Page 1 of 8 ANZMAC 2009 The diffusion of information technology applications in Malaysia's foodservice industry Ahmad Fareed Ismail, University of Western Australia/ University Putra Malaysia, aismail@biz.uwa.edu.au Jamie Murphy, University of Western Australia, jmurphy@biz.uwa.edu.au Boo Huey Chern, University Putra Malaysia, hueychern@yahoo.com Murali Sambasivan, University Putra Malaysia, murali@econ.upm.edu.my Abstract The paper gives perspectives on the two levels of innovation diffusion -adoption and implementation- in the foodservice industry. The study investigates how three organizational factors company affiliation, sufficient capital and company age relate to the adoption and implementation of six information technology (IT) applications by Malaysian foodservice companies. The IT applications fall into two groups, namely basic IT applications and advanced IT applications. A survey conducted among 323 Malaysian foodservice companies in Kuala Lumpur and Selangor. The results of multiple regression analysis revealed that sufficient capital and company affiliation factors related significantly to adoption of basic IT applications. However, only the sufficient capital factor affected the implementation of advance IT applications. Keywords: innovation diffusion, IT applications, foodservice, organizational factors
ANZMAC 2009 Page 2 of 8 The diffusion of information technology applications in Malaysia's foodservice industry Introduction The diffusion of innovations attracts studies from multidisciplinary perspectives (Carter, Jambulingam, Gupta, & Melone, 2001; McGrath & Zell, 2001). The studies mostly stem from Rogers (2003) Diffusion of Innovations (DOI) theory. DOI tends to describe the patterns of adoption, explain the mechanism, and assist in predicting the success of new innovations adopted (Rogers, 2003). However, research comparing the factors affecting the different innovations diffusion stages-adoption and implementation- is still limited and demand for further investigation (Grover & Goslar, 1993). An innovation is subjective and depends on the perception of an individual, group or organization (Rogers, 2003). However, innovations in recent years typically refer to information technologies (IT) or rather information and communication technologies (McGrath & Zell, 2001; Rogers, 2003). For instance, DOI studies examine the Internet, websites, knowledge management systems, e-mail, data warehouse systems and e-customer services (Hollenstein, 2004; Hwang, Ku, Yen, & Cheng, 2004; Murphy, Olaru, & Schegg, 2006; Ryan & Prybutok, 2001). Yet, research on foodservice IT applications is sparse and academics call for further research to investigate IT diffusion in this industry (O'Connor & Murphy, 2004; Oronsky & Chathoth, 2007; Rodgers, 2007). Therefore, this study selected and classified six foodservice IT applications into two groups, Basic and Advanced, which reflect their level of innovation diffusion. First, the Basic IT applications reflected the adoption stage of IT in the foodservice industry. The three Basic IT applications are at the adoption level: Point-of-Sales (POS) systems, Labor Management Systems, and Table Management Systems. Meanwhile, advanced IT applications are extensions of POS systems. Thus, the three IT applications in this group menu management systems, recipe costing, and reporting features suggest IT implementation in the foodservice industry. These IT applications help foodservice companies deal with complex foodservice operations. For example, menu management systems help foodservice companies develop menu variations based on customer demands. Furthermore, menu management systems help manage inventories and automatically reflect necessary purchases. In conclusion, this paper addresses two questions to compare the diffusion of basic and advanced IT applications in foodservice industry: 1. How well do the three organizational variables predict the diffusion of Basic and Advanced IT applications? 2. Which variable is the best predictor of the diffusion? Hypotheses Company affiliation, independent or chain foodservice companies, relates to the interconnectedness factor that is positively associated with organizational innovativeness (Rogers, 2003). Similarly, the number of departments and their level
Page 3 of 8 ANZMAC 2009 of connectivity relate to the diffusion of innovations (Nijssen & Frambach, 2000). The higher the company interconnectedness, either in numbers or communications, the greater the likelihood for a company to adopt an innovation (Zhu, Kraemer, & Xu, 2003). For advanced innovations (e.g., Electronic Data Interchange), interconnectedness among departments or business partners positively contributes to company implementation (Frambach & Schillewaert, 2002). Similarly, implementation of higher level technology in Advance Manufacturing Technology, such as Just-In-Time production and Manufacturing Resources Planning also relates to company collaboration and interconnectedness (Burgess & Gules, 1998). Therefore, company affiliation will relate positively to the adoption of IT applications. H1a: There is a positive relationship between