PROBING FACTORS AFFECTING KNOWLEDGE SHARING ...

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Questionnaire was used as an instrument for data collection from 120 bloggers. .... their true intention. TRA also conceives that behavioral intention is best predictor of actual behavior. ..... Understanding Attitudes Towards Computer Use in the.
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PROBING FACTORS AFFECTING KNOWLEDGE SHARING BEHAVIOR OF PAKISTANI BLOGGERS Rabia Mushtaq Abida Abi. Ellahi [email protected] [email protected] International Islamic University Islamabad Pakistan ABSTRACT The objective of this study is to identify those factors that affect the knowledge sharing behavior of individuals in the context of blogs of Pakistan. A research model has been developed, which consists of six construct derived from three well known theories namely Innovation Diffusion Theory, Social Capital theory and Theory of Reasoned Action. This theoretical model was tested empirically by conducting web based survey. Questionnaire was used as an instrument for data collection from 120 bloggers. Partial Least Square technique was employed to test the model. Four out of five hypotheses were confirmed. This study confirmed that relative advantage, attitude and social interaction ties have significant influence on intention to share knowledge and intention to share knowledge is a predictor of actual knowledge behavior. This study has several implications for professional and academic institutions. Key Words: Blogs, Intention, Complexity, Relative advantage, Theory of Reasoned Actions, Social Capital Theory, Innovation Diffusion Theory. 1. INTRODUCTION Organizations acknowledge that knowledge is a valuable intangible asset for generating competitive advantage (Miller & Shamsie, 1996). Knowledge sharing is a key to success for any organization. It is the behavior of propagating the value able knowledge to other members of an organization as well as to whole community which can get benefit through it. All learning organizations get help of knowledge management for sharing of knowledge. It creates linkages among employees, customers and suppliers through sharing of information (Weathersby, 1999). A lot of factors affect knowledge sharing behavior of individuals (Ryu et al., 2003; Cabrera & Cabrera, 2002). Importance of human behavioral factors cannot be ignored for the spreading of knowledge (Bollinger & Smith, 2001).In human behavior first factor for sharing of knowledge is individual attitude towards knowledge sharing. Individual attitude towards sharing of knowledge is too much important. Attitudes are related with feelings of individuals. Sometime individuals are not willing to share the knowledge due to feelings of insecurity. They feel fear from the loss of superiority and knowledge ownership after sharing their distinctive ideas with others (Hislop, 2003; Yang, 2008). This unwillingness to share knowledge is natural human affinity (Davenport & Prusak, 1998). It means that transferring individuals’ knowledge into valuable organizational knowledge is not a simple phenomenon and contains many challenges (Ryu et al., 2003). Therefore, it is vital to recognize those factors that promote or limit knowledge sharing behavior. Blogs, wiki and podcasts are continuously growing and popular information technology applications. The difference between these are that blogs are text-based log someone writes and updates, sort of like a web journal, whereas wiki is a web site anyone can edit, add to, and update, like Wikipedia and podcast are an online audio or video broadcast people can subscribe to or watch on a web site (usually hosted from a blog). According to Merriam-Webster dictionary (2010) blog “a short for Weblog is a Web site that contains an The Electronic Journal on Information Systems in Developing Countries http://www.ejisdc.org

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online journal with reflections, comments and often hyperlinks provided by the writer.” Most of businesses have started using blogs as a medium to prop up their products and services. Individuals use blogs as the source of getting relevant information and use it as a platform for sharing knowledge (Mohita, 2010). Blogs are renamed as social software which can be employed for social networking as well as sharing of different information. Bloggers can use blogs for collecting ideas and for revealing in-progress activities or works (Avram, 2006). These blogs can be used as tool for creating, organizing and sharing knowledge as well as developing personal knowledge management skills (Pettenati et al., 2007). As blogs are a facilitating medium for quick communication of ideas, sharing of knowledge and dispersions of information to a wide number of readers. Thus, it is not logical for companies to ignore such potential channel. By keeping this view, many companies such as Google and Microsoft etc use blogs as knowledge and information sharing tool both internally and externally (Hsu & Lin, 2008). It is assumed that bloggers voluntarily publish their knowledge in blogs (Karimi & Poo, 2009); however it is not uncommon that bloggers are also affected by certain internal and external factors that limit or encourage them to share knowledge in blogs. There are a large number of researches on knowledge management phenomena but few have studied knowledge sharing and among those few studies, limited studies have investigated knowledge sharing factors in context of blogs. Present study investigates the influence of certain factors on online knowledge sharing via blogs. These factors have different theoretical basis. The objective of this study is to identify those factors that encourage or obstruct the knowledge sharing behaviors of individuals in context of blogs. This study extracted factors from three different theories namely Innovation Diffusion Theory, Social Capital Theory and Theory of Reasoned Action. Two research questions of this research are: 1. What are the factors that support or obstruct knowledge sharing behaviors of users in blogs? 2. Is intention to share knowledge shapes the actual behavior of knowledge sharing in context of blogs or not? The scheme of study is as follow. First, on the basis of theoretical framework, hypotheses are developed and a theoretical model is derived. Second, hypotheses as well as model as a whole is tested to give answer about above certain questions, which are described in the third section of this paper. Finally, conclusion, implication of studies, limitations and future research direction are discussed that draw conclusion on the basis of those results. 2. THEORETICAL BACKGROUND AND HYPOTHESES The propagation of network access has given smoothness to the progress of virtual communities. Virtual communities’ access is not limited to only one community. Individuals’ participation in professional virtual communities is increasing for getting knowledge to solve various problems. All the organizations have recognized that knowledge is an essential for getting competitive advantage (Teo et al., 2003; Hof, Browder, 1997). Thus, without valuable knowledge, virtual communities’ value will be diminished. Virtual communities are playing vital role in society due to their rapid access to valuable knowledge. Community members share the knowledge to get known how about the different sectors of society (Chiu et al., 2006). Different factors influence to virtual communities. These factors facilitate the knowledge sharing among the community members. Following factors can be considered as main source for actual knowledge sharing. These factors come under the umbrella of different theories but they support the environment of virtual communities knowledge sharing.

