How Local Culture Influences Product Adoption - Term Paper Example

How Local Culture Influences Product Adoption Conceptual model This study presents a conceptual model focusing on cultural factors that influence product adoption. The study focuses on firm adoption status as the only dependent variable. Adoption status is the observation on whether or not an organization has adopted or is likely to adopt a product. Exogenous variables in this case are macro-level, meso-level and micro-level factors influencing product adoption. Macro-level variables are national characteristics while micro- and meso-level variables are company and industry attributes respectively (Baldwin & Scott, 2007, p. 78). So as to gain insight into the role of the above elements in influencing product adoption, this paper will present a case study involving European countries where cultural factors have varying influences on the adoption of enterprise resource planning (ERP) system in organizations. Figure 1 describes the conceptual model for this study. Figure 1: Model explaining product adoption status of firms Macro variables One of the main models describing the impact of national culture on product adoption is Hofstede’s cultural framework (Yalcinkaya 2008, p. 207). This framework describes four cultural factors influencing product adoption, namely uncertainty avoidance index (UAI), individualism index (IDV), masculinity index (MAS), and power distance index (PDI). These factors were arrived at through a study conducted in large multinational companies in 72 countries in 1968 and 1972. In 2001, a fifth factor, long-term orientation (LTO), was incorporated into this framework (Yalcinkaya 2008, p. 207). As Hofstede (2001 p. 19) explains, these five dimensions can be used in providing an overall type of a nation’s culture. Table 1 provides a summary of the attitudes and value connotations associated with low and high scores on the five dimensions of culture. Table 1: Organizational characteristics based on scores on the Hofstede (2001) Dimensions Low score on dimension High score on dimension PDI Power Distance Index Decision-making not centralized Decision-making centralized Non-hierarchical structure Hierarchical structure Personal experience use Formal rules and procedures Subordinates are consulted Subordinates rely fully on management Innovations are supported from top Managers involved in adoption decisions Managers not involved adoption decisions Uncertainty Avoidance Index Skeptical about new products Readily accept new products Innovators are independent from rules Rules constrain innovations Tolerate ambiguity in new situations Avoid ambiguous situations Innovations often welcomed Innovations often resisted Individualism Index Decisions made collectively Decisions made at individual level Innovators involve others People venture in innovation individually Innovations carried out within established networks Innovations carried out outside established networks Social mobility across occupations is low Social mobility across occupations is high Masculinity Index Emphasis on working conditions and relations Emphasis on pay interesting work and Security Stress on quality of work life, solidarity and equality Stress on equity, performance and mutual competition Managers are expected to deal with feelings, seek consensus and use intuition Managers expected to be competitive, assertive, decisive and aggressive Job stress is lower Job stress is higher Long-term Orientation Focus on short-term benefits Focus on establishing long-term relationships and future benefits Short-term virtues are upheld Enduring virtues are upheld According to Hofstede (2001, p. 20), power distance is the extent to which power is unequally distributed among individuals within institutions in a society. In societies where the degree of power distance is high, authority and status are quite important. Such societies are constrained by hierarchical characteristics such as use of formal rules and centralized decision making. Consequently, the level of innovation in such societies is low. Opinion leaders and high-status individuals in such societies tend to influence consumption decisions of the low-status individuals. Thus, adoption of new products in these cultures is slower than in cultures with low power distance. Uncertainty avoidance, on the other hand, refers to the degree to which people within a given culture feel endangered by uncertain situations and the level to which they attempt to keep away from these situations by use of rules and regulations (Gauvin & Sinha 2010, p. 65). Societies featured by high uncertainty avoidance tend to avoid ambiguous situations and are less likely to take risks. In the same vein, firms with a high UAI tend to constrain innovations by rules and have highly formalized management (Mohr, Sengupta & Slater, 2009, p. 236). Such organizations do not take unnecessary risks and only adopt products whose value has been proven in their market. On the other hand, societies