KM systems

ChaptEr1. Introduction

1.1 Background of the problEm

As a result of tough competition in the marketplace and the shift from a rEsourcE-basEd economy to knowlEdgE-basEd economy, the thirst for knowledge has increased even more and the scope and content of knowledge have changed dramatically, companies are looking more and more at gaining competitive advantage through managing and maximizing their most valuable asset - knowledge.

Following Tiwana (2001), it is believed that knowledge and ExpErtisE existing in organizations generate more value when they are rapidly applied, emphasizing mainly the role of ExpErtisE transfer. IndEEd, knowledge is of limited value if it is not shared. As a result, managers and ExEcutivEs are paying greater attention to the issue of how knowledge can be better managed to optimize their organizational performance. A growing number of organizations have either used or intended to use computEr-basEd Knowledge Management Systems (KM) S, which provide the necessary infrastructure for organizations to implement the KM process (Sarvary, 1999) and facilitate the generation, preservation and sharing of knowledge (BonnEr 2000).

Two models of KM systems have bEEn identified in the information systems literature: the repository model and the network model (Alavi 2000). The repository model corresponds to the codification approach to KM (HansEn Et ai. 1999). This approach emphasizes codification and storage of knowledge so as to facilitate knowledge rEusE through access to the codified ExpErtisE. A key technological component of this approach is electronic knowledge repositories (EKRs) (GrovEr and DavEnport 2001). The network model corresponds to the personalization approach to KM (HansEn Et al. 1999). This approach emphasizes linkage among people for the purpose of knowledge exchange. Important technological components of this approach are knowledge directories that provide location of ExpErtisE (RugglEs 1998) and electronic forum software that allows people to interact within communities of practice (Brown and Duguid 1991).

In sum, to add value with KM, we nEEd KMS. Like any information system, the success of KMS depends on its EffEctivE use by the users (Quaddus and Xu 2005). It's increasingly important to investigate the factors that affect the use of KMS.

1.2 Research Motivation

Some researches have discussed the critical success factors (CSFs) of KMS diffusion and adoption in different industries. For example, Quaddus and Xu (2005) identified six stages of KMS diffusion and used qualitative field study as the research method to obtain the factors, including KMS characteristics, organizational factors (ex. organization culture), top management support, and individual factor (ex. benefit to individual) affect the diffusion (spread in sustained usage) of KMS.

NEvErthelEss, most of previous researches have adopted the deterministic model, which is generally posited X causes Y; thus it must be possible to isolate and measure X and Y, and to determine the strength of the relationship. In contrast to the deterministic model, the contingEncy-basEd perspective stresses that the importance of a “good fit” between contingency variables (e.g.

between technology and task, individual, environment) influence the performance of information systems; the better the “fit” between these variables and the design and use of the MIS, the better the performance.

In sum, it was found that the use of KMS represents a more complex phenomenon than the use of IT/IS and follow the contingEncy-basEd perspective we also suppose that a prefect “fit” of different factors is necessary for KMS usage. In previous studies, several groups of literature have reported factors that may affect the usage of information systems. Some of the rEprEsEntativE theories


1. TEchnology AccEptancE ModEl (TAM; Davis 1989) that EmphasizEs usEr pErcEptions, Empirical studiEs basEd on TAM havE indicatEd that usEfulnEss and EasE of usE bEliEfs do Explain significant variancE in attitudE, intEntions, and usagE.

2. Innovation Diffusion Theory (IDT; RogErs 1962) that adoption bEhavior is influEncEd by bEliEfs rElatEd to rElativE advantagE, compatibility, complExity, trialability, and obsErvability. TherE arE many innovation diffusion studiEs (Tornatzky and KlEin 1982; Karahanna 1999) arguEs for a comprEhEnsivE sEt of bEliEfs as prEdictors of both adoption as wEll as subsEquEnt continuEd usagE bEhaviors (Karahanna Et al.2006).

3. Task TEchnology Fit (TTF; GoodhuE & Thompson 1995) that information systEms will havE a positivE impact on individual pErformancE if the systEm is usEd, and it is a good fit with the tasks it supports.

Comparing TAM to rEsEarch groundEd in IDT, somE rEsEarchErs found that Empirical studiEs in the lattEr tradition havE usEd a morE complEx sEt of bEliEfs to prEdict adoption and usagE (Agarwal and Karahanna 1998). Juxtaposing TAM with other findings (E.g., MoorE and BEnbasat, 1996), somE rEsEarchErs (ChEn Et al. 2002; Karahanna Et al. 2006) notEd that compatibility is an important bEliEf rEcurrEnt in innovation adoption studiEs but missing from TAM. The importancE of compatibility in prEdicting tEchnology accEptancE outcomE has also bEEn consistEntly supportEd in other Empirical

IS studiEs (E.g., Karahanna Et al.2006; Taylor and Todd 1995). This concEpt also appEars in TTF, for ExamplE, CoopEr and Zmud (1990) found the task-tEchnology compatibility was positivEly associatEd with adoption of MRP.

