key knowledge for it-enabled business process improvement
TRANSCRIPT
364 Int. J. Learning and Intellectual Capital, Vol. 2, No. 4, 2005
Copyright © 2005 Inderscience Enterprises Ltd.
Key knowledge for IT-enabled business process improvement
Phasit Kanjanasanpetch and Barbara Igel* School of Management, Asian Institute of Technology P.O. Box 4, Klong Luang, Pathumthani, 12120, Thailand Fax: +662 5245667 E-mail: [email protected] E-mail: [email protected] *Corresponding author
Abstract: Enterprise Resource Planning (ERP) is a popular tool to improve business processes. However, numerous companies have failed to realise benefits from investing in ERP. Many studies tried to identify the reasons for failure by looking at a wide range of organisational factors, but gave little attention to the role of knowledge resources. This research investigates the key knowledge resources required for successful ERP implementation. The result of five case studies revealed four knowledge types critical for ERP implementation.
Keywords: knowledge; knowledge management; Enterprise Resource Planning (ERP) implementation; IT-enabled business process improvement.
Reference to this paper should be made as follows: Kanjanasanpetch, P. and Igel, B. (2005) ‘Key knowledge for IT-enabled business process improvement’, Int. J. Learning and Intellectual Capital, Vol. 2, No. 4, pp.364–378.
Biographical notes: Phasit Kanjanasanpetch obtained his Bachelor of Engineering (Electrical) from Prince of Songkla University in 1988 and the MBA from Asian Institute of Technology (AIT) in 1995. He has worked in the IT industry for almost ten years as implementation consultant with an ERP vendor and as IT manager in manufacturing companies. His experience is related with the implementation of ERP and e-business solutions. He is currently in the process of obtaining a PhD degree in Management of Technology at AIT school of management. His PhD research deals with managing knowledge in business process innovation by means of implementing IT solutions.
Dr. Barbara Igel received her MA, Economics (1984) from the Technical University Berlin (West) and Doctor rerum politicae, Economics (1989) from the Freie University Berlin (West). She is Associate Professor in Management of Technology, and Coordinator of New Tech-Ventures in the Management of Technology programme, School of Management, Asian Institute of Technology, Bangkok. She has been Visiting Professor for several times at the Helsinki University of Technology, Finland, and worked as a consultant to the World Bank in the IDA credit programme for small-scale export industries in Pakistan. Her research projects deal with the management of innovation in complex technology systems and entrepreneurship development in new, technology-based companies in Asia. Her papers have been published in Technovation, International Journal of Technology Management, International Journal of Entrepreneurship and Innovation Management, Journal of Asian Business, Asian Case Research Journal, among others.
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1 Introduction
The management of knowledge is increasingly considered as main source of competitive advantage for corporations (Grant, 1996; Krogh et al., 2001; Nonaka, 1991; Nonaka and Takeuchi, 1995; Prahalad and Hamel, 1990; Teece, 2000; Zack, 1999). The sustainable competitive advantage of business firms flows from the use of difficult-to-imitate commercial and industrial knowledge assets (Grant, 1999; Penrose, 1999; Spender, 1999). However, knowledge does not usually command significant value until it is embedded in products or processes. One possible way to study how knowledge creates value to organisations is to assess how knowledge is managed in each value activities within the firm’s value chain (Porter, 1985). Information Technology (IT) implementation is one of the supportive activities in the value chain that creates value to organisations by improving productivity. Recent empirical research revealed that IT investments did result in productivity improvements (Brynjolfsson and Hitt, 1998).
Enterprise Resource Planning (ERP) is a corporate IT innovation used to improve business processes and requires redesigning business processes. Dozens of concepts and methodologies for Business Process Redesign (BPR) have been developed since 1993 (see examples in Turban et al., 1999), and thus, there is an agreement among all that IT is the major enabler, and BPR rarely succeeds without IT. Recently, the ERP market worldwide experienced its second straight year of double-digit decline, but the total market value was still very high at $US5.5 billion in 2001 and $US5 billion in 2002 (Gartner Dataquest Predicts, 2002; Gartner Group, 2003). Gartner expects the market to rebound in 2004 (Gartner Dataquest Predicts, 2002), and the eventual market size is projected to reach $US1 trillion by the year 2010 (Bingi et al., 1999).
