8/2/2019 4525d796

1/10

Understanding the Effect of Innovative Climate and Interaction Involvement

on User Intention to Share Knowledge

Eugenia Y. Huang Travis K. HuangDepartment of MIS Department of MIS

National Chengchi University National Chengchi University64, Sec. 2, ZhiNan Rd., Taipei, Taiwan, R.O.C. Department of IM

huang.eugenia@gmail.com Ling Tung University, Taichung, Taiwan, R.O.C.

+886-2-293873481 travisk.huang@gmail.com

AbstractEffective knowledge sharing among members has

rapidly become a competitive necessity fororganizations. Thus it is essential for organizationsto take steps to increase members willingness to

share knowledge, leading to desirable knowledgesharing behaviors. This study explores possiblefactors affecting users intention to share knowledgein the context of system analysis. In addition toinnovative climate, system analyst and usersinteraction involvement, which is viewed as animportant interpersonal communication competence,is proposed and further investigated, taking intoconsideration the links between innovative climate,interaction involvement and user intention to shareknowledge. Based on data collected from 182 studentanalysts and 182 users, the results indicate that

system analysts demonstrated higher levels ofinteraction involvement than users, where interactioninvolvement was measured by responsiveness,

perceptiveness, and attentiveness. In addition,innovative climate positively and directly influencesusers intention to share knowledge, while quite

surprisingly, users interaction involvementnegatively affects his/her intention to shareknowledge.

1. Introduction

Since knowledge is considered to be the mostimportant resource in any organization (Wernerfelt,

[1]), many systems have been applied to identify,create, represent, store and distribute knowledge fromindividual to individual. Examples of such systemsare electronic databases, network systems, knowledgemanagement systems, blogs and virtual communities.However, the initiatives proved far from satisfactory,and more socio-cognitive approaches have beenadopted to motivate individuals to share knowledge(Chow & Chan, [2]). Considering that effectiveknowledge sharing is a competitive necessity, it is

essential for organizations to take action to increasethe individuals willingness to share knowledge withother members, leading subsequently to expected

behaviors.Because intention has been treated as an

important and central construct in widely accepted

theories including the theory of reasoned action(TRA), theory of planned behavior (TPB),decomposed TPB (DTPB), technology acceptancemodel (TAM), and the unified theory of acceptanceand use of technology (UTAUT), theoretical

prediction of knowledge sharing intention and itscorresponding behavior has been examined. Forexample, Kuo and Young [3] proposed thatindividuals knowledge sharing intentions candirectly affect their actual knowledge sharing

practices. The antecedents of intention embraced bythe theories have also been studied. Ryu, Ho and Han[4], for example, reported that an individualsintention to share knowledge is positively influenced

by attitudes, subjective norms and perceivedbehavioral control. Hsu and Lin [5] showed thatcommunity identification and attitude toward blogusage significantly influenced a blog participantsintention to use blogs to share knowledge. Kolekofskiand Heminger [6] showed that a users beliefs andattitudes affected his or her intention to shareknowledge in an organizational setting. Althoughthese antecedents jointly explain most of the variancein intention to share knowledge, other determinantsremain unknown and need to be identified.

In the information systems (IS) field, system

design and implementation is a continuousknowledge-intensive process in which mostknowledge gathering interaction between systemsanalysts and users is observed (Huang & Huang, [7];Huang & Huang, [8]). During the development ofany information system, experienced designersrecognize that the user may not understand the truenature of the information system requirements (Walz,Elam & Curtis, [9]). Systems analysts interact with

2012 45th Hawaii International Conference on System Sciences

978-0-7695-4525-7/12 $26.00 2012 IEEE

DOI 10.1109/HICSS.2012.616

3737

2012 45th Hawaii International Conference on System Sciences

978-0-7695-4525-7/12 $26.00 2012 IEEE

DOI 10.1109/HICSS.2012.616

3796

8/2/2019 4525d796

2/10

users to identify relevant system requirements, tryingtheir best to comprehend the users tasks. In thisregard, the users intention to share their knowledgeis critical for systems analysts so they can produce aset of feasible solutions. Therefore, the studies havefocused on the intention of users, rather than on thesystems analysts, during the process of requirementidentification and system design. Organizations needto create an appropriate culture and climate to foster

positive interactions and enhance participantsintention to share knowledge.