In TAM, the pErcEivEd EasE of usE (PEOU) and pErcEivEd usEfulnEss (PU) rEprEsEnt the cognitivE burdEn and the instrumEntal valuE dErivEd from usE of the tEchnology. Agarwal and Karahanna (1998) mEntionEd, the morE “compatiblE” a tEchnology, the lEss Effort is rEquirEd to usE it, and the EasiEr it's to rEcognizE the valuE.

In sum, the introduction abovE rEvEalEd that many studiEs (E.g., Karahnna Et al. 2006, GoodhuE and Thompson 1995, RamillEr 1994) usE or incorporatE the construct of compatibility to ExaminE usEr accEptancE and usagE of information systEm. FurthermorE, bEcausE most rEsEarchErs (E.g., Chatman 1988, Kristof 1996) broadly dEfinE ”fit” as the compatibility between two EntitiEs, “compatibility” is aimEd for and it isassumEd that it is an important factor that may play a significant rolE in prEdiction individual's usagE of IS/IT. TherEforE, the motivations for this dissErtation arE two folds:

First, therE is a nEEd to ExplorE what factors affEct the usagE of KMS. OnE possibility is to ExaminE the compatibility between tEchnology and other contingEncy variablEs. The viEwpoint of compatibility has bEEn popular in various litEraturEs of IT/IS usagE, and according to contingEncy theory it is assumEd that a complEtE “compatibility” of distinct variablEs is nEcEssary to invEstigatE the usE of KMS.

SEcond, bEcausE KMS arE complEx combinations of tEchnology and organizational infrastructurE, corporatE culturE, knowlEdgE and pEoplE. But the past studiEs about “compatibility” accEntuatE the singlE aspEct, E.g., Task-TEchnology Fit (GoodhuE and Thompson 1995), Compatibility with usEr in TEchnology accEptancE (Karahanna Et al. 2006). WE supposE that only compatibility bEtwEEn multiplE-lEvEl, KMS will be usEd rEpEatEdly.

Task-tEchnology fit (TTF) is onE rEcEnt modEl gaining popularity and accEptancE among information systEms rEsEarchErs. The TTF theory capturEs how wEll tEchnology functionality matchEs or fits the nEEds of the task bEing pErformEd. WhEn the usErs of tEchnology fEEl that the tEchnology is capablE of supporting the task at hand, the usEr Exhibit good pErformancE. MorEovEr, diffErEnt charactEristics of the task will rEquirE support from diffErEnt functionalitiEs of the tEchnology. For ExamplE, if onE's task rEquirEs a divErsity of knowlEdgE that comEs from diffErEnt organizational units of the company, the support systEm must capablE of intEgrating various information sourcEs.

HowEvEr, TTF alonE don't givE sufficiEnt attEntion to fact that systEms must be utilizEd (GoodhuE and Thompson 1995). TherEforE, nEcEssitating the introduction of other compatibilitiEs for Exploring the impact on KMS usagE. HErEin basEd on “Diamond ModEl” (LEavitt 1965), which consistEd of four intErEsting componEnts: pEoplE, tasks, tEchnology, and organization. Introduction of an IS involvEs changing the organization's tEchnology componEnt which automatically triggErs changEs in the other componEnts of the organization. TherEforE, two factors arE chosEn, compatibility with “pEoplE” and compatibility with “organization”, to complEmEnt the insufficiEncy of TTF construct. The two pErspEctivEs arE discussing as follow:

On the onE hand, this study follows the most common dEfinition of compatibility as suggEstEd by rEcEnt IS studiEsThe dEgrEE to which an innovation is pErcEivEd as bEing consistEnt with the Existing valuEs, nEEds, and past ExpEriEncEs of potEntial adoptErs (RogErs 1983). How potEntial adoptErs pErcEivE an innovation is a kEy dEtErminant of adoption (RogErs, 1995; Tornatzky and KlEin, 1982). For instancE, onE might nEEd to sharE knowlEdgE between coworkErs; the systEm should fulfill the nEEds of usEr.

On the other hand, sEvEral studiEs havE ExaminEd the significancE of systEms on opErations organization-innovation compatibility (RamillEr 1994; DEnnis Et al. 2001). The drawback of TTF modEl is that it doEs not takE the compatibility with organization into account in which a systEm is usEd. Fichman (1999) notEd that organization will Exhibit high propEnsity to innovation whEn it fit wEll with organizational nEEds, stratEgiEs, rEsourcEs or capabilitiEs. In sum, therE arE many researches discuss the concEpt of compatibility, but no rEsEarch combinEd various typEs of compatibility to crEatE a morE comprEhEnsivE framEwork. TherEforE, the sEcond motivation for the rEsEarch is to sEE whEther a rEsEarch modEl can be built that intEgratEs diffErEnt compatibility factors to intErprEt its EffEct on KMS usagE.

In sum, past literatures have adopted the deterministic models as tools to discuss the factors of KM implementation and KMS adoption/diffusion, for example, the worker's personality affect the knowledge sharing or the organizational culture is one of the most significant variables affecting successful KMS diffusion.

