본문 바로가기
KERD
논문KCI 등재

The Usage Needs and Adoption Intention of Manufacturing Big Data Technology in Small and Medium-sized Manufacturing Companies

1

조회

0

다운로드

16

인용

0

참고문헌

키워드

주제분류경영학

초록

Considerable attention has been paid to smart manufacturing which utilizes manufacturing big data technology. Previous studies on the utilization of manufacturing big data have mainly focused on large corporations or conglomerates. For example, they can quickly develop vaccine through big data or successfully use big data in predicting the situation in which the parts of their product may experience breakdown. However, the small and medium-sized manufacturing companies(SMMCs) are facing difficulties in realizing and predicting their need for utilizing manufacturing big data technology. It is difficult to implement technology of which the usage needs are unclear. For this reason, only 25% of firms are reported to utilize the big data technology. The purpose of this paper is to identify the underlying needs for manufacturing big data in the context of SMMCs and to assess the extent to which those needs are related to the intention to adopt the manufacturing big data technology. Additionally, this study is designed to identify which companies have higher level of usage needs of the manufacturing big data technology This study comprehensively reviewed previous literature and conducted in-depth interviews with three domestic manufacturing firms in order to understand potential usage needs of SMMCs for the manufacturing big data technology. This study developed the following three big dimensions of manufacturing big data usage need based on the comprehensive review of literature and interview - (1) traditional production needs(cost, quality, flexibility, delivery), (2) social responsibility needs(environmental protection and safety), and (3) customer service-oriented new business development needs. Also, this study suggested the following two driving factors positively influencing the needs - (1) competition and (2) information technology capabilities. This study developed a research model that presents two driving factors → three dimensions of usage needs of manufacturing big data → adoption intention of manufacturing big data technology. Further, this study conducted a survey with 200 SMMCs in order to empirically test the validity of the research model. The empirical test results were summarized as follows. First, the significant usage needs of small and medium-sized manufacturing companies for the manufacturing big data technology include (i) the need to improve production performances in terms of cost, quality, flexibility, and delivery, and (ii) the need to improve social responsibility performances in the areas of environmental protection and safety. These needs were found to be significantly correlated with the intention of SMMCs to adopt the manufacturing big data technology. However, SMMCs do not want to use manufacturing big data to develop a customer service-oriented new business model. Second, competition was correlated with the needs to utilize manufacturing big data to improve both production and social responsibility performances in a positive manner. Third, as in the case of competition, SMMCs with higher level of IT capabilities are likely to have stronger need to use manufacturing big data to increase their production and social responsibility performances. This study has following practical implications. First, the results of this study indicate that the intention of the SMMCs to adopt manufacturing big data technology are related to their needs to improve the performances in traditional production areas and social responsibility areas. These are the perceived purposes of the small and medium-sized companies to introduce the manufacturing big data technology. Therefore, Korean government research institutes or technical consulting firms have to develop appropriate manufacturing big data technologies to meet these purposes. For example, as small and medium-sized manufacturing companies do not introduce manufacturing big data technologies to develop a new business model that is customer service-oriented, the manufacturing big data technology platform must not be overdesigned. Second, this study suggests competition and IT capabilities as the driving factors influencing the needs for utilizing manufacturing big data. Recently, Korean government or Korean government agencies have been trying to promote the widespread adoption of the manufacturing big data technology. Further, they currently carry out policies for selecting companies that are in need of technology support of the manufacturing big data technology. When choosing the companies for such a support, they might prefer to selecting the small and medium-sized manufacturing companies which have the high level of IT capabilities and are faced with fierce competition.