company affiliation and adopting basic IT applications H1b: There is a positive relationship between company affiliation and implementing advanced IT applications. Organizational slack reflects available resources in an organization and the demands made upon them (Tornatzky & Fleischer, 1990). Organizational slack can be human resources or sufficient capital. This paper investigates sufficient capital and the diffusion of foodservice IT applications. Sufficient capital is a safety measure to prevent an organization from facing a financial problems after the innovation diffusion (Wang & Cheung, 2004). An organization with excess resources might provide funds for adoption of innovation (Tornatzky & Fleischer, 1990). In addition, sufficient capital relates with the degree of e-business implementation among travel agencies (Wang & Cheung, 2004). Thus, sufficient capital is a catalyst for strategic organizational decision on innovation implementation (Bourgeois, 1981). Therefore, sufficient capital will relate positively to adoption of basic IT applications and implementation of advance IT applications in foodservice industry. H2a: There is a positive relationship between sufficient capital and adopting basic IT applications H2b: There is a positive relationship between sufficient capital and implementing advanced IT applications. Research suggests that organization age relates negatively with innovation adoption (Flanagin, 2000; Kimberly & Evanisko, 1981; Murphy & Tan, 2003; Nguyen, Murphy, & Olaru, 2003). Younger organizations tend to adopt innovations faster compared to older organizations (Flanagin, 2000; Kimberly & Evanisko, 1981). With regard to implementation and adoption, respectively, a study in Singapore found that compared to older travel agencies, younger agencies were more responsive to their email queries and more likely to use branded e-mail addresses (Murphy & Tan, 2003). Similarly, compared to younger Australian businesses, older businesses used more branded e-mail address (Nguyen, et al., 2003). Meanwhile, implementation of innovations among the intermediate ages and entering firms Spanish firms showed faster pace compared to oldest firms (Elena & Jordi, 2004). Based on these
ANZMAC 2009 Page 4 of 8 arguments, this paper predicts that younger foodservice companies will be more aggressive with IT applications adoption than older foodservice companies. H3a: There is a negative relationship between company age and adopting basic IT applications H3b: There is a negative relationship between company age and implementing advanced IT applications. Methodology This study was a survey of Chief Executive Officers, Owners, Information System or Information Technology Managers, Operations Managers, and Account/Financial Managers in 323 Kuala Lumpur and Selangor foodservice companies. These respondents selected based on the positions available in the foodservice companies themselves. The sample stemmed from the 2004 Malaysian Hotel and Restaurant Purchasing Handbook, a comprehensive list of Malaysian hotel and restaurant companies. A census, all restaurants from Kuala Lumpur and Selangor invited to participate in this study. This study selected the locations because these are the main capitals in Malaysia with high numbers of foodservice outlets and could support the argument in the foodservice study. The survey from September 2006 through January 2007 yielded 145 useable surveys, a response rate of almost forty-five percent. This study used three independent variables namely years the company was in business, independent or chain foodservice company, and sufficient capital. The dependent variables were two groups of IT applications mentioned earlier in this paper, Basic and Advanced IT applications. Respondents indicated either their company had decided or adopted each of the IT application. Results H1. Company Affiliation An independent sample t-test in relation to company affiliation tested for mean differences between chain and independent foodservice companies. The results shows a significant difference in the adoption of basic IT applications by chain foodservice companies and independent foodservice companies [t(143)= -6.533, p<0.001]. Moreover, there was a significant difference in the implementation of advance IT applications by chain foodservice companies and independent foodservice companies [t(143)= -6.175, p<0.001]. These results support both H1a and H1b; company affiliation relates positively to IT adoption and implementation. H2. Sufficient Capital The relationships between adoption of basic IT applications and implementation of advance IT applications with sufficient capital was examined using Spearman s correlation coefficient. There was a positive correlation between adoption of basic IT applications and sufficient capital (rho=.722, n=145, p<.001). Furthermore, there was a positive correlation between implementation of advance IT applications and sufficient capital (rho=.769, n=145, p<.001). These findings support H2a and H2b. The more sufficient capital available, the more likelihood of those IT applications adopted and implemented by the foodservice companies.