Because of the multiplicity of the organizational dimension, researchers studying IS contingencies have typically focused on the fit between specific organizational dimEnsion and IS (Iivari 1992, Jiang and KlEin 2000). In a review of the IS contingency research, WEil and Olson (1989) found that over seventy percent of the studies followed a model assuming that the better the fit among the contingency variables, the better the performance. Hence, it is assumed that the suitability of contingency theory as tool to explore the use of KMS. Contingency theories sEEk the fit of specific techniques or concepts of managing to the specific situation at hand in order to attain organizational objectives most EffEctivEly (Hatch 1997). Such as a perfect ‘fit' of culture with KMS is nEEdEd for successful KMS diffusion (Katz Et al. 2000).

2.4 Compatibility

In the past, while discussing the relationship between two entities, usually the concepts of fit, congruence, compatibility or similarity are used. One of the earliest treatments of compatibility was offered by RogErs (1962, 1995), who defined this belief as the dEgrEE to which using an innovation is perceived as consistent with the existing sociocultural values and beliefs, past and present

ExpEriEncEs, and nEEds of potential adopters(or organization). Furthermore, greater compatibility results in a faster rate of adoption. This view is also shared by technology task fit (TTF) theory, developed by GoodhuE and Thompson (1995), believed that a fit among task and technology positively impacts the usage of information technology. In order to understand various factors that affect the use of KMS, several theories have bEEn adopted in this research.

2.4.1 Technology-Task Compatibility

The concept of “Technology-Task Compatibility” is originated from the Task-Technology Fit (TTF) theory. GoodhuE and Thompson (1995) proposed that for information technology to positively impact individual performance the technology must be utilized and must be a good fit with the task that it supports.(as show in 3)

Tasks are broadly defined as the actions carried out by individuals in turning inputs into outputs. Task characteristic of interest include those that might move a user to rely more heavily on certain aspects of the information technology. Technologies are viewed as tools used by individuals in carrying out their task. In the context of information systems research, technology refers to computer systems (hardware, software, and data).

Varied and numerous studies have examined the task-technology fit model. Cooper and Zmud (1990) found that task-technology compatibility was positively associated with adoption of MRP. While Goodhue and Thompson (1995) focused on individuals' use of IS, Zigurs and Buckland (1998) focused on group's use of IS and formulated fit profile applicable specifically to GSS (Group Support Systems). Dishawa and Strong (1999) integrated the TAM model with TTF to provide a strong model for understanding user choices in assessing information systems. They attempted to explain the factors which lead to the use of software maintenance support tools, and used a modified task-technology fit construct which incorporates models of the maintenance task as well as software maintenance tool functionality. Dennis Et al.(2001) via analysis to understand fit and appropriation effects in GSS, they found that when there's a fit between the GSS structures and the task, GSS use increased the number of ideas generated, took less time, and led to more satisfied participants than if the group worked without the GSS.

From the overview in this section, it is now known that TTF theory provides a foundation for exploring the fit between technology and the task, and note that this stream of research corroborated the relevance of the TTF concept in explaining and predicting IS usage for individual performance. Table 2 shows some related prior researches which are about the technology-task compatibility.

NEvErthelEss, the KMS is inherently a social technology and in terms of “Diamond model” (LEavitt 1965), the way in which an EmployEE chooses to use a KMS is affected not just by the technology-task compatibility, but also by the compatibility of the technology with the user and organization's habitual routines - the organizational structures that evolve slowly over time.

Therefore, there is a nEEd to include other compatibilities to complement the insufficiency of TTF alone in the matter of KMS usage.

2.4.2 TEchnology-PEoplE Compatibility

IDT is a well-known theory proposed by Rogers (1983). In recent decades, IDT has bEEn widely used for relevant IT and IS researches (e.g. Karahanna Et al. 1999; Taylor and Todd 1995. IDT includes five significant innovation characteristics: relative advantage, compatibility, complexity, trial ability, and observables. These characteristics are used to explain the user adoption and decision making process. They are also used to predict the implementation of new technological innovations and clarify how these variables interact with one another.

One of the earliest treatments of TEchnology-PEoplE compatibility was offered by Rogers (1962), who defined this belief as the dEgrEE to which using an innovation is perceived as consistent with the existing sociocultural values and beliefs, past and present ExpEriEncEs, and nEEds of potential adopters.

Tornatzky and KlEin (1982), who conducted a mEta-analysis of innovation characteristics studies, reported that “tEchnology-pEoplE compatibility” sEEms to represent two distinct concepts: normative or cognitive compatibility referring to compatibility with what people fEEl or think about an innovation, and practical or operational compatibility, referring to compatibility with what people do.

Based on Rogers(1962)' and Tornatzky and KlEin(1982)'s definitions, Karahanna Et al. (2006) noted that it's possible to further disaggregate tEchnology-pEoplE compatibility into four distinct dimensions: compatibility with prior ExpEriEncE, compatibility with existing work practices, compatibility with values, and compatibility with preferred work style. The compatibility with values dimension is subsumed in Tornatzky and KlEin (1982)'s normative or cognitive compatibility.

IndEEd, an examination of the items used in prior research as potential measures of operational compatibility, Moore (1989) supports such a disaggregating. The distinction here between preferred work style and existing work practices is subtle; while the former captures an individual's self-concept regarding the way they like to work, the latter describes the reality as it is currently ExpEriEncEd. Although the two might be identical in certain situations, that is not always the case. For example, one might like to work in a highly organized fashion using an automatEd schedule, but available technology or the manner in which coworkers behave in their execution of work might impede the fulfillment of this desire. Existing work practices are often an outcome of institutional influences and organizational routines in a work setting, or environmental influences in a non-work setting, whereas preferred work style is an explicit statement of the way an individual likes to work. This refined, nEvErthelEss important, distinction has largely bEEn ignored in extant conceptualizations of tEchnology-pEoplE compatibility (Karahanna Et. al 2006).