Successful implementation of IT-enabled business process improvement, in addition to the organisational factors listed above, must synthesise existing knowledge. However, most research investigated the use of IT solutions as enabling tools for knowledge management. None of the IT management concepts found in the literature understood the contribution of knowledge resources in IT-driven business process improvement. This pilot study, taking the resource-based perspective of the firm, explores the required key knowledge resources for designing IT-enabled business processes with the help of IT solutions and why some companies are more successful than others in gaining competitive advantage through new business processes. Five corporate project cases were investigated to answer the set research questions, and identify key knowledge resources the companies applied in IT-enabled business process improvement. The findings will identify the importance of different knowledge types for IT-enabled business process improvement, provide guidelines for managing knowledge for business process performance improvement, and add the knowledge perspective into IT projects management.
2 Literature review
2.1 Knowledge as a key resource for business process improvement
Knowledge is a derivative of symbols, data, and information (Probst et al., 2000). Data is the outcome of interaction between rules of syntax and symbols while information is the result of interpretation of data within a particular context. Information, when networked, can be used in a particular field of activity, thus becoming knowledge.
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2.2 Knowledge classifications
The KM literature classified knowledge in a number of categories to pursue different research interests. Various classifications of knowledge also imply recognition of different ways of using the same piece of knowledge, depending on the context and transformation processes involved in achieving goals or knowledge outputs. Table 1 summarised the classifications of knowledge at the firm level and individual level that are commonly used in literature.
Table 1 Summary of knowledge classification in the literature
Proponents Knowledge classification
Organisational knowledge
Roos and Roos (1997) (three types)
Human capital Organisational capital Customer and relationship capital
Sveiby (2001) (three types)
Employee competencies
Internal structure External structure
Teece (2000) (three types)
Personal knowledge
Organisational knowledge External knowledge
Petrash (1996) (three types)
Human capital Organisational capital Customer capital
Fernandez et al. (2000)(four intangible resources)
Human knowledge
Technological knowledge
Organisational knowledge
Relational knowledge
Castro (2002) (four intellectual capital)
Human capital Technology capital
Organisational capital
Relational capital
There are two main streams of knowledge classifications at the firm level. The first stream categorised corporate knowledge into three groups: i.e., knowledge that resides in employees, organisation resources, and external partnerships with customers, suppliers, consultants, and corporate allies (Petrash, 1996; Roos and Roos, 1997; Sveiby, 2001; Teece, 2000).
The second stream differentiated organisational knowledge resources into four types by distinguishing technological from organisational knowledge (Castro, 2002; Fernandez et al., 2000). This four-type model of organisational knowledge is composed of human knowledge, organisational knowledge, technological knowledge, and relational knowledge. Both streams of knowledge classification were developed mainly to assess organisational knowledge and formulate knowledge strategy.
Technological knowledge of IT solutions such as ERP is an important knowledge type for improving business processes. This knowledge needs to be distinguished clearly to measure its contribution to the IT-enabled business improvement success. A more detailed knowledge classification provides better insights into the importance of various knowledge types in achieving IT-enabled business process improvements. Therefore, the four-type knowledge classification of Castro and Fernandez seems to be more relevant for the study of IT-enabled business process improvement than other classifications that lump technological knowledge with other types of organisational knowledge.
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The four types of organisational knowledge used in this study are defined as follows: Human knowledge is acquired and accumulated in employees who increase their professional qualifications and productivity that add value to the firm. Personal contacts, relations, and other individual qualities – e.g., expertise, reputation, work experience, sound judgment, and intelligence – are some examples of human knowledge. This knowledge type needs to be nurtured internally or can be acquired or transferred of experts from outside.
The technological knowledge is required to access and use these IT systems that require specific knowledge on business methods and processes embedded in the ERP system. Technological knowledge also includes the know-how of the underlying IT infrastructure such as databases, developing tools, hardware, and networks. This knowledge can be acquired from external sources by licensing from IT vendors, training, and knowledge transfer from experts, etc.
