An empirical investigation of knowledge management competence for enterprise resource planning systems success: insights from Jordan Mahmoud Mohammad Migdadia* and Mohammed Khair Saleem Abu Zaidb

An empirical investigation of knowledge management competence for enterprise resource planning systems success: insights from Jordan Mahmoud Mohammad Migdadia* and Mohammed Khair Saleem Abu Zaidb

aDepartment of Management Information Systems, Princess Sumaya University for Technology, Amman, Jordan; bFaculty of Planning and Management Al-Balqa’, Management Sciences Department, Al-Balqa’ Applied University, Al-Salt, Jordan

(Received 9 February 2016; accepted 28 February 2016)

The primary objective of the study reported herein is to empirically test the implicit, positive relationships between ERP-related Knowledge Management Competence (KM-competence; knowledge creation, knowledge retention, knowledge transfer, and knowledge application) dimensions and the extended Enterprise Resource Planning System Success construct (ERP system success; individual impact, workgroup impact, organisational impact, information quality, system quality, and vender/consultant quality). Data were collected from 173 of business and IT managers in 455 organisations in Jordan. Statistical techniques employed included confirmatory factor analysis to examine validity of the measurement model, and structural equation modelling using AMOS 16.0 is also utilised to test the hypotheses. The results of analysis show there is a positive significant impact of ERP knowledge creation on ERP success. Also, ERP knowledge retention positively and significantly affects ERP system success. Moreover, ERP knowledge transfer positively and significantly influence ERP system success. Furthermore, ERP knowledge application has positive effect on ERP system success. The results also indicate that ERP success construct is robust since all six observed variables are strongly loaded to the latent variable. Research limitations as well as implications for practice and research are discussed.

Keywords: knowledge management competence; ERP systems success; individual impact; workgroup impact; information quality; system quality

1. Introduction

In today’s business environment, the success of organisations can be linked to how well they manage their knowledge (Desouza and Evaristo 2003); knowledge to represent know-how, expertise, skills, ideas, intuitions and insights. Organisations compete on their know-how and hence have to use knowledge to their advantage, even more than traditional resources.

New information and communication technologies, expeditious data processing models, configurable platforms, net- working and the internet have facilitated enterprises to gain access to external sources of knowledge and have provided them with the opportunity to foster intra/inter-organisational integration with the aim at achieving higher efficiency, effectiveness, better quality of services and minimisation of costs. In this context, enterprise resource planning (ERP) systems have been shown to be powerful ingredients in the success of enterprises (Alsene 2007).

ERP systems are software packages that can integrate organisation’s processes and functions (Wu and Liou 2011; Olson, Chae, and Sheu 2012). ERP packages are an important resource in the manufacturing and production processes of several organisations around the world (Gattiker 2007; Olson, Chae, and Sheu 2012; Powell, Riezebos, and Strandha- gen 2013). An ERP system comprises a central database that stores data across various business functions and activities in an organisation (Supramaniam and Kuppusamy 2011). An organisation typically expects the system to not only address problems associated with business process integration, but also enable information to flow seamlessly across functions and streamline functional processes (Bharathi and Parikh 2012). Hence, ERP system is a strategic IT tool that helps a company to gain competitive advantage by integrating business processes and optimising the resources available.

A significant amount of ERP research has focused on identifying critical success factors (CSFs) associated with ERP system implementation (Grabski, Leech, and Schmidt 2011). However, relatively little research has appeared that focuses on the effort with respect to ERP in the post-implementation period (Muscatello and Parente 2008) and during continuing usage (Grabski, Leech, and Schmidt 2011). After ERP implementation, ERP system usage is a necessity for

*Corresponding author. Email: m.migdadi@psut.edu.jo

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International Journal of Production Research, 2016 Vol. 54, No. 18, 5480–5498, http://dx.doi.org/10.1080/00207543.2016.1161254

daily operations in many organisations. If users could operate the ERP system smoothly, the organisation would get the anticipated benefits.

Existing KM frameworks include the work of Chan and Rosemann (2001) analysed the importance of knowledge types in the ERP life cycle. Similarly, in the study by Esteves et al. (2003), the knowledge types are allocated in the life cycle of a system. However, these frameworks do not present the knowledge dynamics in the project, i.e. the knowledge flows and activities. Research available in the literature also includes specific aspects of knowledge in projects, such as the role of knowledge in the success of projects (Sedara and Gable 2010). It also includes the project manager’s role in integrating and managing knowledge (Kasvi, Vartiainen, and Hailikari 2003), knowledge risks and best practices to avoid them (Reich 2007), the impact of knowledge skills on project success/all the possible aspects of knowledge and KM in projects is a valuable research direction (Reich 2007). Additionally, (Sedara and Gable 2010) indicated that there have been reports of organisations achieving high levels of success with ERP by focusing on effective ERP-related knowledge management in organisations. Furthermore, the researchers indicated that even though these studies have evi- denced the relationship, the general lack of quantitative validation has been lamented and suggested other limitations.

Based on the above discussion and related literature, even though knowledge has been attributed as a key driver of ERP success, there has been very little work conducted to date that assesses the relationship of knowledge management and ERP system life cycle. Moreover, a review of recent studies of Knowledge Management in support of Enterprise Systems, suggests other limitations of past research in the area (Candra 2012). Therefore, to fill this gap, this study empirically examine the impact of each variable of the knowledge management competence construct, that is; (knowl- edge transfer, knowledge creation, knowledge retention and knowledge application) on the extended Ifinedo and Nahar’s (2009) ERP system success measurement model; (individual impact, workgroup impact, organisational impact, informa- tion quality, system quality, vendor/consultant quality and ERP impact) and then, investigate the impact of knowledge management competence as a formative index construct on the ERP system success construct.

2. Theoretical framework

The researchers in this section synthesise the salient phases of knowledge management from previous literature. These phases ultimately form dimensions of the research knowledge management competence construct. Researchers often conceive knowledge management as a systematic process consisting of multiple phases. For instance, Gold, Malhotra, and Segars (2001) described KM as a knowledge process capability that consists of knowledge acquisition, conversion, application and protection. Pentland (1995) defined KM process as an ongoing set of activities embedded in the social and physical structure of the organisation with knowledge as their final product. Also, O’Dell and Grayson (1998) defined managing knowledge as a systematic approach to finding, understanding, and using knowledge to create value. Finally, Bennett and Gabriel (1999) defined KM as a process that involves knowledge capture, storage (i.e. documenta- tion), dissemination and use.

According to Sedara and Gable (2010), some consensus is apparent with four common phases spanning the Knowledge Management Life cycle: (1) acquisition/creation/integration, (2) retention/storage/capture, (3) share/transfer/ disseminate and (4) application/utilisation/use. More succinctly, Sedara and Gable (2010) suggested the four-phases: Creation→Retention→ Transfer→Application, where these four-phases represent the full life cycle of Knowledge Management activities have been adapted by the researchers in this study as shown in Figure 1. In the next Section (2.1), the researchers will present a brief description of each of the four-phases.