It is well recognized that user involvement insystems development plays an important role inimproving the quality of IS (Robey & Farrow, [10];Baroudi, Olson & Ives, [11]). Tait and Vessey [12]summarized prior research on the effect of userinvolvement on system success and reported that userinvolvement has a positive influence on thesuccessful introduction of an information system intoan organization. However, in some cases, userinvolvement is inappropriate and may not beimportant for improving the quality of systems thatare highly structured and well-defined, systems thatrequire considerable technical expertise, or systemsthat are invisible or unimportant to users (Ives &Olson, [13]).

Although user involvement is crucial to systemsanalysis, users seldom treat their input function as a

priority compared with other tasks at hand. In otherwords, they do not get very involved in providinginformation about system requirements. In addition,opportunities for user involvement are often limitedand controlled by the systems analysts (Doll &

Torkzadeh, [14]), who play an important role ineliciting appropriate requirements from end-users.

Nevertheless, very few IS studies have investigatedthe influence of systems analyst involvement (Jones& Harrison, [15]).

After reviewing studies related to involvement,Ives and Olson [13] summarized that participantsmay be involved in system- or non-system-specificactivities or events to which they can respondrelatively objectively. Therefore, the construct ofinvolvement has a wide range of dimensions andvaries from research topic to research topic. Becausegood interview skills and strategies to encourage user

participation require a higher level of involvement bysystems analysts, systems analysts should be activelyinvolved in soliciting rather than passively waitingfor user requirements. Software designers arehistorically valued in terms of both technical andcommunication skills; thus, organizations should payattention to helping them develop communicationskills in more depth (Walz, Elam, & Curtis, [9]).Participant involvement in interpersonal

communication, which is similar to the concept ofinteraction involvement (Cegala, [16]), is critical toidentifying effective system requirements. Thus, thestudy described herein aimed to help fill the researchgap by focusing on the interaction involvement of

both systems analysts and users, rather than that ofusers only.

The main argument of this study is that aninnovative climate influences interaction involvement,and that the antecedents of intention, includinginnovative climate and interaction involvement as

perceived by both users and systems analysts, lead todifferent levels of user intention to share knowledge.Hence, the following questions can be posed. What isthe relationship between innovative climate and theusers intention to share knowledge? What is therelationship between interaction involvement, as

perceived by users and systems analysts, and theusers intention to share knowledge? What is therelationship between innovative climate andinteraction involvement as perceived by users andsystems analysts?

This study thus emphasizes both antecedents andtheir relationships rather than merely the possibleantecedents of intention, in an attempt to clarify howthey affect the users intention to share knowledge. Itis believed that the findings will not only augment thetheory of knowledge management but also provideinsight into the causal chain of an individualsintention to share knowledge.

2. Literature review

Much IS development research has focused on theeffectiveness of participant involvement resultingfrom acquisition of appropriate system requirementsand leading to IS success. Less effort has beendevoted to examining the effects of interactioninvolvement, particularly of interpersonalcommunication. Yet the link between climate andinteraction involvement mandates that the intentionto share knowledge plays an important role inknowledge management. Thus, we investigated

possible antecedents of intention to share knowledge,including innovative climate and interactioninvolvement.

2.1. Innovative climateThe organizational climate consists of a set of

attributes perceived about a particular organization,and it is based on the interactions betweenorganization members (Hellriegel & Slocum, Jr., [17];Downey, Hellriegel & Slocum, Jr., [18]).Organizational climate is known to be a critical

driver of knowledge sharing (Constant, Kiesler &

37383797

8/2/2019 4525d796

3/10

Sproull, [19]; Buckman, [20]). Ali, Pascoe andWarne [21] stated that the communication climateaffects the generation, existence and distribution oforganizational knowledge. Van den Hooff and deRidder [22] found that a constructive climatesignificantly and positively influences knowledgesharing, including knowledge donating andknowledge collecting. In addition, cultural factorssuch as group conformity (Bang, Ellinger,Hadimarcou & Traichal, [23]), fairness, affiliationand innovativeness (Bock, Zmud, Kim & Lee, [24])have been shown to directly influence intention.