The relational knowledge consists of the contribution made by external intangible resources that contribute to the design of new business processes and implementation of IT solutions. Relational knowledge – located in the relationship with external parties such as consultancy firms, IT vendors, experts, etc. – includes industrial best practices, industry knowledge, information obtained from process benchmarking, project management tools and templates, input from IT consultants and experts, etc. It can be sourced into the firm via hiring consultancy firm, making technical support agreements with suppliers, subcontracting work to experts, etc.
Organisational knowledge creates knowledge advantages to the firm and its IT projects by stabilising and integrating human knowledge, technological knowledge, and relational knowledge. Organisational knowledge includes norms and guidelines, project and organisational routines, and implementation methodology and tools. This knowledge is accumulated by project managers and determined by their experience with projects in similar applications.
The organisational knowledge in this study is called management knowledge to better capture its nature and purpose, and to differentiate it from the other three knowledge types, which are also part of the organisation’s knowledge.
2.3 IT-enabled business process improvement
The effective integration of business processes and their expansion into new areas have become decisive factors in maintaining a company’s competitiveness. Davenport and Short (1990) found in their study of new industrial engineering concepts that IT and business process redesign are two vital tools to create a new type of industrial engineering. IT-enabled reengineering is an important approach used to achieve dramatic improvement in business process performance.
The integration of full process chains can be achieved by implementing of process-based software (Curran and Keller, 1998). ERP system is a popular process-based software used in organisations to improve business processes. Such systems can be considered as an IT infrastructure that can facilitate the flow of information among all business processes (Martin, 1998). The full ERP package can integrate all key business activities and improve relationships at all levels to achieve a competitive advantage (Davenport, 2000). A number of ERP systems are available in the market; e.g., top five ERP systems ranked by Gartner consisted of SAP, Oracle, Peoplesoft, Sage, and Microsoft (Gartner Dataquest Predicts, 2002).
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The underlying knowledge of ERP can be grouped by its components into business knowledge and infrastructure knowledge. The business knowledge refers to business processes and method embedded in the ERP system, while the infrastructure knowledge is related with technical components such as database, developing tools, and system architecture.
2.4 Research proposition
Studies in the USA suggest that IT capital investments have made a substantial productivity improvement at the firm level (Brynjolfsson and Hitt, 1995; 1996; 1998; Lichtenberg, 1996), but economists disagreed on whether the increase in productivity would be sustainable (The Economist, 2000). A number of studies tried to find the reasons for the inability of the firms to take full advantage of the implemented information technologies. These studies focused on the effects of organisational structure, strategies and processes to use IT effectively (Brynjolfsson and Mendelson, 1993; Grover et al., 1998; Henderson and Venkatraman, 1994; Lucas and Baroudi, 1994; Morton, 1991).
Literature seems to consider IT as an enabling tool for corporate knowledge management, but less attention is given to various types of knowledge as key resource for successful corporate IT implementation to gain competitive advantage. Two main streams of knowledge classification are commonly used in literature for measuring organisational knowledge and developing a knowledge management strategy. This study proposes to add knowledge resource as a new dimension into IT projects that aim to improve business processes. Applying the classification of four knowledge resource types to managing IT-enabled business process improvement leads to the following proposition. “Firms that have sufficient in-house knowledge resources will be more successful in IT-enabled business process improvement than firms that have deficient or missing knowledge resources”.
3 Research methodology
Multiple case study approach (Yin, 1994) is used in this research for two reasons. First, this study is part of a PhD research focusing on cause-effect type of research questions that had dealt with too many variables to find statistically enough data points for a survey method. Second, the KM concept proposed in the literature is still abstract that the boundaries between phenomenon and context are not clearly evident.
3.1 Selection of company cases and respondents
The unit of analysis in this research is the project level. The valid project cases need to cover competitive companies that are operating in different industries in Thailand (both local and MNC). Both successful and failure project cases chosen had been completed within the past five years, and had adopted leading international ERP systems widely recognised by corporate customers in Thailand. Only two of the top five global ERP vendors have operated in Thailand longer than five years, i.e., SAP and Oracle. Unfortunately, the company cases could not be selected randomly. First, customer data is sensitive information of ERP companies. Only ten big companies out of hundreds
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potential companies were obtained from managers of ERP vendors based on personal relationship with the authors. Second, the failure cases naturally were not willing to extensively share information because such sensitive data could have a negative impact on the companies’ image as well as the persons who provided the information.