RP system success is similar to information systems (IS) success or effectiveness (Gable, Sedara, and Chan 2003; Ifinedo 2006a) and is different from ERP implementation success. ERP system success refers to the utilisation of such systems to enhance organisational goals (Gable, Sedara, and Chan 2003; Ifinedo 2006a, 2006b).

Gable, Sedara, and Chan (2003) developed an ERP system success measurement model that redefined the dimen- sions in Delone and McLean’s (1992) IS success model. They eliminated the use and user satisfaction dimensions (through multi-stage and statistical analysis). The retained ERP system success dimensions in Gable, Sedara, and Chan (2003) model are system quality, information quality, individual impact and organisational impact. Ifinedo (2006a, 2006b) and Ifinedo and Nahar (2006) proposed and extended ERP system success measurement model (through litera- ture reviews and case studies) to include workgroup impact and vender/consultant quality. The researchers argued that any ERP system success measurement model should include a dimension related to workgroup impact because ERP sys- tems are often adopted to overcome the shortcomings of other IT systems, including material resource planning systems that ended up isolating the enterprise into islands of information. Moreover, the underlying logic of ERP is to enhance efficient cross-functional operations. Furthermore, Yu (2005, 117) argued that the engagement of poor quality ERP systems providers ‘can become a negative influence or even a curse which [drags] the entire company into a spiral of ineffectiveness’. Therefore, based on intensive and comprehensive literature, knowledge management competence (four

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dimensions) presented in Sedara and Gable (2010) and the extended ERP system success measurement model of Ifinedo and Nahar’s (2009) are adopted by the researchers in this study as shown in Figure 1. A brief discussion of the sex ERP system success measure will follow (Section 2.2).

2.1 Knowledge management competence

KM aids in planning, organising, motivating and controlling of people, processes and systems in an organisation to ensure that its knowledge-related assets are continuously improved and effectively employed (Rajesh, Pugazhendhi, and Ganesh 2011; Lara, Palacios-Marques, and Devece 2012). Given the unwieldy expression ‘ES-related Knowledge Management Competence’ further reference to this concept is simply ‘KM-competence’ were the ES nature of knowledge is implied (Sedara and Gable 2010). Furthermore, the researchers defined KM-competence as the effective management of knowledge of value for the on-going health and longevity of the Enterprise System. As previously mentioned, the research’s KM-competence construct encompass the four phases of KM (knowledge creation, knowledge transfer, knowledge retention and knowledge application). The following is a brief description of them.

2.1.1 Knowledge creation

Organisational knowledge creation is the capability of an organisation as a whole to create new knowledge, disseminate it throughout the organisation and embody it in products, services and systems (Nonaka and Takeuchi 1995). Organisa- tional knowledge can be created or acquired through various organisational learning processes (Walsh and Ungson 1991; Stein and Zwass 1995). Nonaka (1994) presented a theory of organisational knowledge creation that is initiated by individual learning, which then spreads across the organisation through various communication mechanisms. The the- ory builds on interactions between tacit and explicit knowledge. Tacit knowledge is highly personal and hard to for- malise, while explicit knowledge is expressed using formal representation and can be communicated easily. New ideas are formed through interactions between explicit and tacit knowledge in individual human minds (Nonaka and Takeuchi 1995). Nonaka (1994) described a model of organisational knowledge creation that draws on four patterns of interac- tions between tacit and explicit knowledge, namely – socialisation (from tacit to tacit), combination (from explicit to explicit), externalisation (from tacit to explicit) and internalisation (from explicit to tacit).

The process begins with a generation of new individual tacit knowledge through hands-on experience. Socialisation then follows, involving the construction of a ‘field of interaction’ whose members share experiences and perspectives. Dialogues between members allow theconceptualisation of the tacit knowledge and trigger externalisation. Next follows combination of the new knowledge with existing explicit knowledge and finally, the new concepts are articulated through experimentation and internalisation. Once this process is completed the new knowledge is evaluated – i.e. tested whether it is worthwhile for the organisation – and if proven useful, stored.

2.1.2 Knowledge retention

‘Retaining’ knowledge refers to keeping possession of knowledge, not losing, continuing to have, practicing or recognising knowledge (Martins and Meyer 2012).

ERP system Success

* Individual impact

* Workgroup impact

* Organizational impact

* Information quality

* System quality

* Vendor/Consultant quality

Knowledge Management

Competence

* knowledge Creation

* Knowledge Retention

* Knowledge Transfer

* Knowledge Application

Figure 1. Research model encompasses knowledge management competencies for ERP system success adapted and modified from Sedara and Gable (2010) and Ifinedo and Nahar (2009).

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Knowledge retention is generally defined as how knowledge from, for example, previous product development processes can be stored within the organisation. Hence, knowledge can be codified in order to transform tacit knowledge into explicit knowledge (Anderse’n 2012). Lichtenthaler and Lichtenthaler (2009, 1320) made a distinction between external and internal knowledge retention capacities. Transformative capacity (internal knowledge retention) is defined as how ‘firms maintain knowledge over time and reactivate it subsequently’, whereas connective capacity ‘refers to a firm’s ability to retain knowledge in inter-firm relationships’.

Organisational knowledge is often codified and stored in the various retainers of organisational memory. Walsh and Ungson (1991) analysed organisational memory and described five retainers of memory: individuals, who retain knowl- edge in their memory stores or in their belief structures, values or assumptions. While older experts leave organisations, taking their expertise, the effects of knowledge loss can be countered by new employees entering organisations, repre- senting an influx of knowledgeable personnel (Connell, Klein, and Powell 2003), bringing their own expertise, experi- ence and new ideas. Their knowledge can be shared, and new knowledge evolved within the organisation, when certain factors that enable knowledge sharing are present; culture that stores knowledge in language, shared framework, sym- bols and stories; transformations, procedures and rules which include embedded knowledge such as the logic behind them; structure and roles that represent the organisation’s perception of the environment and social expectations; and finally, the physical settings of the workplace represent knowledge about status hierarchy and behaviour perceptions. Organisational knowledge can also be stored in retainers external to the organisation, such as government agencies, market reports and others.

The researchers adapted Arif et al.’s (2009) which specified four levels that indicate the maturity of an organisation in knowledge retention. The four levels are listed as follows: Level-1: the knowledge is shared amongst the organisation employee; Level-2: the shared knowledge is documented (transferred from tacit to explicit; Level-3: the documented knowledge is stored; and Level-4: the stored knowledge is accessible, can be retrieved and used easily.