On the basis of published reports and interviewsregarding several leading knowledge managementinitiatives within their organizations, Bock, Zmud,Kim and Lee [24] identified three aspects oforganizational climate as being particularlyconducive to the intention to share knowledge,namely, fairness, affiliation and innovativeness.Fairness reflects the perception that organizational

practices associated with knowledge sharing areequitable, whereas affiliation is defined as the senseof togetherness that leads to a willingness to help oneanother. Innovativeness reflects the perception thatchange and creativity are actively encouraged andrewarded. Therefore, individuals in innovativecontexts are more likely to share new and creativeideas with each other. In the authors study, theorganizational climate of Korean firms was furthershown to affect individuals intentions to shareknowledge directly.

In a study, interaction between the systemsanalysts and users is short-term. Organizational

practices and policies will not have a major effect onthose interactions. Thus, the climate in terms offairness and affiliation cannot be clearly observed insuch a study. In addition, the process of systemsanalysis provides an opportunity for business processreengineering. The systems analyst should beinducing the conversation, and both the user andanalyst should be actively engaged. While the userdescribes existing business procedures, the systemsanalyst will queries the user about feasible solutionsin order to acquire proper requirement of informationsystem. Both the user and the systems analyst notonly share but also assess and internalize the

knowledge in order to come up with solution.Effective ongoing of interviews relies on theinnovative climate. Thus, innovation is needed on the

part of the systems analyst and must be appreciatedby the user. Given our particular research context,our study only focused on innovative climate andexpected innovative climate to directly influence theusers intention to share knowledge. This led to thefollowing hypothesis:

Hypothesis 1: Innovative climate is positively relatedto the users intention to share knowledge.

2.2. Interaction involvementInvolvement has been defined as a persons

perceived relevance of the object based on inherent

needs, values and interests (Engle & Blackwell, [25]).Barki and Hartwick [26] further stated thatinvolvement refers to an individuals subjective

psychological state reflecting the importance andpersonal relevance of a topic, product or job. In theIS field, most studies investigate user involvement,which is defined as a series of behaviors performed

by potential users in the system development process(Barki & Hartwick, [26]; Ives & Olson, [13]; Tait &Vessey, [12]). However, user involvement representsonly one side of the equation. The other side of theequation is involvement of the systems analysts.Because a participants perception of his or herinvolvement in an IS project has been found toinfluence the performance of the system (Doll &Torkzadeh, [14]), the involvement of both thesystems analysts and the users is critical to the qualityof the project.

As the study is limited to the context of systemsanalysis, the unique character of short-terminteraction between the systems analyst and the usermust be taken into consideration. Only when systemsanalysts get involved more deeply are they morelikely to comprehend the system requirements, toevaluate the data provided by users, and to providequick and appropriate feedback. Thus, the studyfocused on the involvement that is fundamental to

interpersonal communication, so-called interactioninvolvement (Cegala, [16]; Cegala, Savage, Brunner,& Conrad, [27]), which requires participantsconscious attention to both internal and externalmatters in any interpersonal communication event. Infact, interaction involvement is regarded as anindicator of competence in interpersonalcommunication (Cegala, [16]), which is critical tosystems analysis.

Cegala [16] developed the InteractionInvolvement Scale (IIS) to measure individualsinteraction involvement, which comprises threerelated factors: responsiveness, perceptiveness and

attentiveness. Responsiveness indicates certaintyabout how to respond in social situations, whereas

perceptiveness refers to general sensitivity to themeaning of others behavior. Attentiveness describesthe degree to which individuals pay heed to theinterlocutors.

Involvement varies in degree from one participantto another. Ives and Olson [13] classified degree ofinvolvement into six levels: no involvement,

37393798

8/2/2019 4525d796

4/10

symbolic involvement, involvement by advice,involvement by weak control, involvement bydoing and involvement by strong control. Higher-level involvement leads to more successful systemimplementation. Ives and Olson also recognized thatvarious levels of involvement in a system

development project, such as that of primary users orthat of secondary users, will have different effects onthe project outcome. Thus, this study proposed thatsystems analysts and users not only demonstratedifferent levels of interaction involvement but alsocontribute to different aspects of the outcome.Because systems analysts play an active role ininterviewing the users, their level of interactioninvolvement is likely to be much higher than that ofthe users.

2.3. Involvement and intentionThe relationship between involvement and

intention has been investigated in a number of fields.For instance, psychology research has shown that ahigh level of involvement increases a personsmotivation to handle issue-relevant information andto act in accordance with his or her attitudes (Leippe& Elkin, [28]; Sivacek & Carno, [29]). In themarketing field, a high level of involvement has beenshown to lead to greater interest in gatheringextensive product information and better recall of theinformation shown in advertisements (Gardner,Mitchell & Russo, [30]; Zaichowsky, [31]; Laurent &Kapferer, [32]). In addition, highly involvedconsumers, in contrast to less involved consumers,demonstrate a higher degree of the purchasingintention by reading on-line consumer reviews (Park,Lee, & Han, [33]). Organizational behavior studieshave revealed that the level of job involvement has adirect effect on the level of individual motivation(Lawler & Hall, [34]).