Finally, a total of five project cases was selected based on accessibility and validity as explained above. F1, a public company in food processing company, was founded 15 years ago. The ERP project objective was to improve data accuracy of the old stand-alone system for improving management control and corporate image by implementing ERP system, balance scorecard, and data warehouse. The project involved several business processes such as sales, accounting, procurement, production, warehouse, and strategic management.
F2 was the first international dairy product manufacturer in Thailand. It encountered limitations of the old system to support new business models such as internet sales. Thus, it had to implement a new ERP system to improve two business processes including sales order processing and accounting. Five respondents were interviewed, e.g., former project manager, IT manager, key user, customer service manager, and an external consultant.
Among four wholesale companies in Thailand, T1 is the oldest, founded over 20 years ago. T1 replaced the old system because the upgraded version of its core trading system was not compatible with the old accounting system.
M1 is the oldest agricultural machinery producer in Thailand founded 25 years ago. The parent company of M1 had stopped providing IT services to all subsidiaries. Thus, M1 had to build its own IT system. The project involved the entire set of business processes including sales and marketing, IT, warehouse, quality control, accounting, production, and procurement.
The last case, P1 was the first petrochemical plant in Thailand founded almost 20 years ago. P1 was struggling with fragmented systems to maintain accurate information, and the main objective was to upgrade IT infrastructure to integrate all business processes and isolated systems together. The business processes involved were similar to M1.
3.2 Data collection
Required data were obtained during July–August 2003 through semi-structured interviews with four types of respondents in each of the five companies, i.e., senior IT managers, project managers, key users, and implementation consultants as well as through documents such as company profile, project plan, business process flow diagrams, and implementation proposals. Valid respondents should have worked with the companies before the start of projects (all selected respondents had over five years of experience). All respondents were selected based on personal relationship with the authors or referred by prior respondents. All company cases and informants were ensured that their anonymity was protected. However, new system users in F1 were not collaborative since they were not authorised by managers to give any information to outsiders. Only three persons were interviewed, i.e., a senior manager responsible for the project, a consultant who was project manager, and a manager of a subsidiary company who supported the new system operations. Consultants of P1 were not willing to give customer-related information, therefore additional respondents from project team and system users were added to minimise information bias.
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Following interviewing instrument design of Wengraf (2001), about 30 open-ended questions were developed for the interviews that could be grouped into three sets, namely general information of project case (company profile, motives for the implementation, project objectives, etc.), in-house availability of key knowledge resources (legacy system, project preparation, selection of project members and consultants, problems in the projects, etc.), and the project outcomes (process changes, project achievements, etc.).
All respondents were interviewed individually in Thai language. Interviews were recorded to ensure accurate retrieval, and follow-up interviews were carried out over the phone to obtain missing data. Senior experienced managed and users of the companies were requested to verify the content of draft case study reports presented to them before further analysis.
3.3 Interpretation of project case data
Interpretation of the key variables needed experts’ assessment to minimise any potential interpretation bias caused by limited knowledge of the authors. The assessment of each variable required expertise in different areas for proper interpretation. Therefore, two expert panels were organised to assess availability of key knowledge and achievement level of each project case. At least three experts were required in each panel. Without disclosing company names, all experts were provided with the same case information related to the variables of interested and review and assessment instructions. A Delphi technique was used to reach consensus on expert assessments of key variables.
The expert Panel 1 was added to assess the achievement level of each project. The three experts were composed of a senior director who had over 20 years experience in managing the IT function and had been involved in implementing several ERP systems, an IT professional with over ten years of experience in IT consulting companies and as ERP project manager in a private company. The third expert was a consultant with almost 20 years of work experience in international consulting firms and involvement in many IT projects.