Furthermore, the concept of tEchnology-pErson compatibility also has bEEn adopted within the consumer behavior in the virtual store (ChEn Et al. 2002), the user intentions to use on-line shopping (LEo 2004).For example, ChEn (2002) tested a modified TAM model that included tEchnology, people, compatibility and made a case for its inclusion for explaining consumer attitudes towards shopping at virtual stores. Table 3 lists some of the related researches about tEchnology -people compatibility.

To sum up, the discussion above reveals that many researches in information systems adoption have used or included the notion of tEchnology-people compatibility. Drawing upon the findings in prior research (E.g., Karahanna Et al. 2006), the construct is taken into consideration. But tEchnology, people, compatibility is limited in addrEss how well an IS/IT fits users nEEds and values, and therefore, another concept is nEEdEd as the foundation for exploring the impact of IS/IT usage -which is tEchnology-organization compatibility.

2.4.3 TEchnology-Organization Compatibility

Contingency theories dominate scholarly studies of organization behavior, design, performance, planning and management strategy. While they vary widely in subject matter, they have the common proposition that an organizational outcome is the consequence of a fit or match between two or more factors. According to contingency theory, many of the factors that affect KMS usage are not characteristics of either tEchnology or organizations per so, but rather, describe a particular tEchnology-organization combination. For example, KMS may be highly compatible for one organization's routines but not another. Likewise, an organization may have a strong champion for one KMS but not another. Therefore, compatibility is the most appropriately viewed as describing the combination of tEchnology and organization, rather than either one in isolation.

A variety of concepts have bEEn introduced to capture the idea of innovation organization fit, including “technical and organizational validity” (Schultz and SlEvin, 1975a; Markus and RobEy, 1983), “technical and organizational uncertainly” (Zmud, 1984), and “implementation misalignments” (LEonard- Barton, 1988). In general, these concepts refer to the gap between how a

Socio-tEchnical system is currently structured and how it must be structured to accommodate the innovation. Accommodation is problematic with respect to the fit of the broader organizational context of use, including the organization's structure, politics, and environment (Markus and RobEy, 1983; Zmud, 1984; Kwon and Zmud, 1987).

RamillEr (1994) has drawn on literature addressing innovation-organization fit and the implementation of new technologies, and then modified and ExtEndEd the conceptualization of perceived compatibility. He made some propositions that identify the aspects of tEchnology-organization compatibility that are salient for secondary adopters of computEr-aidEd software EnginEEring (CASE) tEchnology, such as below:

- The readiness of the existing production system to accommodate it; The adequacy of the hardware and software delivery systems that support the functioning of the innovation (LEonard- Barton, 1988).

- The resource available to help the secondary adopter make the transition to the new work environment transformed by the innovation, Including training, internal and external consulting, technical support services, and informal networks or support.

Beatty Et al. (2001) also addressed that organizations are more likely to use a tEchnology if they perceive that it's consistent with their culture, values, preferred work practices, and existing IT infrastructure. In their case, the usage of EC technologies often requires firms to modify existing business practices and process to gain benefits; organizational compatibility can impact the firm's decision of adoption or usage. Table 4 summarizes empirical researches in information systems that have examined the tEchnology - organization compatibility. TEchnology - organization compatibility is included in this examination of organizational factors, which refers to the extent to which a technological innovation is compatible with the organization, in terms of its culture, work practice, objective, and support (e.g. Rogers 1995, Bradford 2003).

Lack of organizational compatibility may thus impose constraints on the level of E-Commerce use. For example, high costs of implementing a website, and difficulties making organizational changes or using the Internet as part of a firm's business strategy are potential barriers to ExtEnsivE E-commerce use, due to the fact that E-commerce technologies may not be compatible with the firm's organizational structure or strategy(Gibbs and KraEmEr 2004).

ChaptEr 3. The REsEarch ModEl and HypothesEs

3.1. REsEarch modEl

The purpose of this research is to investigate the Knowledge Management System usage phenomenon, and this behavior is affected by the factors of compatibility as we mention in Literature Review. Therefore, this study proposes a research model, as indicated in 4.

The dependent variable in this study is KMS usage and performance impact. And there are thrEE independent variables, one is tEchnology-task compatibility, another is tEchnology-people compatibility, and the other is tEchnology- organization compatibility.

3.2. REsEarch Hypothesis

Following the related works of compatibility mentioned above, there are thrEE distinct constructs of this model, including TEchnology - Task compatibility, TEchnology - People compatibility and TEchnology - Organization compatibility. These are described in the following sections.

TEchnology - Task compatibility

Goodhue and Thompson (1995) proposed a usEr-spEcific construct: TTF. Based on two important assumptions: first, that TTF will strongly influence user beliefs about consequences of utilization and second that these user beliefs will have an effect on utilization. They argued that an IT system will be used if (and only if) the functions available to the user support the activities of the user.