Abstract

Considerable attention has been paid to smart manufacturing which utilizes manufacturing big data technology. Previous studies on the utilization of manufacturing big data have mainly focused on large corporations or conglomerates. For example, they can quickly develop vaccine through big data or successfully use big data in predicting the situation in which the parts of their product may experience breakdown. However, the small and medium-sized manufacturing companies(SMMCs) are facing difficulties in realizing and predicting their need for utilizing manufacturing big data technology. It is difficult to implement technology of which the usage needs are unclear. For this reason, only 25% of firms are reported to utilize the big data technology. The purpose of this paper is to identify the underlying needs for manufacturing big data in the context of SMMCs and to assess the extent to which those needs are related to the intention to adopt the manufacturing big data technology. Additionally, this study is designed to identify which companies have higher level of usage needs of the manufacturing big data technology This study comprehensively reviewed previous literature and conducted in-depth interviews with three domestic manufacturing firms in order to understand potential usage needs of SMMCs for the manufacturing big data technology. This study developed the following three big dimensions of manufacturing big data usage need based on the comprehensive review of literature and interview - (1) traditional production needs(cost, quality, flexibility, delivery), (2) social responsibility needs(environmental protection and safety), and (3) customer service-oriented new business development needs. Also, this study suggested the following two driving factors positively influencing the needs - (1) competition and (2) information technology capabilities. This study developed a research model that presents two driving factors → three dimensions of usage needs of manufacturing big data → adoption intention of manufacturing big data technology. Further, this study conducted a survey with 200 SMMCs in order to empirically test the validity of the research model. The empirical test results were summarized as follows. First, the significant usage needs of small and medium-sized manufacturing companies for the manufacturing big data technology include (i) the need to improve production performances in terms of cost, quality, flexibility, and delivery, and (ii) the need to improve social responsibility performances in the areas of environmental protection and safety. These needs were found to be significantly correlated with the intention of SMMCs to adopt the manufacturing big data technology. However, SMMCs do not want to use manufacturing big data to develop a customer service-oriented new business model. Second, competition was correlated with the needs to utilize manufacturing big data to improve both production and social responsibility performances in a positive manner. Third, as in the case of competition, SMMCs with higher level of IT capabilities are likely to have stronger need to use manufacturing big data to increase their production and social responsibility performances. This study has following practical implications. First, the results of this study indicate that the intention of the SMMCs to adopt manufacturing big data technology are related to their needs to improve the performances in traditional production areas and social responsibility areas. These are the perceived purposes of the small and medium-sized companies to introduce the manufacturing big data technology. Therefore, Korean government research institutes or technical consulting firms have to develop appropriate manufacturing big data technologies to meet these purposes. For example, as small and medium-sized manufacturing companies do not introduce manufacturing big data technologies to develop a new business model that is customer service-oriented, the manufacturing big data technology platform must not be overdesigned. Second, this study suggests competition and IT capabilities as the driving factors influencing the needs for utilizing manufacturing big data. Recently, Korean government or Korean government agencies have been trying to promote the widespread adoption of the manufacturing big data technology. Further, they currently carry out policies for selecting companies that are in need of technology support of the manufacturing big data technology. When choosing the companies for such a support, they might prefer to selecting the small and medium-sized manufacturing companies which have the high level of IT capabilities and are faced with fierce competition.

참고문헌 (52)