2.1.3 Knowledge transfer

Knowledge transfer is defined as the process through which one organisational unit is affected by the experience of another (Watson and Hewett 2006), as an event through which one entity learns from the experience of another (Rezania and Ouedraogo 2014). Organisations that are effective in transferring knowledge from one unit to another are reckoned to be more productive and profitable (Rezania and Ouedraogo 2014). Explicit knowledge can be transferred and shared between a knowledge owner and a learner through verbal explanation and/or documents (Fan and Ku 2010; Maruta 2014). If an organisational database system is used as a knowledge-base, explicit knowledge becomes accessible to mul- tiple learners in the organisation at anytime from anywhere. Genuinely tacit knowledge can only be transferred indirectly (Maruta 2014). The knowledge owner and the learner are closely linked as apprentice/trainer, trainee, tutoring or even through simple conversations and discussions.

Effective knowledge transfer raises the level of innovation, retains and leverages existing knowledge, accelerates product knowledge transfer, identifies and effectively deploys best practices, speeds up problem-solving, integrates and exploits new expertise and accelerates individual and organisational learning (Rao 2005).

2.1.4 Knowledge application

Usage of knowledge entails applying the knowledge already owned by an individual or organisation to the solution of business problems at all levels of the organisation – operational, tactical and strategic. Once knowledge is available for use, certain processes need to be in place in order to properly apply it for problem-solving. Application processes are ‘those oriented toward the actual use of knowledge’ (Gold, Malhotra, and Segars 2001, 191). Knowledge can be viewed as an object or a process (Zack 1999). When viewed as a process, it cannot be separated from its respective action, i.e. its application. However, when dealing with explicit knowledge on a level other than the individual, there is a gap between the point where new knowledge is acquired and when it is actually put to use. This is where application pro- cesses come into play in enabling effective use of such knowledge. While conversion processes organise knowledge for effective retrieval and sharing, it is through application processes that knowledge is actually retrieved and shared.

2.2 ERP system success measurement

ERP systems are gaining interest from both practitioners and researchers because these systems are essential to organisa- tional and individual user’s productivity (Grant, Hwang, and Tu 2013). Researchers emphasised the significance of post-implementation success of ERP system primarily because the potential business value of ERP system can’t be fully

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realised until they are extensively assimilated in business processes and the effective application of ERP system in sup- port of organisational business processes and value-chain activities is more relevant to benefit realisation (Shao, Feng, and Liu 2012; Moalagh and Ravasan 2013). Moreover, IS usage has been proposed in several studies as a measure of the success of an IS. IS usage construct provides a measure of post-implementation behaviours (Lippert and Forman 2005). Therefore, ERP system usage is an important measure for ERP system success after ERP implementation. ERP system usage includes automating and informating (Lorenzo 2001). Automating means using ERP systems to automate business processes, hence, they can be performed with more uniformity, continuity and control. ERP system usage in automating is a basic function of an ERP system and one of the first benefits experienced by an organisation. Informat- ing refers to using ERP systems to generate information about the processes by which an organisation performs its work. According to Lorenzo (2001), informating enables ERP systems to be used for solving problems and justifying decisions, for coordinating activities amongst different business areas and amongst superiors and subordinates, and for servicing both internal and external customers.

Success measurement models used for other typical IT systems’ evaluation may not be adequate for ERP systems (Yu 2005; Ifinedo 2006b). According to Yu (2005, 117) ‘the system assessment after ERP implementation is not an end’; these researchers also argued that such an exercise should focus on relevant issues beyond those encountered dur- ing implementation. Given that ERP systems are a different class of IT systems, it is therefore vitally important for a specialised success measurement framework or model to be used when evaluating or measuring the success of such sys- tems (Ifinedo 2007). Hence, the extended ERP success measurement model of Ifinedo and Nahar’s (2009) is adapted by the researchers in this study as shown in Figure 1. A brief description of the dimensions of this construct is to follow.

2.2.1 System quality

Systems quality is a multifaceted construct designed to capture how the system performs from a technical and design perspective. (Felix 2010; Ifinedo and Olsen 2015) identified a comprehensive list of measures including data currency, response time, turned around time, data accuracy, reliability, completeness, ease of use and system flexibility. Gable, Sedara, and Chan (2003) identified another list which encompasses system reliability, user-interface consistency, ease of use, documentation quality, maintenance ability of the programme code, and ease of learning, quality of the system functionality and sophistication and integration of the system.

System quality as a measure of ERP system product performance is found to generate positive operational outcomes by assisting organisations in problem solving, autonomy in job performance, management visibility and cross functional- ity (Wickramasinghe and Karunasekara 2012).

2.2.2 Information quality

Information quality captures the perceived goodness of the product of IS. The growth of data warehouses and the direct access to information from different sources by managers and information users have increased the need for, and aware- ness of high quality information in organisations (Lee et al. 2002).

Information quality refers to the changes in the availability of consistent and reliable information from the ERP sys- tem. The ERP system captures data at a single point and this data is then made available across the firm. Stringent data entry checks from streamlined operational areas and automated transactions ensure that data integrity is maintained thus ensuring high quality output from the ERP system (Mabert, Soni, and Venkatramanan 2003b).

The quality of information produced by an ERP system is critical to its use (Chien and Tsaur 2007). It influences user acceptance, facilitates human interaction with the system and allows improved decision-making and productivity and increases user satisfaction (Nelson, Todd, and Wixom 2005).Information quality consists of timeliness, relevance or importance of the information worked up.

2.2.3 Individual impact

Individual impact is generally concerned with how the implemented system has influenced the performance of an individual. Firms are recognising that individual user productivity with ISs is one of the most important determinants for firm’s organisational productivity (Ruivo et al. 2013). Moreover, Kositanurit, Ngwenyama, and Osei-Bryson (2006) concluded that ERP usage is a major factor affecting work at the individual level and user performance is a direct result of system usage. DeLone and McLean (2003) used the term individual impact to explain the effect of information on the recipient by giving the recipient a better understanding of the decision context, improving his/her decision-making productivity, producing a change in recipient’s decision behaviour or changing the decision-maker’s perception of

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importance/usefulness of IS. Hence, individual impact could be an indication that an IS has improved the user’s decision-making productivity or effectiveness. This might be, for example, learning to transact or process with the system, interpret information accurately, understand information and work-related activities in this area better, make more effective decisions and generally be more effective (Felix 2010).