In the IS field, differences in the level ofinvolvement have been shown to cause significantdifferences in expectations of success (Kahai, Solieri& Felo, [35]). When the information system isimportant and personally relevant to both users andsystems analysts, they are both more likely to beinvolved in system development (Barki & Hartwick,[26]; Robey & Farrow, [10]) and to try their best toclarify information needs and explain task procedures.Therefore, both the users and the systems analysts

perception of their own interaction involvement ispredicted to affect the intention to share knowledge.These findings suggest the following hypothesis:

Hypothesis 2: The level of perceived interactioninvolvement is positively related to the usersintention to share knowledge.

Because each interaction involves a systemsanalyst and a user, Hypothesis 2 is expanded asfollows:

Hypothesis 2a: The system analysts perceived levelof interaction involvement is positively related to theusers intention to share knowledge.

Hypothesis 2b: The users perceived level ofinteraction involvement is positively related to theusers intention to share knowledge.

2.4. Climate and involvementThe relationship between organizational climate

and employees involvement has been clarified, but

few studies in the IS field have investigated the linkbetween climate and participant involvement. Whiteand Ruh [36] posited that employee involvement isaffected by organizational culture and furtherascertained that organizational climate is an

Figure 1. Conceptual model.

Users Intention

to Share Knowledge

H2bUsers

Interaction

Involvement

H1

Systems Analysts

Interaction

Involvement

Innovative

Climate

H2aH3a

H3b

37403799

8/2/2019 4525d796

5/10

important precursor to employee involvement.Shadur, Kienzle and Rodwell [37] proposed that aninnovative organizational climate is positively relatedto perceptions of participation in decision making,teamwork and communication. This notion is similarto the concept of interaction involvement formulatedin our study. Accordingly, Hypotheses 3 is as follows:

Hypothesis 3: Innovative climate is positivelyassociated with participants interaction involvement.

Because the systems analyst and user playdifferent roles as participants, Hypothesis 3 isexpanded as follows:

Hypothesis 3a: Innovative climate is positivelyassociated with the system analysts interactioninvolvement.

Hypothesis 3b: Innovative climate is positivelyassociated with the users interaction involvement.

With the belief that both innovative climate andinteraction involvement influence the participantswillingness to share knowledge, the proposed studyfurther focused on the users intention to shareknowledge and treated innovative climate andinteraction involvement as antecedents. Figure 1depicts the complete conceptual model. Note that themodel explores three antecedents of the usersintention to share knowledge by recognizing thatknowledge sharing involves collective action at itscore. As such, interaction involvement perceived by

both the user and systems analyst were taken asindependent variables in the study to measure theeffect of interaction involvement on the usersintention to share knowledge. In addition to the directeffects of the systems analysts interactioninvolvement, the users interaction involvement andinnovative climate, the indirect effect of innovativeclimate was also measured.

3. Research method

3.1. Subjects and procedure

One hundred and ninety college seniors majoringin IS from four sections of the same course,Information Systems Analysis and Design, wererecruited. One section was taught one semesterearlier than the other three sections. As a majorcourse requirement, student analysts were required toidentify IS requirements in various real businesscases. Each section was taught by the same instructor,using the same teaching materials and under the same

learning conditions. The course has been offered forthe past 2 consecutive years.

To investigate interaction in terms ofcollaboration between systems analysts and users inthe context of systems analysis, questionnaires wereadministered to 190 student systems analysts andtheir 190 respective users. Student analysts wereaware that they were involved in projects for whichoutput of the systems analysis would be measured,

but they were blinded to the research hypotheses.Each student analyst was assigned a real business

case, and during the semester each student analystscommunicated with a user from the business firm toascertain the system requirements. The main task waseither website design or IS planning. Students

project reports consisted of the system requirementsfor their respective firms. After the projects had beencompleted, the student analysts and the users wereasked for their perception of the innovative climate,interaction involvement and intention to shareknowledge during the interaction.