ERP implementation achievement was categorised into three levels: successful (Class A), partial success (Class B), and failure (Class C) (see definition of each class in Table 2). The expert Panel 1 suggested modification of an original four-level ABCD classification1 proposed by Wight (2000) due to its two limitations. First, most companies have seldom attempted to measure the improvements and benefits gained from newly implemented IT systems. All the managers only gave qualitative feedback on the new system’s performance in their functional area and especially described how the new system helped them run business processes better. Second, the expert panel found it difficult to distinguish between Class B and C performance based on the available information.
Expert Panel 2 had assessed the level of key knowledge resources and problems of KM practices in each IT project. This panel consisted of four experts, which included the second expert of the Panel 1. The additional three experts were two IT consultants in ERP vendor firms who had over ten years of experience with implementing and managing several IT projects, and one IT manager in a construction material company who used to work with ERP vendors as implementation consultant and had over ten years of experience.
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Table 2 Definition of achievement level of IT implementation project
Achievement class Definitions
A (successful) Entire system has been effectively used company-wide at all levels to run the business; all project objectives have been achieved. Company could not operate without the system.
B (partial success) The system was only partially used by some groups of users or some processes were not used effectively to run business. Project objectives were not fully met, but the company still depends on the system to run business.
C (failure) Information inaccurate and poorly understood by users; system provides little help in running the business.
4 Findings and analysis
4.1 Achievement level of the project cases
The achievement levels of each project case are presented in Table 3. Given that firms F1 and F2 could use only accounting process of the ERP system and were planning to re-implement it, informants and experts considered both cases as unsuccessful. On the other side, firms T1 and P1 were assessed as successful cases since they have fully integrated all ERP modules to support their business operations. Firm M1 could achieve only a partial success level because the production module was not used to manage and control production processes but only to capture material and accounting transactions required by other related business processes.
Table 3 Status of key knowledge in the project cases
Level of key knowledge resources Cases Human Technology Management Relational
ABC class
P1 Sufficient Sufficient Sufficient Sufficient A
T1 Sufficient Sufficient Sufficient Sufficient A
M1 Deficient Deficient Sufficient Sufficient B
F1 Deficient Deficient Deficient Deficient C
F2 Deficient Deficient Deficient Deficient C
4.2 Key knowledge for implementing IT solutions
Availability of knowledge resource at the beginning of each project, according to the experts’ assessment of project case data, can be ranked along three levels as missing, deficient, and sufficient. The missing category means that a key knowledge resource is not available in-house. The label deficient is used to state that some key knowledge resources are available in-house but not sufficient to ensure success. The missing and deficient in-house knowledge needs to be complemented with knowledge from external sources to avoid hampering the project. Sufficient knowledge refers to key knowledge resources that are sufficiently available in-house for designing new business processes and implementing the related IT solutions. Table 3 shows the knowledge availability of each case that reflects a consensus among all expert panellists. Some examples for each type of key knowledge shown in Table 3 are provided below.
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4.2.1 Human knowledge
Diversity of knowledge perspectives and knowledge depth among project team members determines the quality of human knowledge available for the project. Five levels of human knowledge depth were found in the project case data: know-what, know-how, know-why, know-who, and care-why. The design of new business processes requires staff from related business functions who have accumulated substantial knowledge of their business unit functions to work in the project, that is especially related to cognitive knowledge (know-what), system understanding (know-why) and advanced skills (know-how) of their unit’s business processes.
All project cases applied a set of minimum required criteria to select project team members. The project staff needed to know business process flows of their units as well as supportive systems used in these business processes (know-what and know-why) well enough, and had to have several years of working experience with these processes and systems (know-how). These criteria were used to distinguish cases that had sufficient levels of in-house human knowledge at the beginning of projects. Additional criteria were used in the case T1 where management tried to make the project successful by assigning more experienced users to the project. T1 assigned business process owners to design new business processes for their units and implement the supporting IT systems. These managers had knowledge of process constraints and alternatives (care-why), and understand well the interface of specific business processes and knowledge workers (know-who), and had authorisation to make decisions about the new business process designs in addition to the required know-what, know-why, and know-how knowledge. Company F1 had used obsolete PC-based systems for long time. Most employees did not understand the concept of IT system integration. The knowledge of functional managers assigned into project was still insufficient for the project.