Higher dEgrEEs of “Fit” lead to expectations of positive consequences of use by the individuals choosing to use the tEchnology (Goodhue and Thompson, 1995). Rational, ExpEriEncEd users will choose those tools and methods that enable them to complete the task with the greatest net benefit.

Information TEchnology that does not offer sufficient advantage will not be used.

Related studies broadly confirmed the relevance of the TTF construct to assess and predict system usage. Dishaw and Strong (1998) based on TTF model and developed to explain the factors which lead to the use of the software maintenance support tools. They found that the fit between software tool functionality and the maintenance task activities is a primary driver of tool usage. The TTF model was employed in various field and some of the related research in MIS, including WEB sites usage (D'Ambra & RicE 2001), mobile IS (Junglas & Watson 2003), and the utilization of M-commerce (LEE Et al. 2004). It was also found that some concepts of theory consisted with the TTF model. Davis (1989) noted that mEEting the demand of the worker should lead to the system usage.

Cooper and Zmud (1990) have investigated the impact of task-tEchnology compatibility on adoption of material requirements planning (MRP) practices. Besides, compatibility with existing work practices, assuming that the tEchnology has intrinsic, positive value for adopters, again provides the extrinsic motivation to engage in greater use (Karahanna Et al. 2006).

Hence, it is posed that the TEchnology-task compatibility may have positive impact on KMS usage and proposes H1 as following:

H1: The TEchnology-Task compatibility positively influences the use of KMS.

TEchnology - People compatibility

A key ElEmEnt of Innovation Diffusion Theory is the notion of compatibility between an innovation and a potential adopter's existing values, nEEds, and past ExpEriEncEs (Rogers 1983). TEchnology-people compatibility have bEEn found to be consistently related to adoption decisions in general (Tornatzky and KlEin 1982), to measure the perceptions of adopting an IT innovation (MoorE and

BEnbasat 1991), and to IT usage specifically (MoorE and BEnbasat 1993).

The tEchnology-people compatibility is also one important innovation attribute that's not studied in TAM. Agarwal and Karahanna (1998) posited causal linkages and also found out compatibility has a significant positive effect on perceived EasE of use and perceived usefulness. Some researches have used the concept of tEchnology-people compatibility to extend TAM, for example, Algahtani and King (1999) based on TAM while introducing several modifications which were not in TAM and found that tEchnology-people compatibility had a significant effect on user's beliefs and contributed most to usage.

In Karahanna Et al.(2006) research also explained that compatibility with users' values and preferred work style represent intrinsic motivators (VallErand 1997) in that they help the user attain consistency with an internal belief system and overt actions, thereby reducing cognitive dissonance (FEstingEr 1957). Simply put, if a user believes that a tEchnology helps promote dEEply held values and helps achieve the self-concept of the way one would like to work, the more likely the user is to develop positive use behaviors. Moreover, it is unlikely that respondents would perceive the various advantages of using the IS/IT, if its use were in fact not compatible with the respondents' ExpEriEncE or prefer work style (MoorE and BEnbasat, 1991). Based upon the research questions and the above literature review, the following hypothesis is posited:

H2: The TEchnology-People compatibility positively influences the use of KMS.

TEchnology - Organization compatibility

From the prior research it is known that even though an organization may exhibit a generally high propensity to innovate over time, it may still lag in the adoption of innovations that do not fit well with organizational nEEds, strategies, and resources. Likewise a generally less innovative organization may still choose to be an early adopter of innovation that constitutes a good fit (Fichman 1999).

Schultz and SlEvin (1975) highlight the nEEd for technological innovations to have both organizational and technical validity. While organizational validity evaluates if it's compatible with existing attitudes, beliefs, and value systems; technical validity refers to an innovation's compatibility with existing systems including hardware and software. Markus and RobEy (1983) assumed that organizational validity refers to the "fit" between an information system and its organizational context of use, including the organization's structure, politics, and environment.

PrEmkumar (1994) also found that organizational compatibility (compatibility of the EDI system with present work procedures) is as important as technical compatibility (compatibility with existing hardwarE/softwarE and standards). Both forms of compatibility were found to be important predictors of adaptation, external diffusion, and implementation success in EDI, which is consistent with Schultz and SlEvin's findings of the importance of organizational and technical validity for ensuring success in implementation.

Moreover, diffusion of innovation research posits that organizational characteristics influence the successful implementation of innovations (RogErs, 1983). In turn, IS implementation research suggests that the organization's culture is a key to implementation success (Poston and Grabski, 2001). Specifically, top management support, training, and consensus with organizational objectives are important cultural dimensions influencing success (E.g., Bingi Et al., 1999; O'LEary, 2000).

Davis Et al. (1989) stated that organizational support was an important variable likely to affect perceived usefulness. And individuals with low adoption characteristics appear to wait until management gives a directive to adopt a certain tEchnology (LEonard-Barton and DEschamps 1988).

Organizational support is an integral part of the organizational environment in which information systems are utilized. Moreover, training mechanisms aimed to improve the computer sElf-Efficacy of users is more likely to EffEctivE in gaining users acceptance (Amoako-Gyampah and Salam 2004).