that have a low UAI easily accept uncertainties. Thus, individuals in such societies are more willing to take risks and to accept new products. According to Hofstede (2001, p. 22), individualism is the extent to which a person exercises independence from groups or organizations in which he or she belongs to. In individualistic cultures, personal tasks and accomplishments are given priority over a group’s tasks and people are expected to show independence in thinking. On the other hand, some cultures emphasize interdependence, loyalty, identification with groups, solidarity and conflict avoidance. This is known as collectivism. In individualistic cultures, individual rights are paramount and people tend to form looser relationships compared to people living in collectivist cultures. This implies that the impact of word-of-mouth on acceptance of new products in individualistic cultures is less than in collectivist cultures. As Hofstede (2001, p. 27) explains, communication and consultations lead to a delay in the adoption decision process, meaning that people and organizations in collectivist nations may take longer in making product adoption decisions. Masculinity index refers to the value that is attached to the traditionally male and female cultural values. According to Hofstede (2001, p. 28), masculine cultures embrace values such as self achievement, career advancement, ambition, competition and focus on performance. In these cultures, achievement is valued and thus, any products that lead to such achievements are highly valued. Thus, individuals in masculine cultures keep on looking for opportunities for high earnings and advancement. One way of achieving this is to be innovative and to adopt the latest products that are expected to bring success and an elevated status in the society. Thus, individuals and organizations in such societies are more likely to accept new products. In contrast, feminine societies are characterized by values such as social relationships, solidarity, equality, care, intuition and sympathy (Frambach & Schillewaert 2002, p. 304). Recognition, advancement and higher status are less efficient motivators in these cultures and thus, individuals and organizations adopt innovation at a slower rate than in masculine cultures. Finally, long-term orientation index is the degree to which a society depicts a future oriented outlook as opposed to a short-term outlook (Doole & Lowe 2008, p. 91). In long-term oriented cultures, individuals show pertinacity towards slow results and thrift, are doubtful to sudden changes and are cautious toward novel ideas. These cultures are featured by values such as adaptations of traditions to new conditions, persistence and personal adaptableness. On the other hand, short-term cultures pay attention to both the present and the past. Individuals in short-term-oriented cultures value novelty and respect personal stability and tradition. This implies that individuals and organizations in short-term oriented cultures are projected to be more inventive than their counterparts in long-term oriented societies. However, individuals and firms in long-term oriented cultures are more likely to adopt in-depth investments with long-term benefits compared to individuals and organizations in short-term oriented cultures (Yalcinkaya, 2008, p. 207). In general, these dimensions culture are applicable in explaining national consumer innovativeness across different cultures as well as business-to-business adoption. Micro-level variables According Yaveroglu and Donthu (2008, p. 117), there are two micro-level variables affecting adoption status of new products by firms, namely, characteristics of the adopter and the adopter’s perception of the new product. The relevant adopter characteristics in this context are organizational structure, size and innovativeness. Organizational structure in this context may be defined as the level of integration among information systems and processes across various functions within a firm. This aspect is particularly relevant in this case since the main purpose of the ERP software is to integrate business information across various functional areas within a firm. As Yaveroglu and Donthu (2008, p. 117) explain, the size of a firm has also been established to affect product acceptance status. A firm’s innovativeness implies the stance of a firm towards the new product or the readiness of a firm to accept innovation or new ideas. IT-savvy organizations for instance have a high likelihood of investing in ERP software faster than organizations that are IT-conservative. Perceived innovation attributes refer to the value that a firm will derive in investing or in taking up a new innovation on product. Onkvisit and Shaw (2008, p. 340) explain that organizations compare the current situation to the expected situation after implementation, before arriving at the final decision on whether or not to adopt a new product or innovation. They weigh the advantages of adopting the new product or innovation