Diversity of project team knowledge is another determinant of human knowledge availability for the project. The knowledge perspectives from all related business areas have to be involved in the design of new business processes. Interview data reveal that at least the know-what, know-how, and know-how of related business processes are required for a successful redesign. More knowledge depth and diversity among project team members clearly benefits the new business process design. For instance, F2 had involved managers from all related units, but the human knowledge was still not sufficient because the logistics manager delegated his tasks to new junior staff, and the accounting manager was replaced by new staff who did not fully understand the company’s business processes. Company F2 did not have enough IT staff to learn technical knowledge of the new system during the project since the internal IT team was underdeveloped (only five staff including manager). Similarly, F1 and M1 also had small team of IT staff and all of them were not updated with new technologies being implemented.
Project cases with sufficient in-house human knowledge were found in companies T1 and P1. For example, P1 dedicated 35 full time staff members from all related business functions to the project. Additional staff from the internal audit unit, one experienced manager from the project management division, and ten IT staff were assigned to work full-time on the project. Both companies, P1 and T1, also sent their project teams to attend the formal training by the software vendor before the project started. All experts
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rated human knowledge of these two cases as sufficient. Some experts said, “P1 is an ideal project that had perfect project teams and resources. They had more than enough resources normally required for such a system implementation”.
4.2.2 Technological knowledge
Technological knowledge, in the study of IT-enabled business process improvement, refers to IT infrastructure and business methods and processes embedded in ERP. The IT infrastructure knowledge is related to access and use of IT solutions and its underlying technologies such as databases, networks, developing tools, and applications. The business method knowledge refers to functional knowledge of new business model and related processes that may be either provided with the IT solutions or designed and configured into the system later. Three levels similar to human knowledge were used to gauge the availability of technology related knowledge resources. Table 3 shows levels of technological knowledge resources available in each project case.
For example, T1 did a good preparation of providing technological knowledge before the project started. Three professional ERP system experts joined the IT management team. They were experts on different components of the software version being implemented at that time. On the other hand, M1 had an underdeveloped internal IT unit and staff since the IT functions used to be handled by the corporate IT team. The ERP system implementation was a completely new task for the employees and newly established IT teams at this subsidiary firm.
4.2.3 Management knowledge
Management knowledge contributes order, stability, and quality to the design of new business processes. It includes implementation strategy and methodology, project norms and practices, and guidelines to control the activities of creating new business processes in line with implementing a new IT system. Management knowledge is determined by the experience and skills of project managers.
Company F2 had implemented the old software about 13 years ago, and most experienced employees had already left the company. However, the new ERP system technology being targeted was much more complicated than the legacy systems. In addition, the in-house project manager had limited experience in managing IT software development projects. This evidence for the level of in-house management knowledge at F2 was considered by the experts as not sufficient to ensure ERP implementation success.
On the positive side, P1 had prior experience using and implementing several enterprise systems. Some members of the project team and steering committee had work experience in implementing other IT solutions. Senior managers from the project management division were also assigned to support the project. They had experience in managing large complex projects like the construction of a petrochemical plant.
4.2.4 Relational knowledge
Relational knowledge, in addition to the internal knowledge of an organisation, is a key ingredient for IT-based process improvement projects as it adds more knowledge perspectives to project teams, fills in internal missing or deficient knowledge, and contributes best practices and industry knowledge as a source of new process design ideas. Relational knowledge for IT projects is sourced typically into the project by means
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of hiring external consultants, learning from technology vendors, partnering with a consultancy firm, etc. Useful relational knowledge contributes complementary elements to fill in deficient in-house key knowledge.
The external consultants subcontracted in cases of F1 and F2 had little experience of implementing similar IT solutions in this type of industries. Some consultants just received a few hours of training on the IT solutions shortly before project started. Both cases changed project managers several times since they were not senior and knowledgeable enough to manage the projects. The consequence was deficient relational knowledge in both cases. P1, unlike F1 and F2, carefully assessed knowledge and skills of project manager and consultants to select a consultancy firm. The consultants and project manager had experience of implementing similar solutions in other companies in the same industry, and the consultants were certified experts for such IT solutions and competent to implement them.