In our study, most of the real business cases areoffered by small-size companies, which are willing to

collaborate with student analysts for betterunderstanding their information system requirements.In fact, these companies are in need of IS outsourcing.After the interviews, the company received a freecopy of system analysis report detailing the ISsuggestions and cost evaluation. The companies were

benefited by such collaboration. For instance,although no real system was going to be developedimmediately afterwards, the company could refer tothe systems analysis report for further IS

implementation. When companies become moreknowledgeable, it will be much easier for them tochoose and identify qualified outsourcing vendorsand further to make better deals. Besides, thecompanies may consider hiring the student analystsor collaborating with them in the subsequent systemimplementation course scheduled for the second halfof their senior year to carry out the implementation.These students were not only familiar with the actualsystem planning but also well-trained in programcoding. They all have the abilities to act as specialistsfor IS implementation projects.

The data were collected by means of a survey,

and the research model shown in Figure 1 was testedby using the data pertaining to innovative climate,interaction involvement perceived by the systemsanalysts and users, and users intention to shareknowledge, and the hypotheses were examinedthrough structural equation modeling using thecollected data.

37413800

8/2/2019 4525d796

6/10

3.2. Measurement and data collectionThe independent variables in this study were

innovative climate and interaction involvement asperceived by the systems analysts and users. Thedependent variable was the users intention to shareknowledge. Measurement of each construct is furtherdescribed as follows:

Innovative climate. According to Bock, Zmud, Kimand Lees [24] definition of organizational climate,three aspects were identified. They areinnovativeness, fairness, and affiliation. Among them,innovativeness is most relevant to our study.Therefore, we adopted Bock, Zmud, Kim and Leesdefinition of innovativeness, and called it innovativeclimate, because the innovativeness refers to anaspect of climate. It is defined as the degree of

perception that change and creativity are activelyencouraged. We also used Bock, Zmud, Kim andLees measurement items of innovativeness tomeasure innovative climate. There are totally threeitems which focused mainly on measuring theinnovative climate perceived by the users and thestudents who played the role of systems analysts.

Cronbachsfor this three-item measure was .65.

Interaction involvement. Interaction involvement wasassessed by means of 18 items adopted from CegalasInteraction Involvement Scale (Cegala, [16]). Theseitems reflect three aspects of an individualscommunicative competence during the interpersonalcommunication process: attentiveness, perceptivenessand responsiveness. While attentiveness (6 items)focuses on hearing and observing, perceptiveness (4

items) indicates being aware of message meanings(Cegala, [16]). Responsiveness (8 items) refers to a

persons certainty about how to respond to othersduring a conversation (Cegala, Savage, Brunner, &

Conrad, [27]). Cronbachs for the three factors

was .725, .729 and .861, respectively.

Intention to share knowledge. Bock, Zmud, Kim andLee [24] developed a scale to assess the intention toshare knowledge. The items focused mainly onmeasuring the degree to which a user is willing toshare tacit knowledge (3 items) and explicit

knowledge (2 items). Cronbachs for this five-

item measure was .818.All respondents, including both users and systems

analysts, were asked to evaluate the significance ofthe measurement items using a Likert scale of 1-7,where 7 represented strongly agree, and 1represented strongly disagree.

4. Results

4.1. Data analysisOf the 190 students attending the class, 182

completed questionnaires as systems analysts, andtheir respective users also completed questionnaires,for a total of 364 completed questionnaires. Theresponse rate was 95.7%. Correlation between the

proposed variables was tested by means of Pearsonscorrelation coefficient, and differences in thosevariables between systems analysts and users weretested by paired-samples t-test.

Table 1. Correlation Between Variables

1 2 3 4 5

1. Intention to share knowledge 12. Innovative climate .569** 13. Attentiveness -.235** -.022 14. Responsiveness -.262** -.062 .792** 15. Perceptiveness -.252** -.081 .712** .770** 1

Note. *p< .05. **p User4. Responsiveness 5.954*** Systems Analyst > User5. Perceptiveness 3.769*** Systems Analyst > User

Note. *p< .05. **p

8/2/2019 4525d796

7/10

Table 1 shows Pearson correlation coefficients forthe study variables. Innovative climate was notsignificantly related to interaction involvement, i.e.,to attentiveness, responsiveness or perceptiveness(r=-.022, r=-.062; r=-.081; p>.05), whereasinnovative climate was positively related to intentionto share knowledge, which was negatively related toattentiveness, responsiveness and perceptiveness.