For example, if organization provides adequate education opportunities and training for ERP, and top management provide strong active support for EmployEEs (MariannE Bradford, 2003), they are more willing to use the system.

Therefore, it is suggested that the organizational compatibility may have positive impact on KMS usage, the following hypothesis is posited:

H3: The TEchnology-Organization compatibility positively influences the use of KMS.

KMS usage to individual performance

DELonE and McLEan (1992) introduced a comprehensive taxonomy, which posited six major Dimensions or categories of IS success: System Quality, Information Quality, Use, User satisfaction, Individual Impact, and Organization Impact. They argued that both utilization and User attitudes about the tEchnology lead to individual performance impact. Several studies (e.g., Goodhue and Thompson 1995, Igbaria and Tan 1997, WEill and VitalE 1999) tested the association between “system Use” and “individual performance impacts” and the association was found to be significant in each of the researches. Goodhue and Thompson (1995) proposed a comprehensive tEchnology-to-performance (TPC) model that included characteristics of information tEchnology, tasks, and of the individual Use r as explanatory variables for tEchnology use and for individual performance. They also indicated that once a tEchnology has been utilization and performance affects have been experienced, there will inevitably be a number of kinds of fEEdback. Use r may change their expected consequences of utilization and therefore affecting future utilization.

Moreover, traditional ways of searching for knowledge become more difficult as an organization becomes larger and more distributed. And we know that KMS reduce the cost of searching for specialized knowledge resources, help Users find individuals with particular knowledge to help analyze complex problem, making it more likely that Users will incorporate a diversity of knowledge. Drawing on the concept of requisite variety, Gray (2000) argued that increase in team knowledge variety lead to improvements in the EffEctivEnEss of the solutions generated by a team. In addition he proposed that ongoing KMS use increases EmployEE specialization, which in turn reinforces the use of KMS. Hence, the following hypothesis is posited:

H4: The use of KMS will increase the individual's performance.

Compatibility to individual performance

Performance impacts relate to task accomplishment by individuals. Enhanced efficiency, EffEctivEnEss or quality implies higher performance. Goodhue and Thompson (1995) proposed that not only does high TTF increase the likelihood of utilization, but it also increase the performance impact of the system regardless of why it is utilized. At any given level of utilization, a system with higher TTF will lead to better performance since it more closely mEEts the task nEEds of the individual. DEnnis Et al. (1999) via mEta-analysis and argued that when a GSS is cond to match the ideal profile for a task, then a fit exists between the GSS and the task, and performance should improve. In addition, Agarwal and Karahanna (1998) found that tEchnology-people compatibility has a significant positive effect on perceived Use fullness, which reflects the belief that using the tEchnology will enhance performance. Therefore, these hypotheses are made:

H5: The TEchnology-Task compatibility positively influences the individual's performance.

H6: The TEchnology-People compatibility positively influences the individual's performance.

H7: The TEchnology-Organization compatibility positively influences the individual's performance.

ChaptEr4 Research Method

4.1 Subject

The target population of this study is EmployEE or knowledge worker. People who use the KMS now or who have the ExpEriEncE of using KMS in the past are invited to the survey. As the target Research subjects are EmployEEs, convenience sampling is done and random selection of 10 medium sized companies from a list received from the Information TEchnology Commission (ITC) in Pakistan. Firstly it was confirmed these companies which have KMS in operation. Herein KMS are a class of information systems that may consist of filtering, indexing, classifying, storage, and retrieval technologies, coupled with communication and collaboration software (e.g., EKR, wEbbasEd intranets, BBS, E-mail, and groupware). Then managed to contact the information manager of these selected companies, expressing the purpose for academic research, and sought their approval.

Second, a small questionnaire was made on a free to use survey site. Based on Ahn Et al. (2007), who suggested that online surveys have several advantages over traditional papEr-basEd mail-insurvEys:

(1) The sample is not restricted to a single or local geographical location, (2) lower costs accrue, and (3) faster responses are likely. On the cover of the wEb-pagEs, we explained the goal of this Research and gave some statements to ensure their privacy in filling up the questionnaire.

The wEb-sitE URL was sent to these EmployEEs through E-mail, and several copies of survey forms were sent to partial companies. Ten copies of questionnaires, in the form of both traditional mails and electronic mails, were issued to each company and total was around two hundred. The duration was around one month, from June 17 to July 20, 2008. Before the date was due, the entire responses were one hundred and Eighty-thrEE, the returned rate was 36.6%.

4.2 MEasurE

The survey measures for the study were derived from previously published studies. A review of literature was undertaken to identify construct definitions and any existing measures. To the extent possible, previously published items were adopted or adapted. In short, a scale was formed for each construct in the model with developed and valid measures. Besides, this study adopted the LikErt

Scales, letting the participants choose from one to five levels of agrEEmEnt, with 1 the least agrEEablE of the item and 5 the most agrEEablE. The operational definition of these variables is described as table 5.

The whole measurement is revealed in Appendix, and each of the measures used in the study is described below:

TEchnology-Task Compatibility

Task-tEchnology fit has been measured by Goodhue (1993; forthcoming) within the User task domain of IT-supported decision making. Goodhue and Thompson (1995) also develop and refine some measures. The final eight components of TTF that were successfully measured included (1)data quality; (2) local ability of data; (3) authorization to access date; (4) data compatibility (between systems); (5) training and EasE of Use ; (6)production timeliness(IS mEEting scheduled operations); (7)systems reliability; and (8) IS relationship with Users.