Page 5 of 8 ANZMAC 2009 H3. Company Age A Pearson s correlation coefficient was performed to investigate relationships between company age with the adoption of basic IT applications and implementation of advance IT applications. There was a positive correlation between adoption of basic IT applications and company age (r=.174, n=143, p=.019). Moreover, there was a positive correlation between implementation of advance IT applications and company age (r=.155, n=143, p=.032). The findings, however, failed to support H3a and H3b hypotheses that younger companies were likely to adopt and implement faster than older companies. Multiple Regressions Multiple linear regressions were performed to investigate the contribution of each independent variable to the overall model (Hair, Black, Babin, Anderson, & Tatham, 2006). Results for the adoption of basic IT applications and implementation of advance IT applications are in Table 1. Independent variables Table1: Results of regressions analysis Basic IT Adv IT β t p β t p Company Affiliations.152 2.258.026.059 1.032.304 Sufficient Capital.628 9.275 <.001.777 13.520 <.001 Company Age.065 1.108.270.035.713.477-2.592.011-0.962 <.001 Constant R 2.533.664 Adjusted R 2.523.656 F 52.91 91.39 P.001.001 The model explained 52.3% of the variance in basic IT applications adoption. As standardized β-values reflect the importance of each factor, sufficient capital makes the strongest contribution to explain the adoption of basic IT applications in the Malaysian foodservice industry. Company affiliation (p=.026) and sufficient capital (p<.001) made a statistically significant contribution to the prediction of basic IT applications adoption, but not company age. Compared to the basic IT adoption model, the advance IT implementation model explained 65.6% of the variance. However, only sufficient capital (p<.001) made a statistically significant contribution to predicting advanced IT application implementation. Discussion The results supports the hypotheses related to company affiliation and sufficient capital in both the adoption of basic IT applications and implementation of advanced IT applications. However, despite negative relationships hypothesized, company age showed significant positive relationships with both types of IT applications. The results might due to the importance of both IT applications acknowledged by the older
ANZMAC 2009 Page 6 of 8 companies. These companies probably adopted and implemented the technologies earlier to increase the market barrier to the newer companies (Porter, 2001). Meanwhile, only company affiliation and sufficient capital were significant predictors of adopting basic IT applications. Only sufficient capital, however, predicted implementing advanced IT applications. In contrast to the univariate findings, company age was an insignificant predictor of both IT adoption and implementation. Conclusion and Implications The study extends DOI by exploring basic and advanced IT application in the foodservice industry. The univariate results support the two independent variables sufficient capital and affiliation related to technology adoption found in other industries (Zhu, et al., 2003). However, the positive relationships found for organizational age, challenge previous findings (Flanagin, 2000; Kimberly & Evanisko, 1981; Murphy & Tan, 2003; Nguyen, et al., 2003). This study suggested that the older the company, the more they accept the innovation. Perhaps, it is a phenomenon in the foodservice industry that merits in depth investigation. Few studies, however, have investigated organizational factors affecting IT adoption in hospitality, particularly in foodservice (O'Connor & Murphy, 2004; Oronsky & Chathoth, 2007). Moreover, to the authors' knowledge, this is the first hospitality study to examine simultaneously the diffusion of several basic and advanced IT applications. Most studies investigate the adoption of one or two innovations, such as spreadsheets, websites, e-mail, e-customer services, data warehouse systems, and knowledge management systems, without any classification (Hollenstein, 2004; Hwang, et al., 2004; Murphy, et al., 2006; Ryan & Prybutok, 2001). Therefore, this study illustrates examining multiple innovations and multiple diffusion stages. The findings suggest that in the foodservice industry, company age does not effect the diffusion of IT applications. Thus, suppliers or IT providers should use these findings to plan their marketing strategies, approaching all foodservice companies regardless of company age. Furthermore, suppliers should focus on companies with sufficient capital for all IT applications and the chains foodservice companies for basic IT applications. The results of this study also propose to the foodservice companies that have planned to use these IT applications to consider their sufficient capital before their decisions. Inappropriate decisions will cause the company financial troubles after the adoption and implementation. This situation, known as bandwagon effects, reflects the decision made based on popular trend rather than understanding the benefits, usage and needs of the IT applications on an organization (Flanagin, 2000). Finally, this study excluded organizational size, even though it is one the most studied factors in the literature (Hollenstein, 2004; Hwang, et al., 2004; Patterson, Grimm, & Corsi, 2003). The inclusive findings of organizational size demands comprehensive studies especially in unexplored fields such as the foodservice industry. Furthermore, in response to the importance of sufficient capital, future research could study on the suitability and affordability of IT applications within the foodservice industry.