As shown in Table 2, all variables differedsignificantly between users and systems analystsexcept innovative climate. In other words, there wasno difference in the perceived innovative climates

between users and systems analysts during theirinteractions. In addition, in comparison to systemsanalysts, users demonstrated higher level of intentionto share knowledge and a lower level of interactioninvolvement, i.e., attentiveness, responsiveness and

perceptiveness. Therefore, as systems analysts playan active role in interviewing the users, their level ofinteraction involvement is proved to be significantlyhigher than that of the users.

4.2. Structure modelBy maximum likelihood estimation using AMOS,

the proposed model was assessed with the 182records of interaction, each of which was treated as aunit of analysis and consisted of data from onesystems analyst and one user. All calculations were

based on the covariance matrix of the variables. Fivecommon model-fit measures were used to assess the

models overall goodness of fit: the ratio of2 to

degrees of freedom (d.f.), goodness-of-fit (GFI),adjusted goodness-of-fit index (AGFI), comparativefit index (CFI), and root mean square error of

approximation (RMSEA).The results indicated that the proposed model (2

(30, N=182) = 2.643; GFI=.935, AGFI=.873,CFI=0.956, RMSEA=.095) had a much better fit than

the recommended values (2 (30, N=182)3; GFI

.90, AGFI .80, CFI .90, RMSEA .80).

Therefore, we used it to examine our hypotheses.None of the standardized path coefficients ofinnovative climate, shown in Figure 2, werestatistically significant, disconfirming Hypotheses 3aand 3b (=-.09, =-.06, p>.05), except for the pathrunning from innovative climate to users intention toshare knowledge, thereby supporting Hypotheses 1empirically. In addition, the relationship between thesystems analysts interaction involvement and theusers intention to share knowledge was notsignificant, disconfirming Hypothesis 2a (=.09,

p>.05). Although the users interaction involvementwas significantly related to the users intention to

share knowledge, the effect was negative rather thanpositive. Therefore, Hypothesis 2b was not supported.In summary, whereas innovative climate had a strong

positive effect on the users intention to shareknowledge, the users interaction involvement had anegative effect. The R2 value indicates that 41% ofthe variance in the users intention to shareknowledge was explained by innovative climate andthe users interaction involvement in the model.

5. Conclusion

5.1. Data analysis

The study described herein empirically examinedhow the interaction involvement of participants of

Figure 2. Standardized Path Coefficients of the proposed Model(Note: The dotted lines indicated not statistical significant)

Users Intention

to Share Knowledge(-.35 )

UsersInteraction

Involvement

(.51 )

Systems Analysts

Interaction

Involvement

Innovative

Climate

37433802

8/2/2019 4525d796

8/10

different roles (analyst and user) affects the usersintention to share knowledge. The systems analysts,in comparison to the users, demonstrated higherlevels of interaction involvement, includingresponsiveness, perceptiveness and attentiveness.Additionally, innovative climate positively influencesthe users intention to share knowledge. Yet,surprisingly, users interaction involvementnegatively affects his/her knowledge sharingintention, while the relationship between systemsanalysts interaction involvement and users intentionto share knowledge is not confirmed. It is indeed

puzzling that more interaction involvement leads tolower degree of users knowledge sharing intention.However, it is likely that exhibiting more interactioninvolvement is perhaps a good strategy to avoidactual knowledge sharing. If a user resists to

participating in requirement specification duringsystems analysis, then good communication skillsmay effectively lead to more interaction involvement,

but not necessarily the actual sharing intention. Thatis to say, if a user doesn't want to share knowledge,having him/her talk will not solve the problem, sincehe/she might just share what he/she really wants toshare, but maybe not his/her knowledge.

In addition, contrary to our expectation,innovative climate affects neither system analystsinteraction involvement nor users. A possibleexplanation is that the effect of innovative climate is

by nature long term effect which cannot easily beobserved in a short run.

In conclusion, a very important practicalimplication of our findings is the possibility of

increasing the users intention to share knowledge byenhancing the innovative climate during interactions

between systems analysts and users. The findings ofthis study contribute to theory building concerningthe effect of innovative climate and interactioninvolvement on knowledge sharing. By identifying a

possible antecedent, innovative climate, andanswering the hows and whys of the relationships

between innovative climate, interaction involvementand intention to share knowledge, results of the studycontribute substantially to development of the theory.