2.2.4 Workgroup impact

Workgroup impact means the impact of the system on the sub-units and/or functional departments of an organisation. Klein, Rai, and Straub (2007, 621) commented that ‘the ISs success (DeLone and McLean 1992; DeLone and McLean 2003) formulate the presence of both individual and organisational performance with potential intermediate levels at dif- ferent points in between (e.g. the business unit)’. Luo et al. (2015) distinguished between two types of IS usage and group performance. The first IS usage is exploitation which takes advantage of existing knowledge and resources, and facilitates the utilisation of both. Group exploitative usage means the group’s compliance to and familiarity with a set of predefined rules and procedures of IS usage, hence facilitates the integration of IS usage and process (Schwarz 2003). Moreover, exploitative usage enhances knowledge absorption (Cohen and Levinthal 1990) and promotes in-depth routinised work processes. Therefore, it provides an efficiency advantages in daily work (Becker 2010).

The second IS usage is explorative which allows group members to develop innovative solutions for tricky problems. Group exploration usage implies a group’s successful experimentation and application of new IS features to improve task performance or organisational processes (Jasperson, Carter, and Zmud 2005). Moreover, it helps a group leverage the potential value of IS to a higher level (Jasperson, Carter, and Zmud 2005), enhance group inventive capability, and thus booster effectiveness.

2.2.5 Organisational impact

Organisational impact measures the impact of the system on the organisation. It could be assessed by looking at the per- formance (effectiveness and efficiency) and the effect of that the applications have within the organisation, e.g. organisa- tional costs or staff requirements. Consistent with this line of thinking, Ifinedo et al. (2010) indicated that an ERP package is considered successful at the post-implementation phase, if it enhances potential benefits through organisa- tional cost reductions, higher operational productivity, increased customer satisfaction levels and so forth.

Although ERP systems are essentially transaction-focused, those firms that use ERP analytics capabilities can easily and quickly use data for managerial decision-making and realise an advantage in their pursuit of sustainable perfor- mance through unique business insight information (Chiang 2009). Firms leveraged this information output to affect efficiency improvements in functional areas such as inventory management, procurement and order management (Mabert, Soni, and Venkataramanan 2000).

2.2.6 Vendor/consultant quality

Vendor/consultant quality the need for vendor’s support in ERP implementation is stronger than in another IS project because ERP implementation project requires a wide range of skills and technical implementation knowledge (Daven- port 2000). ERP systems, a lifelong commitment for many companies, require continual investment in new modules and upgrades to add functionality, achieve better fits between business and system and realise their strategic value. ERP implementations are not often repeated by organisations. Little internal expertise exists in the organisations for a first time implementation and there is a little incentive to develop the internal competencies for a system that is not repeti- tively installed (Wang and Chen 2006). Therefore, organisations implementing ERP should supplement the skill sets of their internal teams with implementation resources from a software vendor or consulting firms that offer the requisite skills and knowledge (Lapiedra, Alegre, and Chiva 2011).

For instance, consultants provide technical and business expertise, reduce the learning burden of clients, configure appropriate ERP systems and train users to fully exploit the technology (Staehr 2010). Thus, it is crucial that consultants be experts in the ERP and associated processes (Ko, Kirsch, and King 2005).

3. Knowledge management competence and ERP success

There have been reports of organisations achieving high levels of success with ERP by focusing on effective ERP-related knowledge in organisations (Candra 2012). In a typical ERP project, members of an organisation exchange knowledge with several business consultants and IT specialists to study and redesign processes in the organisation

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(Rezania For instance, consultants provide technical and business expertise, reduce the learning burden of clients, configure appropriate ERP systems and train users to fully exploit the technology (Staehr 2010). Thus, it is crucial that consultants be experts in the ERP and associated processes (Ko, Kirsch, and King 2005; Rezania and Ouedraogo 2014).

Moreover, ERP projects often use a combination of complex technologies that pose a high knowledge burden and that are difficult for project members to grasp. In many cases, the ability of project members to learn and use technol- ogy-related knowledge as well as domain knowledge effectively is critical for successful ERP implementation. There- fore, ERP consultants; who have the knowledge required to operate the new system must provide relevant knowledge because the organisation lacks internal knowledge about ERP systems (Hung et al. 2012). For instance, the assistance provided by external consultants, effective communication and knowledge transfer concerning technical aspects of ERP systems are essential factors for successful ERP implementation (Maditinos, Chatzoudes, and Tsairidis 2011). Hence, internalising the knowledge embedded in ERP systems is a most critical strategy for achieving success in ERP projects (Ram, Corkindale, and Wu 2013). The following presents the relationships amongst the study’s model variables utilised in this study and their related hypotheses.

3.1 Knowledge creation vs. ERP system success

Demsetz (1991) and Grant (1996) suggested that knowledge acquisition and creation requires greater specialisation than is needed for knowledge utilisation; hence the production of knowledge requires a coordinated effort of individual specialists who possess many different types of knowledge. Typically, the necessary expertise is brought to bear by three key players contributing to ES implementation and ongoing support: (1) the client organisation, (2) the ES software vendor and (3) the implementation partner (Gable et al. 1997; Soh, Sia, and Tay-Yap 2000).

Having engaged a suitable implementation partner, the client organisation completes the implementation process, goes live with the ERP and moves into the post-implementation maintenance and upgrade cycle (Timbrell 2006). Fur- thermore, the researcher stated that in order to keep the ERP ‘live’ and relevant, the client organisation must either draw from their ERP capabilities transferred in during the implementation period or seek support (knowledge) externally. In order to increase client independence post-implementation, it is expected (with some variation) that the external parties (consultant and vendor) bring to the client organisation (mainly to its employees) new knowledge on the software and on ‘best-practice’ business processes (Davenport 1998b), while the client organisation shares organisational business process knowledge with the external parties. Timbrill (2006) described three different types of consulting practices: the expertise practice which employs considerable raw brain power to solve frontier (unique, ‘bleeding-edge’, new) prob- lems; the experience practice which has dealt with similar situations in previous assignments; and efficiency based firms which can demonstrate established procedures and systems to handle specific problems cost-effectively.

Organisations planning to support the ERP in-house (insourcing) face the issue of attracting or developing, then, retaining staff with the necessary knowledge, they will often aspire to post-implementation ERP knowledge self-sufficiency. Therefore, internal capabilities will be generated through an ongoing process of absorbing information, converting it into knowledge, and utilising knowledge to effectively undertake functional activities (Jansen et al. 2005a). Furthermore, Chesbrough (2003) suggested that there are many innovative solutions developed outside organisational boundaries and a new model of innovation needs to find ways to use this when it is not possible to own all the capabilities in-house.

External sourcing requires the ability to transfer outside knowledge into the organisation and to integrate this new knowledge with the existing knowledge base (Gopalakrishnan and Bierly 2001). Some firms have learned superior acquisition and integration capabilities by engaging in multiple acquisitions over time (Hayward 2002). Acquisitions are an increasingly important strategic tool for attaining the external technological know-how to supplement internal R&D efforts in a timely manner (Vanhaverbeke, Duysters, and Noorderhaven 2002).