Page 7 of 8 ANZMAC 2009 References Bourgeois, L. J. (1981). On the measurement of organizational slack. The Academy of Management review, 6(1), 29. Burgess, T. F., & Gules, H. K. (1998). Buyer-supplier relationships in firms adopting advanced manufacturing technology: an empirical analysis of the implementation of hard and soft technologies. Journal of Engineering and Technology Management, 15(2-3), 127-152. Carter, F. J., Jambulingam, T., Gupta, V. K., & Melone, N. (2001). Technological innovations: a framework for communicating diffusion effects. Information & Management, 38(5), 277-287. Elena, H., & Jordi, J. (2004). How Does Probability of Innovation Change with Firm Age?. Small Business Economics, Volume 22(3-4 ), 193-207. Flanagin, A. J. (2000). Social Pressures on Organizational Website Adoption. Human Communication Research, 26(4), 618-646. Frambach, R. T., & Schillewaert, N. (2002). Organizational innovation adoption: a multi-level framework of determinants and opportunities for future research. Journal of Business Research, 55(2), 163-176. Grover, V., & Goslar, M. D. (1993). The Initiation, Adoption, and Implementation of Telecommunications Technologies in U.S. Organizations. Journal of Management Information Systems, 10(1), 141-163. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis (Sixth ed.). Upper Saddle River, NJ: Prentice Hall. Hollenstein, H. (2004). Determinants of the adoption of Information and Communication Technologies (ICT): An empirical analysis based on firm-level data for the Swiss business sector. Structural Change and Economic Dynamics, 15(3), 315-342. Hwang, H.-G., Ku, C.-Y., Yen, D. C., & Cheng, C.-C. (2004). Critical factors influencing the adoption of data warehouse technology: a study of the banking industry in Taiwan. Decision Support Systems, 37(1), 1-21. Kimberly, J. R., & Evanisko, M. J. (1981). Organizational Innovation: The Influence of Individual, Organizational, and Contextual Factors on Hospital Adoption of Technological and Administrative Innovations. Academy of Management Review, 24(4), 689-713. McGrath, C., & Zell, D. (2001). The Future of Innovation Diffusion Research and its Implications for Management: A Conversation with Everett Rogers. Journal of Management Inquiry, 10(4), 386-391. Murphy, J., Olaru, D., & Schegg, R. (2006). Investigating the Evolution of Hotel Internet Adoption. Information Technology and Tourism, 8(3/4), 161-178. Murphy, J., & Tan, I. (2003). Journey to Nowhere? Electronic Customer Service by Travel Agents in Singapore. Tourism Management, 24(5), 543-550. Nguyen, D. T. H. C., Murphy, J., & Olaru, D. (2003). Investigating the Adoption of Electronic Customer Service by Australian Businesses. Managing Service Quality, 13(6), 492-503. Nijssen, E. J., & Frambach, R. T. (2000). Determinants of the Adoption of New Product Development Tools by Industrial Firms. Industrial Marketing Management, 29(2), 121-131. O'Connor, P., & Murphy, J. (2004). A Review of Research on Information Technology in the Hospitality Industry. International Journal of Hospitality Management, 23(5), 473-484.
ANZMAC 2009 Page 8 of 8 Oronsky, C. R., & Chathoth, P. K. (2007). An exploratory study examining information technology adoption and implementation in full-service restaurant firms. International Journal of Hospitality Management, 26(4), 941-956. Patterson, K. A., Grimm, C. M., & Corsi, T. M. (2003). Adopting new technologies for supply chain management. Transportation Research Part E: Logistics and Transportation Review, 39(2), 95-121. Porter, M. (2001). Strategy and the Internet. Harvard Business Review, 79(3), 63-77. Rodgers, S. (2007). Innovation in food service technology and its strategic role. International Journal of Hospitality Management, 26(4), 899-912. Rogers, E. M. (2003). Diffusion of Innovations (Fifth ed.). New York, New York: Simon & Schuster. Ryan, S. D., & Prybutok, V. R. (2001). Factors affecting the adoption of knowledge management technologies: A discriminative approach Journal of Computer Information Systems, 41(4), 31-37. Tornatzky, L. G., & Fleischer, M. (1990). The process of technological innovation. New York: Lexington Book. Wang, S., & Cheung, W. (2004). E-Business Adoption by Travel Agencies: Prime Candidates for Mobile e-business. International Journal of Electronic Commerce, 8(3), 43-63. Zhu, K., Kraemer, K., & Xu, S. (2003). Electronic business adoption by European firms: a cross-country assessment of the facilitators and inhibitors. Eur J Inf Syst, 12(4), 251-268.