4.3 Key knowledge for successful implementation of IT solutions
The finding and expert analysis results show that the successful cases (T1 and P1) had all four types of key knowledge resources available for the project implementation (see Table 3). On the other hand, both failure cases (F1 and F2) showed deficiencies in all four knowledge resources. This empirical evidence confirms the importance of all four knowledge types to ensure successful IT-enabled business process improvement.
However, deficiencies of some knowledge types did not always end up with project failure. For example, M1 partially succeeded, despite the lack of in-house knowledge resources. They overcame the deficient in-house technology and management knowledge through collaboration with external sources (relational knowledge) such as the ERP vendor that contributed skilled consultants, a business process design expert, and an experienced project manager to the project.
5 Conclusion
This study explored the contribution of key knowledge resources to successful business process improvements that are enabled through IT solutions such as the ERP. Our assessment of both success and failure project cases revealed that implementation of stage-specific knowledge inputs is required for a successful ERP deployment. We found empirical evidence of four knowledge domains residing in the case firms, i.e., human, technological, management, and relational knowledge. Findings from the five cases confirm that all four knowledge domains played an essential role in IT-enabled business process improvement. We demonstrated that firms with sufficient knowledge resources available during ERP project planning and acquiring stages created distinctive new knowledge necessary for IT-enabled business improvement. This was evident in three of our five company cases, while the absence or deficiency of those knowledge resources caused the other two firms to fail.
The findings provide several business implications for managing IT-enabled business process improvement projects. For example, companies should ensure the availability of all four types of key knowledge before implementation. The missing or deficient knowledge resources could be acquired to the project by means of training project teams, hiring consultants, recruiting experts, subcontracting, etc. The companies could reduce
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the amount of in-house knowledge required upfront for the implementation by adopting suitable software and implementing in multiple phases to match with available in-house knowledge and allow time for learning. These implications are not limited to ERP only but rather beneficial to implementation of other process-based software such as CRM, e-commerce, etc.
The RBV theory, in addition to having access to key knowledge, also suggests that companies enjoy a competitive advantage if they know how to manage this knowledge well. However, managing knowledge is possible only if the processes leading to its development and usage can be established and improved. The concept of ‘Organisational routine’ (Nelson and Winter, 1982) explains a firm’s capabilities, and Coombs et al. (Coombs and Hull, 1998) suggested to study routines or processes that are important in shaping the knowledge base of the firm and make it visible and available enterprise-wide. These routines are called knowledge management practices. The underlying components of knowledge management practices consist of knowledge process, knowledge domain, targeted units, and specific format of the practices. Future research should explore the KM practices specific to each ERP implementation process to investigate how to manage the key knowledge resources that ensure the successful implementation of new corporate IT system innovation such as the ERP.
6 Limitation of the study
This study, based on case study approach, may have some limitations that could result in certain information bias. First, the cases could not be selected randomly as mentioned in the methodology section. A second limitation may be the subjectivity of qualitative measures based on data from open-ended interview questions. This was mitigated by interviewing four types of respondents and interpreting the data by two expert panels. Thus, the experts were not informed about the name and other detail of companies to avoid any further bias. The reasons of assessment, in case of difference in expert opinions, were distributed to other experts for review and discussion until a consensus could be reached. Further research, given these potential limitations, should expand the scope of this study by developing quantitative measures to conduct a survey of a larger sample.
Acknowledgement
The authors would like to express their appreciation to Dr. Sutham Cheurprakobkit Department of Sociology, Geography and Anthropology, Kennesaw State University for his valuable comments to improve this manuscript.
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Note
1 The original ABCD classification measures success of ERP implementation on four levels: as presented in the following table.
Adoption class Definitions
Class A Effectively used company-wide; generating significant improvements in customer service, productivity, and costs.
Class B Supported by top management; used by middle management to achieve measurable quality improvements.
Class C Operated primarily as better methods for ordering materials; contributing to better inventory management.
Class D Information inaccurate and poorly understood by users; providing little help in running the business.
Note: The original ABCD Classification of ERP adoption of (Wight, 2000)
Source: Wallace and Kremzar (2001)