Based on the above discussion, the researchers formulate the following hypothesis:

H1: There is a significant, positive relationship between knowledge creation and ERP system success.

3.2 Knowledge retention vs. ERP system success

To prevent detrimental effects on their business success and survival, organisations need to pay serious attention to the issue of knowledge loss and attrition by determining where the risks are and implementing a knowledge retention strat- egy (Martins and Meyer 2012).

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The human resource (HR), KM, and operations management literatures addressed different drivers of knowledge loss leading to various impacts on performance. Such drivers include ineffective organisational routines and memory (deHolan and Phillips 2004) and employee turnover (Martins and Meyer 2012). Chang and Gable (2001) suggested that given the current high staff turnover, it is imperative to avoid a situation in which ERP died a way along with the departure of certain personnel. As for the knowledge retention strategies, the potential for knowledge loss should be assessed through position risk factor, social network analysis and knowledge mapping and auditing tactics (Daghfous, Belkhodja, and Angell 2013). The KM literature suggested practices such as network building initiatives to increase knowledge sharing and retention, and to mitigate the impacts of knowledge loss. Knowledge retention involves ‘embed- ding knowledge in a repository so that it exhibits some persistence over time’ (Sedara and Gable 2010). The repository may be an individual or an IS.

People are the main repositories for organisational memory. Most of the knowledge and information that individuals use in their work is already in their memory, and no system is able to replace the human ability to retrieve relevant information and correctly apply it in a new context. However, people also have limitations in terms of the amount of information they can absorb and remember, and in making links between related pieces of information. ISs can over- come some of these limitations, and strengthen the overall organisational memory.

An ERP system may be viewed as part of the organisational memory, being a retention medium (IS) that embeds memory contents (Stijn and Wensley 2001). The researchers further stated that all four types of memory contents may be embedded in the ERP system. For example, information regarding financial resources or technological knowledge regarding logistic planning is represented in the ERP system, e.g. logistic planning modules. Paradigms also underpin the ERP system, though they may be implicit for the user organisation. For instance, paradigms concerning best prac- tices (Kumar and Van Hillegersberg 2000) and effectiveness are included, e.g. inventory schedule modules. Skills could be included as well, either elicited in the form of routines or decision models, or in the form of a skill database in the HR component of the ERP system, linking employees and skills. Hence, the researchers introduced the following hypothesis:

H2: There is a significant, positive relationship between knowledge retention and ERP system success.

3.3 Knowledge transfer vs. ERP system success

Knowledge transfer channels can be informal or formal, where unscheduled meetings, informal gatherings and coffee break conversations are examples of the informal transfer of knowledge (Newell, Huang, and Tansley 2007). Sedara and Gable (2010) argued that formal transfers through training programmes ensure wider distribution of knowledge and suit highly context-specific knowledge such as that of Enterprise Systems. User training is considered an important factor that reduces the resistance of change and positively affects the possibility of a successful ERP system implementation (Bradley 2008). There will be a higher possibility of successful implementation of ERP systems when systematic and efficient education programmes are provided for inside users. User education reduces inside resistance during the implementation process, promotes system understanding, and facilitates the implementation process. Hence, the knowl- edge transfer during training sessions produces improved human–system interaction and improved users’ confidence, thus -resulting in fewer problems in the accomplishment of routine and mission-critical business tasks (Hwang 2011). Further, training makes it possible to use the ERP systems after ERP is implemented.

A stream of knowledge sharing research in project management has recently focused on how to transfer and share knowledge within a project (Pee, Kankanhalli, and Kim 2010). To maximise the use of internal knowledge, the knowl- edge must be shared amongst individuals or teams (Chang et al. 2013). Effective knowledge sharing can result in accel- erating the relationship between the business clients and the IS consultants that are involved in the IS project (Park and Lee 2014). Moreover, the time spent on problem solving can be reduced significantly because the project participants’ benefit from the shared and accumulated knowledge.

Knowledge sharing after ERP implementation involves more than the connection of how to perform routine tasks; it enables employees to develop and exchange their underlying opinions, assumptions and the ways of working. For instance, employees could quickly update each other with tips on work when one figures out how to perform a useful task. Colleagues’ sharing feedback could produce improved ERP system usage (Nah and Delgado 2006). That is, through knowledge sharing, users can exchange what they know to create new knowledge jointly, enable correct opera- tions and, consequently, facilitate system usage. Knowledge sharing is important for organisational members to assimi- late ERP knowledge, thus to have a deeper understanding of system functionalities and capabilities (Shao, Feng, and

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Liu 2012). Hence, knowledge sharing is a key factor in successful ERP system usage (Chou et al. 2014). Therefore, the researchers present the following hypothesis:

H3: There is a significant, positive relationship between knowledge transfer and ERP system success.

3.4. Knowledge application vs. ERP system success

Lee et al. (2013) stated that knowledge application has a positive relationship with an organisation’s innovation level when knowledge is properly applied and used. The researchers also stated that knowledge application is essential for successful technological and organisational innovation. Sedara and Gable (2010) suggested that the source of competi- tive advantage resides not in knowledge itself, but in the application of the knowledge. Knowledge application is a strategic competitive asset for the modern businesses. It is useful for promoting organisational innovation and supporting new forms of cooperation by applying the relevant knowledge acquired. Madhoushi et al. (2011) also showed that the application of knowledge allowed organisation expertise and knowledge to be translated into products produced. With effective knowledge application, the organisations are able to enhance new product development and create more inno- vative production processing technologies. For instance, effective ‘knowledge application’ is important in every phase of the ERP life cycle, particularly in maintenance and upgrades (Markus et al. 2003), and is a frequent organisational concern that appears to be closely related to ERP system success (Sumner 2003). Therefore, the researchers present the following hypothesis:

H4: There is a significant, positive relationship between knowledge application and ERP system success.

4. Research methodology

4.1 The survey instrument

The two study constructs were validated and the study model tested employing survey data from several previous studies. All questionnaire items employed five-point Likert scales with the end values (1) ‘Strongly Disagree’ and (5) ‘Strongly Agree’, and the middle value (3) ‘Neutral’. Thirty six survey questions were designed to measure KM-competence as follows: eight questions on knowledge creation encompass Filius, de Jong, and Roelofs (2000) and the researchers’ questions. Eight measures on knowledge transfer adopted from Shao, Feng, and Liu (2012) and Filius, de Jong, and Roelofs (2000). Ten items on knowledge retention adopted from Filius, de Jong, and Roelofs (2000) and Arif et al. (2009) and another ten items of knowledge application adopted from Arif et al. (2009) and Filius, de Jong, and Roelofs (2000).