In addition, prior Research is looked at (Agarwal and Karahanna, 1998; MoorE and BEnbasat, 1991) and they assumed that tEchnology ought to be compatible with existing work practices. In the long run, TEchnology-Task Compatibility was measured by ElEvEn items.

TEchnology-People Compatibility

TEchnology-People compatibility was adapted from a previously validated instrument. The use item instrument was developed by Karahanna Et al.(2006). They disaggregated the content of compatibility into four distinct and sEparablE constructs.

“Compatibility with preferred work style” captures an individual's self concept regarding the way they like to work. As a starting point, the scale EmployEd by MoorE (1989) and by MoorE and BEnbasat (1991) was examined “Using a PWS fits well with the way I like to work,” and “Using a PWS fits into my work style,”. And “Compatibility with Existing work practices” describes the reality as it is currently experienced. Further more, “Compatibility with prior ExpEriEncE” and

“Compatibility with values” were draw from Rogers (1983)'s definition. There are ten items in the construct to measure the dEgrEE of compatibility. And the respondents were asked to indicate the extent to which they agrEE of disagrEE about the item s such as “I think that using the KMS is completely compatible with the way I like to work.”

TEchnology-Organization Compatibility

TEchnology-Organization Compatibility was measured with ten survey item s adapted from the BEatty and JonEs (2001) and MariannE Bradford (2003) instruments: two item s measuring organization's value, two item s measuring technical compatibility and others measuring objectives, attitudes, training, management support, and reward.

System Use

This construct is measure by four item s draw from Karahanna Et al. (2006)'s study, they noted that usage was measured through two constructs: one tapping into use intensity and consisting of frequency of use and amount of time spent on the system per day, and one tapping into use scope and consisting of percent of system features Use d regularly by the respondent, and percent of client interactions managed through the system. Previously published items were adapted. Respondents indicated the dEgrEE of they agrEEmEnt or not agrEE with item s such as “I usually spend a lot of time using the KMS.”

Individual Performance

This construct was measured with eight item s, four of them were adapted from RobEy (1993) and others were adapted from Borman and Motowidlo (1993). The former reflects standard concerns associated with the efficiency and quality of work as well as the EffEctivEnEss of individual, and the latter is about task performance.

4.3 Procedure

After the proposed quEstionnairE was completed in its design, it was in the first place reviewed and fine-tuned by an expert panel of two MIS professors from the National University of Science and TEchnology (NUST), Pakistan an EmployEE from IBM. The formal questionnaire was then compiled after removing unclear wording and accurate scales.

4.4 Data Collection

After completing all the data collection, data cleaning was deployed necessarily. Reckless responses with significantly illogic answers were merely dumped. Finally, 168 complete and valid questionnaires were rEciEvEd for data analysis (after deleting 15 ExtrEmE cases in the data scrEEning process). Some of the questions described in a negative expression were also converted as positive by reversing their score (in the 5-point scale) when being EntErEd into MS-Excel. In the measurement the participators' demographic data was also collected. Sample demographic gives a clear idea and an overview of all qualified respondents. From these samples, the gender ratio is about 77% male and 23% female, showing that male is majority. More than half of participants are below 40 years old. About the level of education, all of respondents are above university. The majority of respondents' academic backgrounds are management (47%), while 27% are EnginEEring or so. A significant concentration in the category of industry they engage in are TEchnology (31%) and manufacturing (30%) industry.

4.4 Data Analysis

The results are discussed in the following sequence: TEchnology-Task compatibility, TEchnology-People compatibility, TEchnology-Organization compatibility part, and then usage to performance part and compatibility to performance part. First of all, when the Research model was designed, a link was proposed from tEchnology-task compatibility to KMS usage. It was found that the effect of tEchnology-task compatibility to KMS usage is positive and quite strong. Additionally, the relationship between tEchnology-People compatibility and KMS usage was significant. The path from tEchnology-organization compatibility to KMS usage was also positive and significant. But it has the least impact on KMS usage. Finally, the link between KMS usage and performance, while in the direction predicted by the model, was significant as well. Furthermore, the path from tEchnology-People compatibility to individual performance is positive and significant. But the links between tEchnology-task compatibility and individual performance, and the links between tEchnology-organization compatibility and individual performance, while in the direction predicted by the model, were not significant. In sum, the results are summarized in the table below.

Hypothesis Result

H1: The TEchnology-Task compatibility positively influences use of the KMS Supported

H2: The TEchnology-People compatibility positively influences use of the KMS Supported

H3: The TEchnology-organization compatibility positively influences use of the KMS. Supported

H4: KMS use will increase the individual performance. Supported

H5: The TEchnology-Task compatibility positively influences the individual's performance. Not Supported

H6: The TEchnology-People compatibility positively influences the individual's performance. Not Supported

H7: The TEchnology-organization compatibility positively influences the individual's performance Not Supported

Chapter 5 Discussion and Implication

5.1 Discussion

This study proposes and tests a model for the factors of compatibility impacts of knowledge management systems on tEchnology use and performance. In general, the results are encouraging and providing support for the main objective of the study. The major objective was related to the development of a fresh perspective on the notion of compatibility both in terms of dimensionality as well as the structure of the construct. Based on the theoretical definition of compatibility, thrEE distinct aspects of compatibility were proposed: TEchnology-Task compatibility, TEchnology-People compatibility, TEchnology-Organization compatibility.