5.2. Limitation and future research

A few limitations should be noted. First, the studyfocused on the relationships between innovativeclimate, interaction involvement and intention toshare knowledge, but the relationships between other

climates and other involvements should beconsidered to fill the gap in the published research.Second, the research results may be linked to thecontext being investigated. Therefore, to confirmapplicability of the findings to other contexts, the

research model will need to be tested in varioussettings. Third, system analysts intention to shareknowledge may be of interest for furtherunderstanding of whether the knowledge sharingintention on one side (for example, analyst) affectsthat of the other side (for example, user). When this

potential interaction is investigated and the contextsof study categorized, a general conclusion can bedrawn with greater confidence.

In addition, the interactions between users andstudent analysts are nearly similar to the short-termIS outsourcing cases. In fact, most students are quitewell trained in information system development andwould be engaging in IS outsourcing projects rightafter graduation. Hence, their lack of experience isthe only concern. Although the context is not exactlya real case, studying numerous interaction casesduring system analysis phase probably is only asclosely as this study can get. Nevertheless, to applythe research findings in understanding systemanalysis, one does need to keep in mind this researchlimitation.

Finally, innovative climate was the only variablefound to positively affect users intention to shareknowledge, and it was not shown to affect

participants (both analyst and user) interactioninvolvement. This could be due to the definition ofinvolvement in the study. Future research may refineor expand the construct of involvement. Interactioninvolvement could be the tipping point for a series offuture studies in identifying antecedents of intentionto share knowledge.

Acknowledgments

This research is partially supported by NationalScience Council in Taiwan under project number

NSC 99-2410-H-004-135. The authors wish to thankProfessor Joshi, Professor Nissen, Dr. Cooper andthree anonymous reviewers for their helpful feedbackand suggestions.

References

[1] B. Wernerfelt, A resource-based view of the firm,Strategic Management Journal, 5, 1984, pp. 171-180.

[2] W. S. Chow and L. S. Chan, Social network, socialtrust and shared goals in organizational knowledge sharing,Information & Management, 45, 2008, pp. 458-465.

[3] F. Kuo and M. Young, Predicting knowledge sharingpractices through intention: A test of competing models,

Computers in Human Behavior, 24, 2008, pp. 2697-2722.

37443803

8/2/2019 4525d796

9/10

[4] S. Ryu, S. H. Ho, and I. Han, Knowledge sharing

behavior of physicians in hospitals, Expert Systems withApplications, 25, 2003, pp. 113-122.

[5] C. Hsu and J. C. Lin, Acceptance of blog usage: Theroles of technology acceptance, social influence and

knowledge sharing motivation, Information &

Management, 45, 2008, pp. 45-74.

[6] K. E. Kolekofski and A. R. Heminger, Beliefs andattitudes affecting intentions to share information in anorganizational setting, Information & Management, 40,2003, pp. 521-532.

[7] E. Y. Huang and T. K. Huang, Antecedents andoutcomes of boundary objects in knowledge interaction inthe context of software systems analysis, In Proceedingsof the 44nd Annual Hawaii International Conference on

System Sciences, 2011.

[8] T. K. Huang and E. Y. Huang, A max-min approach tothe output evaluation of knowledge interaction, In

Proceedings of the 42nd Annual Hawaii InternationalConference on System Sciences, 2009.

[9] D. B. Walz, J. J. Elam, and B. Curtis, Inside a softwaredesign team: Knowledge acquisition, sharing, andintegration, Communications of the ACM, 36(10), 1993,

pp. 63-77.

[10] D. Robey and D. Farrow, User involvement ininformation system development: A conflict model andempirical test, Management Science, 26(1), 1982, pp. 73-85.

[11] J. J. Baroudi, M. H. Olson, and B. Ives, An empirical

study of the impact of user involvement on system usageand information satisfaction, Communications of ACM,29(3), 1986, pp. 232-238.

[12] P. Tait and I. Vessey, The effect of user involvementon system success: A contingency approach, MISQuarterly, 12(1), 1988, pp. 91-108.

[13] B. Ives and M. H. Olson, User involvement and MISsuccess: A review of research, Management Science,30(5), 1984, pp. 586-603.

[14] W. J. Doll and G. Torkzadeh, A congruence constructof user involvement, Decision Sciences, 22(2), 1991, pp.443-453.

[15] M. C. Jones and A. W. Harrison, IS project team

performance: An empirical assessment, Information &Management, 31, 1996, pp. 57-65.