Thirty-six survey questions were also designed to measure ERP system success as follows: four questions on ERP individual impact and four measures on ERP workgroup impact adopted from Ifinedo and Nahar (2009). Six items on ERP organisational impact, six items on ERP information quality and eight items on ERP system quality adopted from Sedara and Gable (2010). Five measures on ERP vendor/consultant quality adopted from Ifinedo and Nahar (2009).

4.2 The sample profile

Consistent with the purpose of this study, Jordanian companies that have implemented an ERP system were sampled. In order to determine an appropriate sample, data collection was divided into two stages. In the first stage, the researchers determine an initial sample based on Interview of ERP consultants, vendors’ representatives in Jordan. In the next step, the researchers firstly contacted the preliminary informant in each firm (IT manager and business managers) to solicit cooperation, and to identify the key informants. Business managers are chosen because these executives are ideally suit- able to act as key informants in the assessment of IT (and ERP) success or impacts on their organisations and IT man- agers are important actors in modern organisations because the use of IT systems is growing for organisations that are gradually realising the strategic importance of IT systems in their operations (Ifinedo 2007, 271).

Out of (455) contacted organisations, (116) organisation accepted to participate in the survey. Questionnaires were sent to the IT/Business managers of each of the 116 sampled firms. A follow-up procedure began with a reminding telephone or email about one month after the initial questionnaire was sent to the key informant. A total of 173 questionnaires were returned.

5488 M. M. Migdadi and M. K. S. Abu Zaid

As shown in Table 1, the majority (68.2%) of the companies which participated in the study belongs to the industry sector, while 31.8% belongs to the service sector. Moreover, 9.2% of the companies of the sample employ 50 and less employees, 32.9% employ 51 to 100 employees, 57.8% employ more than 100 employees. The majority of the respon- dent companies (70.7%) have been using an ERP system for more than 2 years, while 29.3% used it for less than 2 years.

As for the respondents, the majority (56.1%) of sample of respondents were IT managers, while (43.9%) were top managers. Accordingly, the sample statistics indicated that employee experience in the current organisation with less than 10 years represents 54.9% of the respondents, while the second larger category (33.5%) represents respondents that have experiences in the current organisation between 10 and 20 years. Finally, (11.6%) represents the participants that have experiences in the current organisation with more than 20 years.

4.3 Validity and reliability

Confirmatory factor analysis (CFA) was used to test the uni-dimensionality of the study constructs. Model fit indices are χ2/df ≥ 3, comparative fit index (CFI) values >0.9, standardised root mean square residual (SRMR) values <0.08, the incremental fit index (IFI) and the Tucker–Lewis index (TLI) are above the recommended value of 0.90 as suggested by (Hu and Bentler 1999).

Based on CFA, convergent validity and discriminant validity were assessed. While convergent validity is achieved if agreement between indicators and the underlying theoretical construct is reached. The discriminant validity measures the degree to which the specified latent factors differ even though they are correlated.

Based on factor loading convergent validity was assessed. All standardised factor loadings were greater than (0.50) and hence, they were statistically significant (p < 0.05). In addition, composite reliability (CR > 0.6), and average variance extracted (AVE > 0.5) were well above the recommended value, as shown in Table 3.

The researchers assess discriminant validity by comparing the square roots of the AVE values of each construct with the correlations between the means and other constructs. The discriminant validity is verified when an individual con- struct’s AVE square root value is higher than its correlations with other constructs. Table 2 shows the results of confirm discriminant validity. All an individual construct’s AVE square root values are higher than its correlations with other constructs (Lai et al. 2012). Finally Cronbach’s alpha coefficient were used to examine the reliabilities among the items within each construct. Table 2 shows that Cronbach’s alpha of all constructs were above 0.70 (Nunnally 1978). So reliability is achieved.

5. Discussion and conclusion

5.1 Descriptive statistics

Table 2 shows the means, standard deviations and correlations of all variables. There are 2 implications regarding (Table 2): First: there is a positive relationship amongst all ERP knowledge dimensions with the correlated range of

Table 1. Sample profile.

Sector Frequency Percent

Industry 118 68.2 Service 55 31.8 Number of employees 50 and less 16 9.2 51 – 100 57 32.9 101 and more 100 57.8 Positions Top managers 76 43.9 IT managers 97 56.1 Employee experience in the current organisation Less than 10 years 95 54.9 10 and 20 years 58 33.5 More than 20 years 20 11.6 Using an ERP system More than 2 years 82 70.7 2 years and less 34 29.3

International Journal of Production Research 5489

T ab le

2. M ea n,

st an da rd

de vi at io n,

G ro nb

ac h’ s α co rr el at io n be tw ee n K M

co m pe te nc e an d E R P sy st em

su cc es s.

V ar ia bl e

M ea n

S ta nd

ar d de vi at io n

α 1

2 3

4 5

6 7

8 9

0 11

E R P kn

ow le dg

e cr ea tio

n 3. 82

.6 62

.7 22

(. 77

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ow le dg

e tr an sf er

4. 00

.8 12

.7 51

.7 29

** (. 76

6) E R P kn

ow le dg

e re te nt io n

3. 71

.6 09

.7 61

.6 59

** .5 75

** (. 76

5) E R P kn

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3. 66

.6 16

.7 89

.6 14

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** .7 25

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6) W or kg

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.7 24

.8 11

.1 53

* .0 53

.2 42

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** (. 82

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.7 16

.7 71

.7 69

** .7 54

** .7 17

** .7 21

** .1 86

* (. 79

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pa ct

3. 84

.6 72

.8 81

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** .2 60

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** .2 07

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al ity

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qu al ity

3. 84

.7 16

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** .2 17

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qu al ity

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.6 33

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1)

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N ot es : T he

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5490 M. M. Migdadi and M. K. S. Abu Zaid

(0.515–0.729). Second: there is also a strong relationship between ERP Knowledge dimensions and ERP success with the correlated range of (0.153–0.769).

5.2 Assessment of the structural model

We tested the proposed model by performing structural equation modelling using AMOS 16.0. Figure 2 presents the results of the structural model. The model fit indices are χ2/df = (2.037), CFI = .969, RMSEA = 0.078 and SRMR = 0.031, TLI = .943, these indices are acceptable (Hu and Bentler 1999).

The results of analysis showed that there is a significant, positive impact of ERP knowledge creation on ERP system success. Thus, the proposed positive effect (H1) (β = 0.549, t = 5.480 p < 0.05) is supported. Also, the results of analy- sis showed there is a positive significant impact of ERP knowledge retention on ERP success supporting Hypothesis 2 (β = 0.179, t = 2.287, p < 0.05). Therefore, the researchers accept H2, ERP knowledge retention is found to significantly, positively influence ERP system success. ERP knowledge transfer is found to significantly, positively affect ERP system success (β = 0.241, t = 2.949 p < .05).