  1. Bagozzi, R. P., "On the evaluation of structural equation models", Journal of the Academy of Marketing Science, 16(1), pp.74~94 (1988)
  2. Barney, J., "Firm resource and sustained competitive advantage", Journal of Management, 17(1), pp.99~120 (1991)
  3. Cebr, "Data equity: Unlocking the value of big data", ~ (2012)
  4. The overview of predictive manufacturing system, "Chi, S. Y.", Electronics and Telecommunications Research Institute, ~ (2014)
  5. The industry 4. 0 of German and its implication, "Cho, H. J.", Hyundai Research Institute, ~ (2013)
  6. Cho, W. S., "The analytical tools for utilizing manufacturing big data", Chungbuk National University Seminar (2014)
  7. Choi, J. S., "Big data, 3D printer, internet of things", Dong-A Business Review, pp.80~86 (2014)
  8. Davenport, T. H., "Enterprise analytics: Optimize performance, process, and decisions through big data" (2013), FT Press
  9. Dutta, D., "Managing a big project : The case of Ramco Cements Limited", International Journal of Production Economics, 165, pp.293~306 (2015)
  10. "Big data solution development for government and small and medium-sized manufacturing firms" (2013)[URL]
  11. Ferdows, K., "Lasting improvements in manufacturing performance : In search of a new theory", Journal of Operations Management, 9, pp.168~183 (1990)
  12. Frizzo-Barker, J., "An empirical study of the rise of big data in business scholarship", International Journal of Information Management, 36, pp.403~413 (2016)
  13. 곽교, "기업의 사회적 책임이 기업에 대한 신뢰 및 평판과 고객충성도에 미치는 영향: 소비자의 윤리적 의식의 조절효과를 중심으로", 기업경영연구, 23(2), pp.23~42 (2016), 한국기업경영학회[DOI]
  14. Hair, J. F., "Multivariate data analysis with readings" (2006), Macmillan Publishing Company
  15. Hamel, G., "The future of management" (2007), Harvard Business School Press
  16. Iansiti, M., "Digital ubiquity : How connections, sensors, and data are revolutionizing business", Harvard Business Review, pp.90~99 (2014)
  17. ITU, "Internet of things global standards initiative", ~ (2015)
  18. Jang, Y. J., "Utilizing big data technology in manufacturing sector", Information and Telecommunication, 29(11), pp.30~35 (2012)
  19. Jang, Y. J., "New paradigm as seen in Google’s unmanned automobile", Dong-A Business Review, pp.64~70 (2012)
  20. Kim, G. H., "Customizing and analyzing big data", Dong-A Business Review, pp.72~78 (2012)
  21. 김근아, "기업 내적 IT 자원이 기업 민첩성과 성과에 미치는 영향: 관리적 IT 능력과 경영진 존재의 조절효과", Information Systems Review, 15(3), pp.39~69 (2013), 한국경영정보학회
  22. Kim, J. D., "The utilization strategy of big data in the manufacturing strategy", School of Business Seminar on Big Data (2014), Chungbuk National University
  23. 김지대, "가치혁신 생산전략의 성과: 생산전략의 새로운 패라다임", 기업경영연구, 20(2), pp.273~293 (2013), 한국기업경영학회
  24. 김진호, "경영학 연구에서의 구조방정식 모형의 적용: 문헌 연구와 비판", 경영학연구, 36(4), pp.897~923 (2007), 한국경영학회
  25. 김종영, "공급사슬 지향성, 민첩성 및 성과 간의 관계: 수도권 소재 중소 제조 기업을 대상으로", 기업경영연구, 22(5), pp.229~247 (2015), 한국기업경영학회
  26. Kim, K. Y., "Mastering the quality staircase, step by step", Business Horizons, pp.17~21 (1997)
  27. Kim, S. B., "Utilizing big data for improving manufacturing process quality", ie Magazine, 20(2), pp.42~45 (2013)
  28. 2013 database white paper, "Korea Database Agency", ~ (2013)
  29. A guide of assisting information innovation clusters by industrial sectors, "Korea Federation of SMEs", ~ (2010)
  30. An approach for utilizing big data for the competitiveness of manufacturing industry, "Lee, H. H.", Korea Institute for Industrial Economics & Trade, ~ (2013)
  31. Lee, H. H., "Utilizing big data for reinforcing manufacturing firms’ competitiveness", KIET Industrial Economy, pp.45~54 (2014)
  32. Lee, J., "Internet of things in the manufacturing industry", Dong-A Business Review, pp.60~65 (2014)
  33. Lee, J., "Recent advances and trends in predictive manufacturing systems in big data environment", Manufacturing Letters, 1, pp.38~41 (2013)
  34. 이종운, "클라우드 서비스 생태계 활성화 방안: 공급자와 사용자 관점 기반", Entrue Journal of Information Technology, 13(3), pp.73~88 (2014), 엘지씨엔에스
  35. Lee, O. S., "A model on priority-setting of technology investment based on demand response degree and technology gap: In the case of geo-technology", Phd, pp.2013
  36. Lopez Research, "Building smarter manufacturing with the internet of things(IoT)", ~ (2014)
  37. McKinsey Global Institute, "Big data: The next frontier for innovation, competition, and productivity" (2011), Mckinsey & Company
  38. McKinsey Global Institute, "Game changers: Five opportunities for US growth and renewal" (2013), Mckinsey & Company
  39. Mello, R., "Is big data the next big thing in performance measurement systems?", Proceedings of the 2014 Industrial and Systems Engineering Research Conference, pp.1~10 (2014)
  40. Min, G., "Utilizing big data in the manufacturing industry", 2013 Korea Management Science, 2013, 719~736
  41. 노규성, "제조실행시스템에의 빅데이터 적용방안에 대한 탐색적 연구", 디지털융복합연구, 12(1), pp.305~311 (2014), 한국디지털정책학회[DOI]
  42. Park, E. L., "Data based segmentation and summarization for sensor data in semiconductor manufacturing", Expert Systems with Applications, 41, pp.2619~2629 (2014)
  43. Park, H., "The fourth industrial revolution and cyber-physical system", Dong-A Business Review, pp.52~59 (2014)
  44. Park, J., "A study about modeling of big data system to improve the petrochemical processing", Phd, pp.2014
  45. Setlur, B., "Informed manufacturing: The next industrial revolution", Cognizant, pp.1~13 (2014)
  46. Shih, W. C., "What it takes to reshore manufacturing successfully", MIT Sloan Management Review, pp.55~62 (2014)
  47. The typology and status of Big data technology, "Shin, S.", National Information Society Agency, Big Data Strategy Center, ~ (2013)
  48. Shin, S. -J., "Predictive analytics model for power consumption in manufacturing", Procedia CIRP, 15, pp.153~158 (2014)
  49. Shin, Y., "The evolving analytical tool", Dong-A Business Review, pp.88~91 (2012)
  50. Vargo, S. L., "Evolving to a new dominant logic for marketing", Journal of Marketing, 68, pp.1~17 (2004)
  51. Wernerfelt, B., "A resource-based view of the firms", Strategic Management Journal, 5, pp.171~180 (1984)
  52. Yoo, K. H., "Department of Software Seminar on Big Data", Chungbuk National University (2014), Chungbuk National University

관련 논문AI추천

이 간행물의 다른 논문 (7건)