Following of this study was to find empirical support for the theoretical consequents of these compatibilities. With no exception, the posited theoretical relationships between the thrEE kinds of compatibility and usage (both scope and intensity) were all supported. The TEchnology-Task compatibility has the most impact on usage and its path coefficient is higher than the TEchnology-People compatibility. The potential explanation for this circumstance may be that People are adaptable and always take advantage of learning to improve personal capability. Even if the KMS incompatible with User's prior ExpEriEncE, compatible with task nEEds, they may willingly use it to handle the hard task. In addition, the TEchnology-Organization compatibility has the least impact on usage. The reason for this result possibly that the organizational compatibility is a contextual factor for personal KMS use, so it indicates less influence than else.

In the theoretical development, it was hypothesized that TEchnology-Task compatibility would positively relate to usage (H1). The results support this hypothesized relationship and indicated that the fit between the task and the available knowledge management tEchnology is associated with the usage. The result is different from the initial EvidEncE of the causal link between TTF and utilization, which was ambiguous (Goodhue and Thompson, 1995).Our explanations of this outcome are from two viewpoints:

First, from the view of task characteristic, most Users of KMS are Knowledge workers. A creative knowledge worker can contribute to face the problems that nEEd new kinds of resolution, the situations that demand various knowledge resources. They also apply the KMS to achieve a work-related task, to deal with the sEmi-structurE issue and to cooperate or communicate with fellow workers. As the dEgrEE to which a job requires different activities and skills in carrying out the work, Use r has the demand to acquire the explicit knowledge for his/her job, and the KMS should have the capabilities to combine new sources of knowledge and just in time learning (Alavi &LEidnEr 2002), provide enough to mEEt User's business nEEds.

Second, unlike the original TTF model (Goodhue and Thompson, 1995), which was focusing on the general Information System (IS), herein the object is Knowledge Management System (KMS).

Through the integration of different systems that were individually designed and developed into an infrastructure (Cantin, 2004), KMS aim to enhance the capability of worker to manage and generate knowledge faster and more accurate in proposing an overall solution of task, and then they will count on KMS frequently. As postulated by hypotheses 2, the direct relationship between this compatibility and usage is significant in the current. This means that higher compatibility of KMS with one's ExpEriEncE or belief, in turn, results in more usage. And KMS usage would be expected to be less as tEchnology person compatibility decreases.

By far the majority of Research on compatibility has looked at its impact on adoption decision (Rogers 1983, Tornatzky and KlEin 1982) and attitudes (Agarwal and Karahanna 1998). Previous researches (Karahanna Et al. 2006, VEnkatEsh Et al. 2003) noted that as the compatibility of IT increase, attitude towards information system usage should more positive. This model directs the attention to another aspect of system usage.

Moreover, factors affecting information system usage are best investigated at the individual User's level (DELone and McLEan 1992, 2002). For example, greater consistency with prior ExpEriEncEs should facilitate the learning process and improve KMS Use. Thus, it may be that individuals who have Use d similar systems before or who have Use d systems that embodied similar types of functionality won't nEEd to spend extra time on adapting and additional learning than individuals who encounter such a system for the first time.

Finally, the relationship between tEchnology-organization compatibility and usage was also examined (H3). The result shows that the more compatibility of the KMS was with organization's culture and work practices the more usage it was revealed. Hence, organizational compatibility is as important as others compatibilities, to affect the usage of KMS. The point is consistent with Schultz and SlEvin's findings of the importance of organizational and technical validity for ensuring success in implementation. This outcome also supports Rogers's argument that innovations are more likely to be adopted when they are compatible with an organization's values, previously introduced ideas, and nEEds (Rogers, 1995).

Training can enhance use of KMS through building people's competence and confidence to try and to adopt it. When organization provides more training in system use, therefore, it will help end users overcome the fear of complexity of the KMS and improve the computer sElf-Efficacy of users is likely to be EffEctivE in gaining user acceptance (Amoako-Gyampah and Salam 2004).

Moreover, extrinsic rewards are important motivators for knowledge sharing in organizations (HubEr 1991; Pan and Scarbrough 1998). Organizations provide incentives or tie rewards (promotion and bonuses) to contributions as well as reuse (DavEnport Et al. 1998), then will increase the frequency of KMS usage.

In the performance part, the result provides that the use of KMS improves the performance of EmployEE indEEd. This finding sEEms coincidence with the prior argument that KMS can improve individuals' performance and productivity in terms of time and spEEd of the knowledge sharing process (MaiEr 2002). Furthermore, the tEchnology-People compatibility has dEEpEr effect on performance than other compatibilities. But there is something worth to be noticed. The proportion of variance explained by the relationship between usage and performance is relative low. There may be other variables that nEEd to be included to better explain performance.

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