Finally, ERP knowledge application has positive effect on ERP system success (β = 0.256, t = 3.208, p < 0.05). The results also indicate that ERP system success construct is robust since all seven observed variables are strongly loaded to the latent variable.

5.3 Discussion of study findings

The main goal of this study is to empirically examine the impact of each variable of the knowledge management com- petence construct (knowledge transfer, knowledge creation, knowledge retention and knowledge application) on the ERP system success construct (individual impact, workgroup impact, organisational impact, information quality, system quality, vendor/consultant quality and ERP impact). Then, empirically investigate the impact of knowledge management competence as a formative index construct on the ERP system success construct in Jordan. To that end, this study built upon Sedara and Gable (2010) and Ifinedo and Nahar (2009) extended ERP system success measurement model to include workgroup impact and vendor/consultant quality not included in the Sedara and Gable (2010) model. The results showed that a large proportion of variance in model, i.e. about 46% is explained by the knowledge management compe- tence variables (as shown in Figure 2). Our results provide strong support for the five hypotheses of this study. The study found a strong support for the positive relationship between the knowledge creation variable and ERP system suc- cess construct (H1). This result, which has the largest path coefficient value (β = 0.549) strongly affirms the view that ERP knowledge acquired from external sources such as consultants and vendors which incorporated into the organisa- tion employees’ ERP knowledge to create new ERP knowledge, plays a major role in the ERP system success. This finding is consistent with other prior studies affirming the existence of such a relationship (Davenport 1998b; Timbrill 2006).

The path between knowledge retention and ERP system success was found to be significant enough to support the prediction in hypothesis (H2). Consistent with this line of thinking Chang and Gable (2001) stated that it is imperative to avoid a situation in which ERP died a way along the departure of certain personnel. This is a daunting challenge for organisations to maintain the integrity of ERP knowledge and keep the momentum of innovations and reengineering.

EKC

EKR

Individual impact

Workgroup impact

Organizational impact

ERP success Information quality

EKT System quality

Vendor/consultant quality EKA

.549

.179

.241

.256

.915

.205

.182

.317

.209

.704 R2= .457

Figure 2. Structural model of the hypothesised testing.

International Journal of Production Research 5491

Hypothesis Three (H3), which predicted a significant, positive relationship between knowledge transfer and ERP system success, is also strongly supported by our data. This finding is consistent with other prior studies affirming the existence of such a relationship such as Wang et al. (2007), which found that effective knowledge transfer and sharing can lead to better fit between ERP systems and organisational processes, further, to enhance business performance and achieve competitive advantage. In addition, Shao, Feng, and Liu (2012) found that explicit and tacit knowledge sharing to mediate the relationship between organisational culture and ERP system success.

The discussion for Hypothesis Four (H4) is similar to the discussion for hypothesis three (H3) since there is a strong association between ERP knowledge application and ERP system success. This result indicates that benefits will be gained from the application and usage of the ERP system. In that respect, the study result is consistent with the findings of previous studies such as Wang et al. (2007), which found a strong relationship between ERP knowledge application variable and ERP system success construct. The researchers stated that if the ERP-related knowledge can be effectively absorbed and utilised by a client, it is more likely to satisfy the client’s needs and meet the expected benefits from ERP system.

5.4 Theoretical implications

The goal of the study was to statistically test the implicit, positive relationships between KM-competence dimensions and ERP systems success; the hypotheses state that there is a significant, positive relationship between each dimension of ERP-related KM-competence and ERP system success. The research presents quantitative, empirical evidence of sig- nificant, positive relationships between KM-competence dimensions and ERP system success. Study implications for research are several. It is believed that this is one of few empirical studies to have quantitatively evidenced statistically significant, positive relationships between knowledge management (KM-competence) dimensions and system success (ERP system success). Although past IS success studies have reported anecdotal evidence of such relationships, quantitative empirical evidence has been lacking. The squared multiple correlation coefficient (R2) of .457 indicates that KM-competence explains fully almost half the variance in ERP system success. We believe the study results offer useful guidance to future researchers with interest in empirically evaluating relations between KM-competence and its possible antecedents and consequences.

This research has implications for IS success, in general and ERP system success in particular. While the original DeLone and McLean (1992) IS success model has been extensively tested in the literature, not many have used the DeLone and McLean’s (1992) schema and other related conceptualisations to assess the success or effectiveness of ERP applications in business organisations. Thus, our research effort may entice other ERP researchers to consider this area of study. With more and more emerging studies in this particular area, it is reasonable to expect that adopting organisa- tions will be better informed as to how to improve the effectiveness of their ERP packages in their respective contexts.

In particular, our research effort extends an ERP system success model proposed by Sedara and Gable (2010) through the utilisation of Ifinedo and Nahar (2009) model. Furthermore, we contributed to the literature by testing the relationships between KM-competence dimensions and the ERP system success construct. This study has responded to the call made by several studies such as Petter, DeLone, and McLean (2008) for studies examining the relationships amongst constructs that are employed to examine the effectiveness of IS to be commissioned. By specifically using the Sedara and Gable (2010) ERP system success framework as a base, we have responded to the call made by Petter,

Table 3. Model fit indices.

Construct χ2/df CFI SRMR IFI TLI CR AVE

ERP knowledge creation 2.345 0.964 0.042 0.964 0.947 0.892 0.599 ERP knowledge transfer 2.531 0.97 0.056 0.971 0.941 0.895 0.587 ERP knowledge retention 2.8 0.944 0.046 0.945 0.906 0.759 0.585 ERP knowledge application 2.661 0.965 0.025 0.966 0.926 0.914 0.587 Workgroup impact 2.026 0.99 0.03 0.99 0.971 0.818 0.673 Individual impact 2.057 0.992 0.016 0.992 0.973 0.874 0.636 Organisational impact 1.958 0.975 0.026 0.975 0.956 0.873 0.513 Information quality 1.286 0.997 0.015 0.997 0.992 0.873 0.541 System quality 1.785 0.989 0.017 0.99 0.973 0.899 0.507 Vendor/consultant quality 1.4 0.998 0.007 0.998 0.99 0.846 0.529 Criterion measure 1.969 0.987 0.013 0.987 0.956 0.792 0.564

5492 M. M. Migdadi and M. K. S. Abu Zaid

DeLone, and McLean (2008) for IS researchers to use that model (or an extended version of it) to enhance theory development in this area.

Indeed, our re-specified ERP system success measurement model strikes a balance between comprehensiveness and parsimony. We utilised Ifinedo and Nahar (2009) which added two new dimensions to the Sedara and Gable’s (2010) model, which we also argued are pertinent for the evaluation of ERP system success in latter stages of the system life cycle for adopting organisations. Prior research mainly uses the original D & M IS success model to assess the effectiveness of IS without paying due attention to the ERP KM-competence dimensions. Hence, model test results in Figure 1 provide additional evidence of the validity of the ES-success/IS-Impact construct as reported in Sedara and Gable (2010) and Ifinedo and Nahar (2009).