[16] D. J. Cegala, Interaction involvement: A cognitivedimension of communication competence,Communication Education, 30(2), 1981, pp. 109121.

[17] D. Hellriegel and J. W. JR. Slocum, Organizational

climate: Measures, research and contingencies, Academyof Management Journal, 17(2), 1974, pp. 255-280.

[18] H. K. Downey, D. Hellriegel, and J. W. JR. Slocum,Congruence between individual needs, organizational

climate, job satisfaction and performance, Academy of

Management Journal, 18(1), 1975, pp. 149-155.

[19] D. Constant, S. Kiesler, and L. Sproull, Whats mineis ours, or is it? A study of attitudes about informationsharing, Information Systems Research, 5(4), 1994, 400421.

[20] R. H. Buckman, Knowledge sharing at Buckmanlabs, Journal of Business Strategy, 19(1), 1998, pp. 11-15.

[21] I. M. Ali, C. Pascoe, and L. Warne, Interactions of

organizational culture and collaboration in working andlearning, Educational Technology & Society, 5(2), 2002,

pp. 60-68.

[22] B. Van den Hooff and J. A. de Ridder, Knowledgesharing in the context: The influence of organizational

commitment, communication climate and CMC use onknowledge sharing, Journal of Knowledge Management,8(6), 2004, pp. 117-130.

[23] H. Bang, A. E. Ellinger, J. Hadimarcou, and P. A.Traichal, Consumer concern, knowledge, belief and

attitude toward renewable energy: An application of theReasoned Action Theory, Psychology & Marketing, 17(6),2000, pp. 449-468.

[24] G. Bock, R. W. Zmud, Y. Kim, and J. Lee,Behavioral intention formation in knowledge sharing:

Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate, MISQuarterly, 29(1), 2005, pp. 87-111.

[25] J. F. Engel and R. D. Balckwell, Consumer behavior,

New York: Dryden Press, 1982.

[26] H. Barki and J. Hartwick, Rethinking the concept ofuser involvement, MIS Quarterly, 13(1), 1989, pp. 53-63.

[27] D. J. Cegala, G. T. Savage, C. C. Brunner, and A. B.Conrad, An elaboration of the meaning of interaction

involvement: Toward the development of a theoreticalconcept, Communication Monographs, 49(4), 1982, pp.229248.

[28] M. R. Leippe and R. A. Elkin, When motives clash:

Issue involvement and response involvement asdeterminants of persuasion, Journal of Personality and

Social Psychology, 52(2), 1987, pp. 269-278.

[29] J. Sivacek and W. D. Garno, When motives clash:Issue involvement and response involvement asdeterminants of persuasion, Journal of Personality and

Social Psychology, 43(2), 1882, pp. 210-221.

37453804

8/2/2019 4525d796

10/10

[30] M. P. Gardner, A. A. Mitchell, and J. E. Russo, Low

involvement strategies for processing advertisements,Journal of Advertising, 14 (2), 1985, pp. 4-12.

[31] J. L. Zaichowsky, Measuring the involvementconsturct. Journal of Consumer Research, 12(3), 1985, pp.341-352.

[32] G. Laurent and J. Kapferer, Measuring consumerinvolvement profiles, Journal of Marketing Research, 22,1985, pp. 41-53.

[33] D. Park, J. Lee, and I. de Han, The effect of on-line

consumer reviews on consumer purchasing intention: Themoderating role of involvement, International Journal ofElectronic Commerce, 11(4), 2007, pp. 125-148.

[34] E. E. Lawler and D. T. Hall, Relationship of jobcharacteristics to job involvement, satisfaction and intrinsic

motivation, Journal of Applied Psychology, 54(4), 1970,pp. 305-312.

[35] S. S. Kahai, S. A. Solieri, and A. J. Felo, Activeinvolvement, familiarity, framing, and the illusion of

control during decision support system use, DecisionSupport Systems, 23, 1998, pp. 133-148.

[36] J. K. White and R. A. Ruh, Effect of personal valueson the relationship between participation and job-attitudes,Administrative Science Quarterly, 18, 1973, pp. 506-514.

[37] M. A. Shadur, R. Kienzle, and J. J. Rodwell, Therelationship between organizational climate and employee

perceptions of involvement: The importance of support,Group & Organization Management, 24(4), 1999, pp. 479-503.

37463805