against costs before making the final decision. According to Onkvisit and Shaw (2008, p. 340), the relative benefits of a new product, as viewed by the members of an organization, are positively correlated with its rate of acceptance. As well, the perceived level of compatibility of an innovation with current organizational procedures highly influences the decision on whether to adopt it or not. Onkvisit and Shaw (2008, p. 340) note that adopting ERP software may requires numerous changes in an organization’s work-related procedures and norms. Meso-level variables There are two key meso-level factors that affect the adoption status of a new creation by a firm, namely, supply-side activities at industrial level and industry competitiveness. According to Robertson and Gatignon (1986, p. 47), suppliers’ marketing activities influence the rate at which an innovation or a new product is accepted in the targeted market. The more a supplier is aggressive in marketing ERP software to firms, the more they are likely to adopt it. Secondly, the degree of rivalry that thrives in an industry influences the rate of adoption of new products. Intense rivalry among firms within an industry increases the likelihood of product adoption. Severe competition prompts organizations to adopt new ideas fast so as to enjoy any benefits that may occur and if possible, achieve a competitive edge (Yalcinkaya 2008, p. 204). As such, an organization is likely to adopt ERP system if it is thriving in an industry that is highly competitive. Case Study Waarts and Everdingen (2005, p. 601) conducted a survey on the cultural factors influencing adoption of innovation/new product in 2647 medium-sized companies from ten European nations (France, Finland, Norway, Denmark, Belgium, Netherlands, Italy, Spain, Sweden and the UK). They selected these countries since they perceived them as having varying cultures and hence, they would give different values on most of the Hofstede cultural dimensions. The study included six industries in each country, namely electronics industry, process industry, automotive industry, wholesale industry, project industry, and food and beverage industry. Data for the study was collected using pre-structured questionnaires containing questions about intention or actual plans to adopt ERP and also questions related to meso- and micro-variables (Waarts &Everdingen 2005, p. 608). The researchers developed the questionnaires in English and were subsequently translated by official translation agencies into local languages. Data related to macro-economic variables (Hofstede dimensions on culture) was derived from secondary sources, mainly from domestic chambers of commerce records. The researchers then developed various measures for the variables applied in their research model, as provided in table 2. Adoption status was measured based on whether a firm had adopted the ERP system by 1998 or whether plans were underway to adopt the system by 2000. Table 2: Variables and measures applied in the research Type of variable Variables Measures Dependent variable Adoption of ERP Presence/absence of ERP program in one or more practical areas within an organization at the time of the survey (Yes/No) Independent variables at the meso-level Activities on the supply side Average rise/decline in the budgets allocated to information systems in all firms within an industry/country Industry IT competitiveness Level of awareness of ERP suppliers by firms in an industry/nation (recalled none = 0; recalled one or more =1 Independent variables at the micro-level Benefits of ERP (innovation characteristic) Importance: Position of the best new technology, implementation period and how well it fits with the current procedures and norms (most important =3; not mentioned =0) Attitude towards IT innovation IT conservative = 1 IT mainstream = 2 or IT pioneer = 3 Level of IT integration Extent to which information systems within an organization are attuned to each other (high integration rated 5; low integration rated 1) Resources devoted to IT annually Annual budget allocation on IT ($106)/number of employees When conducting the survey, the researchers divided the sample into 60 segments with distinct characteristics. 45 observations were then taken from each segment. The researchers developed a professional call centre to perform actual sampling and conduct telephone interviews. The recipients targeted in this survey were IT and financial managers involved in making IT purchase decisions. Table 3 shows the results derived from the survey with various descriptive statistics for the sample used. Also, the table gives the sample size of the survey in each country as well as organization’s adoption status 1998 and 2000. The scores on Hofstede’s cultural dimensions derived from the research are given (Waarts & Everdingen 2005, p. 606). Table 3: ERP uptake status between 1998 and 2000 Country ERP Survey Hofstede culture dimensions Sample size ERP penetration 1998(%)a ERP penetration 2000(%)b IND