5.5 Practical implication

Our research has useful implications for practitioners as well. The study findings suggest potential gains to practice from increased emphasis on ERP-related knowledge management competence. Past studies (e.g. Mabert, Soni, and Venkataramanan 2000; Ifinedo 2007; McGinnis and Huang 2007a) and the commercial press (Stedman 1999; Songini 2000) suggest that many organisations are dissatisfied with benefits obtained from their ERP Systems investments. Having explained almost half of the variance in ERP-success, the study identifies KM-competence as possibly the most important antecedent of success. Given that many ERP installations around the world struggle to deliver expected bene- fits, we recommend a stronger emphasis on related knowledge management. Study results reinforce the early call by Gable, Scott, and Davenport (1998) for ‘cooperative ERP lifecycle knowledge management’, who argued ‘There is strong motivation for better leveraging ERP implementation knowledge and making this knowledge available to those involved in the ongoing evolution of the ERP system’ (Gable, Scott, and Davenport 1998, 228).

Chang et al. (2000) suggested that ERP ‘clients [organisations] require a lifecycle-wide knowledge sourcing strategy’. It is our belief that each of the four phases of KM-competence must be addressed in all life cycle-wide management plans for ERP Systems. Though past knowledge management initiatives have typically sought to improve creation (exploration) of knowledge and knowledge reuse (exploitation) (Levinthal and March 1993), this study demon- strates the unique importance of all four knowledge management phases; each phase making a distinct and significant contribution to KM-competence. Given the observed strong positive relationship between KM-competence and ERP sys- tem success, the goal of IS researchers should be to aid practice to effectively and efficiently maximise their ERP-related KM-competence, thereby improving levels of ERP system success.

The study findings suggest that improvement in any and all of the KM-competence dimensions/phases will result in improved levels of ERP system success. It is further suggested that success with each phase of the KM life cycle is a necessary but not sufficient requirement for success with each subsequent KM life cycle phase (the life cycle represents a process model rather than a causal model). Thus, although the final ‘Application’ phase may be the most causally influential phase with ERP system success, all KM phases are important, commencing with creation; knowledge must be created to subsequently be retained, transferred and applied.

First, as this study is partly motivated by the need to provide managers with guidelines for assessing the success of their ERP software, we hope that our comprehensive list of ERP system success dimensions and measures could be used as a diagnostic tool in success evaluations of such packages. The identified dimensions/measures used in this study can be used to assess the effectiveness of the system for the individual, their work unit and the entire organisation.

In brief, the re-specified ERP system success measurement framework is simple yet comprehensive. If the proposed measurement evaluation tool is utilised appropriately and periodically, management could use it to obtain timely feed- back about the ‘success’ of the ERP package in their setups. Corrective actions and measures aimed at improving less than favourable aspects of the package could then be taken to address such concerns.

Second, this study implies that for a clear picture of the effectiveness of ERP in the adopting firm to be understood, management must accommodate several levels of analysis, including the sub-unit level. Finally, the attention of practi- tioners is drawn to post-implementation ERP system success issues, which we argued should not be conflated with ERP implementation CSF.

5.6 Conclusions

Study results reinforce the early call by Gable, Scott, and Davenport (1998) for ‘cooperative ERP life cycle knowledge management’, who argued ‘There is strong motivation for better leveraging ERP implementation knowledge and making this knowledge available to those involved in the ongoing evolution of the ERP system’ (Gable, Scott, and Davenport 1998, 228).

International Journal of Production Research 5493

The study findings also suggest that improvement in any and all of the KM-competence dimensions/phases will result in improved levels of ERP system. It is further suggested that success with each phase of the KM life cycle is a necessary but not sufficient requirement for success with each subsequent KM life cycle phase (the life cycle represents a process model rather than a causal model). Thus, although the final ‘Application’ phase may be the most causally influential phase with ERP system success, all KM phases are important, commencing with creation; knowledge must be created to subsequently be retained, transferred and applied. Finally, model test results in Figure 1 provide additional evidence of the validity of the ES-success/IS-Impact construct as reported in Sedara and Gable (2010) and Ifinedo and Nahar (2009).

5.7 Study limitation and future research

After discussing the study’s implications we will briefly discuss its other limitations. This study’s sample is not randomly selected. Nor can we rule out personal bias in instances where a single

informant presented an average view of his respective organisation. We used perceptual measures in this study; it is likely that objective measures of ERP system success (e.g. profit and productivity measures) might yield a result different from ours.

Additionally, when the measures of some of the constructs used in this study are expanded, it is likely that more useful insight will emerge. We did not control for the types of ERP used by the participating firms. Our sample comprised mixed ERP software, including top-brand names (e.g. SAP and Oracle) and mid-market products (e.g. Nova). It is possible that the heterogeneous nature of the ERP systems used for our study are limiting.

The study has emphasised a-theoretical, somewhat inductive evidence of a statistical relationship between KM-competence and ERP system success. Further research is warranted, focusing on theory building or identification of potential theory to explain the strong positive relationship observed. Also, the study suggests a quasi-theoretical view on the KM life cycle and KM-competence, conceptualising KM-competence as a composite formative index comprised of the four life cycle phases; further theoretical justification for this conception too is warranted.

Disclosure statement

No potential conflict of interest was reported by the authors.

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  • Abstract
  • 1. Introduction
  • 2. Theoretical framework
    • 2.1 Knowledge management competence
      • 2.1.1 Knowledge creation
      • 2.1.2 Knowledge retention
      • 2.1.3 Knowledge transfer
      • 2.1.4 Knowledge application
    • 2.2 ERP system success measurement
      • 2.2.1 System quality
      • 2.2.2 Information quality
      • 2.2.3 Individual impact
      • 2.2.4 Workgroup impact
      • 2.2.5 Organisational impact
      • 2.2.6 Vendor/consultant quality
  • 3. Knowledge management competence and ERP success
    • 3.1 Knowledge creation vs. ERP system success
    • 3.2 Knowledge retention vs. ERP system success
    • 3.3 Knowledge transfer vs. ERP system success
    • 3.4. Knowledge application vs. ERP system success
  • 4. Research methodology
    • 4.1 The survey instrument
    • 4.2 The sample profile
    • 4.3 Validity and reliability
  • 5. Discussion and conclusion
    • 5.1 Descriptive statistics
    • 5.2 Assessment of the structural model
    • 5.3 Discussion of study findings
    • 5.4 Theoretical implications
    • 5.5 Practical implication
    • 5.6 Conclusions
    • 5.7 Study limitation and future research
  • Disclosure statement
  • References

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