goals (Solomon, 2004). On the contrary, collectivism describes any of several types of social organization that ascribe central importance to the groups to which individuals belong (Hofstede, 2001). In societies largely characterized by collectivism, people are usually integrated into cohesive groups from birth onwards (Hofstede, 2001).and subordinates their personal goals to those of a stable in-group (Solomon, 2004): These groups assure protection in exchange of unquestioning loyalty (Hofstede, 2001). In Hofstede´s model, the word collectivism has no political meaning and is pertinent to all societies in the world (2001). The third dimension refers to masculinity as opposed to femininity. Masculine cultures ascribe importance to competitiveness, assertiveness, ambition, accumulation of wealth and material possessions (Hofstede, 2001). On the contrary, feminine cultures place more value on relationships and quality of life (Hofstede, 2001). The fourth dimension deals with uncertainty avoidance, which is the “degree to which people feel threatened by ambiguous situations and have beliefs and institutions that help them to avoid this uncertainty” (Solomon, 2004). More specifically, this dimension considers the intensity by which a culture programs its members to feel either comfortable or uncomfortable in situations that are novel, unknown, surprising, and different from usual such as the first encounter with a new product (Hofstede, 2001). Hofstede labels such situations as unstructured and suggests that uncertainty avoiding cultures tries to minimize these contingencies by means of strict laws and rules, safety and security measures as well as a philosophical and/or religious belief in the absolute truth (Hofstede, 2001). This implies that people in uncertainty avoiding countries are largely driven by emotions and anxiety (Hofstede, 2001). In contrast, people in uncertainty accepting cultures are more phlegmatic, contemplative and less emotional because of a greater propensity to tolerate new and different opinions (Hofstede, 2001). Also, uncertainty accepting cultures tend to have as few rules as possible. Finally, the dimension of long-term orientation describes the importance society ascribes to the future versus the past and the present. Thrift and perseverance largely characterize long-term oriented societies, whereas in short-term oriented societies, respect for tradition, fulfilling social obligations such as reciprocation of gifts and favours, and protecting one's face are valued more (Hofstede, 2001).
Hofstede’s (2001) theorizing gives reason to believe that these well investigated dimensions have a significant impact on newness perception and new product liking. This idea finds support in Steenkamp et al. (1996), who showed that innovativeness is largely culture dependent. More specifically, Baumgartner and Steenkamp showed that masculinity and individualism as opposed to high uncertainty avoidance positively influence innovativeness. Consequently, consumers in countries, which are largely characterized by individualism and masculinity, have a greater propensity to innovation and are consequently more open to new products (Baumgartner, & Steenkamp, 1996). Besides, Michaut (2004) suggested a link between Berlyne’s (1967) differential effect of incongruity and complexity on product liking and Hofstede’s (2001) uncertainty avoidance dimension. In fact, people in countries with high uncertainty avoidance tend to overestimate the perceived complexity of new products as opposed to consumers in low uncertainty avoidance cultures (Michaut, 2004). Thus, consumers may undervalue incongruity as they naturally tend to go towards new products and therefore have more experience with them. These observations imply that initial perceived incongruity and complexity differentially influence cross-border new products’ market success since consumers will manage them differently with regard to cultural attitudes. This is in line with Hall (2007) and Strang and Soule (1998), who suggested that uncertainty avoidance slows new products’ diffusion. Among others, Rogers (1985) cited different situations where compatibility with existing social norms has strongly influenced the adoption of innovations. For instance, the adoption of various types of contraceptives in underdeveloped countries greatly depends on local religions and cultural mores.
 In this study, the words innovativeness and newness are treated as synonyms.
 According to this linear model, innovation is supposed to go through a well-defined set of consecutive stages: from research, through development, to production and marketing (Kline, & Rosenberg, 2007).
 Berlyne (1960, p. 30) summarizes two homogeneous groups of novel stimuli under the name collative variables “since they all depend on the collation or comparison of stimulus elements, whether they be elements appearing simultaneously in different sectors of a stimulus field or elements that have been perceived at different times”. Namely, these properties are defined as collative variables because they depend on the comparison of stimulus elements appearing simultaneously or at different times. Moreover, Berlyne (1963) states that all stimuli are originally new and encompass these collative variables but gradually loose them as a consequence of habituation. This implies that consumers perceive new products as new as long as these collative variables are present (Berlyne, 1963).
 See chapter 2.1
 According to Solomon (2004) consumers’ buying decision is largely affected by some kind of perceived risk. Among others, complexity is one cause of perceived risk. See also chapter 3.4.3.
 See chapter 4.2
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