PDl MASC UAI LTO Belgium 251 29 58 75 65 54 94 38 Denmark 232 64 83 74 18 16 23 46 Finland 228 20 69 63 33 26 59 41 France 304 29 62 71 68 43 86 39 Italy 282 41 64 76 50 70 75 34 Netherlands 275 54 72 80 38 14 53 38 Norway 239 32 75 69 31 8 50 44 Spain 316 21 52 51 57 42 86 19 Sweden 221 45 79 71 31 5 29 22 United K. 301 15 48 89 35 66 35 35 All countries 2649 34 65 72 43 34 59 36 Source: Waarts and Everdingen (2005, p. 606). ERP Adoption Standing Across the Ten Nations (Macro-Level) As indicated in table 3, adoption status is yes if a firm had ERP software installed in at least one of its functional areas. The options provided for functional areas in the survey were: inventory and materials management, purchase and sales order management, production and assembly, transportation, marketing and sales, service and maintenance, warehouse management, human resource management and financial accounting (Waarts & Everdingen 2005, p. 607). Figure 2 shows the adoption status across the 10 countries included in the survey. Figure 2: Adoption status across countries Source: Waarts and Everdingen (2005, p. 607). According to Waarts and Everdingen (2005, p. 607), 34% of the firms included in the survey had installed ERP software in one or more functional areas by mid 1998. 47% of the firms that had not installed the ERP system indicated that they had plans to adopt it within the next two years. Interestingly, Denmark, The Netherlands, and Sweden had acceptance rates higher than 45% while Spain, Finland and the UK were lagging far behind with acceptance rates far below 21%, as indicated in figure 2. The data collected indicated that adoption rates in Norway and Finland were expected to increase, though they were relatively low in 1998. The Nordic countries appeared to have greater adoption rates compared to countries from southern Europe in 2000. This shows that there were certain cultural factors at play (Waarts & Everdingen, 2005, p. 607). Macro, Meso and Micro Impacts on ERP Adoption Resolutions Giving consideration to Hofstede’s cultural dimensions results in table three, the countries surveyed can be characterized as accepting medium power levels, somewhat avoiding uncertainty, rather feminine, highly individualistic and paying attention to the short-term. Nonetheless, there is a strong disparity in the values recorded on some of these measures, particularly UAI, MAS and PDI across the nations surveyed. In order to test the influence of the meso- and micro-level variables on adoption of the ERP system, the researchers, came up with three logistic regression equations, with firm’s adoption status being the dependent variable in all of them. The first equation includes the macro-, meso and micro-level variables; the second equation includes meso- and micro-level variables; and the third equation includes only micro-level variables. However, Hofstede’s culture variables were not included in the same model at the same time to avoid the problems of multicollinearity. According to Waarts and Everdingen (2005, p. 608) they conducted a separate assessment for the other Hofstede’s culture variables. Table 4: Logistic regression analyses results (dependent variable: yes/no adoption in 1998) Model I: Micro variables Model II: Micro, meso variables Model III: Micro, meso, macro variables b S.E. Sign. b S.E. Sign. b S.E. Sign. Company characteristics Attitude toward IT innovation .19 .08 .01 .20 .08 .01 .17 .08 .03 Level of IT integration .26 .05 .00 .27 .05 .00 .27 .05 .00 IT budget/employee .44 .27 .10 .12 .28 .66 .31 .29 .28 Perceptions of innovation characteristics Importance of: • Best new technology .14 .08 .10 .09 .09 .29 .14 .09 .11 • Best fit −.16 .04 .00 −.14 .04 .00 −.13 .04 .00 • Implementation period .06 .10 .51 .02 .10 .87 .09 .10 .39 Industry sector (ref.cat = wholesale) .00 .00 • Automotive .30 .18 .09 .32 .18 .08 • Electronics .47 .20 .02 .63 .20 .00 • Food & Beverage .12 .22 .59 .44 .23 .05 • Process industry .07 .19 .70 .14 .20 .47 • Project industry −.37 .20 .07 −.38 .21 .07 IT competitiveness in sector .01 .01 .05 .01 .01 .12 Supply-side activity .75 .11 .00 .79 12 .00 Culture dimensions (Hofstede) Uncertainty Avoidance −.01 .00 .00 Masculinity −.01 .00 .00 Long-Term Orientation .02 .01 .00 Model Chi-square 66,491 (d.f. = 6) (p = .00) 142,22 (d.f. 13) (p = .00) 207,89 (d.f. 16) (p = .00) Nagelkerke R-square .05 .11 .16 % adopters correctly classified 7,11% 23,74% 32,06% Source: Waarts and Everdingen (2005, p. 608). Table 4 indicates that all the three logistic regression models significantly explain organization adoption status. Model I in particular explains firm adoption status, particularly through three micro-level variables, namely, the importance of ‘Best fit’ of the innovation, Level of IT integration and attitude towards IT innovation. Adding meso-level variables increases the model R-square, as noted in table 4. Chi-square test between model I and model II gives a difference of 75.72 with 7 degrees of freedom, which is significant at Read More