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2.1 Attitudinal Factors Impact on Actual Knowledge Sharing Fishbein and Ajzen (1975) gave the best attitudinal theory, which is the theory of reasoned action. Specifically, it focuses on the link between attitude and intention, and between intention and actual behavior. A major contribution of the theory of reasoned action is the specificity of attitudes and intentions to match with actual behavior (Bobbitt & Dabholkar, 2001). Most research in the area of technology has focused on the antecedents of technology adoption. What is the rate of adaptation of technology by individuals? With how much intensity people actual adopt it and use it? (Gatignon & Robertson, 1985).Attitude is always an antecedent of adaptation (Bobbitt& Dabholkar, 2001). For sharing of knowledge, employees need a positive attitude to behave in certain direction. Usually individuals don’t give importance for sharing of knowledge (O’Dell & Grayson, 1998). People don’t share due to feelings of insecurity. As insecurity is related with employees’ feelings, may be they considered that by sharing the knowledge they will lose their opportunities or they have no such caliber to share the knowledge. Thus, these different factors may impede the intention towards sharing of knowledge (Szulanski, 1996). Sometimes individuals are not willing to share their knowledge when they have no good feelings from learning experiences (Cameron, 2002). Chatzoglou (2009) highlights that there is the requirement of creating a climate that would help individuals to develop a more favorable attitude toward knowledge sharing with their true intention. TRA also conceives that behavioral intention is best predictor of actual behavior. Behavioral intention can be measured an individual’s potency of intention to carry out a certain behavior ((Ajzen, 1991). Behavioral intention contains motivational aspects affecting a particular behavior and it is a person’s intention to perform or not perform a certain behavior. Consequently, intention to share knowledge in blogs is predictor of actual knowledge sharing (Kuo & Young, 2008) Thus, it can be hypothesized that Hypothesis 1: Intention to knowledge sharing has a positive effect on actual knowledge sharing. Hypothesis 2: Attitude toward knowledge sharing will be positively related to actual knowledge sharing. 2.2 Innovation Diffusion Theory There can be different sources of getting actual knowledge. Now question is why only virtual communities should be the source of sharing. For answering this question, innovation diffusion theory can be used who support to the use of virtual communities. This theory addresses the rate of adoption for innovations by the member of society. The relative speed with which members of a social system adopt an innovation depends on the different factors (Rogers, 1962). Some characteristics of innovation facilitate to the use of new object. These characteristics are categorized into five sub characteristics which are relative advantage (improvement of innovation over the previous method), compatibility (extent of comfort level given by that innovation in individual life), complexity (extent of difficulty level), trialability (experimental ease for early adopters) and observability (visibility level of innovation) (Rogers 1962).Two sub characteristics of innovation are address in this study as they are more relevant. They are relative advantage and complexity. 2.2.1 Complexity. Complexity occurs when an innovation is difficult to understand or use then individuals in all possibility will have inadequate knowledge, skill, and experience to use it. Thus, in these circumstances adoption rate will be slow down (Robertson, 1971; Rogers, 1995; Gatignon & Robertson, 1985). When the individuals have learning behavior The Electronic Journal on Information Systems in Developing Countries http://www.ejisdc.org

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then they will try to use new thing and share it with others after learning it (Wilton & Pessemier, 1981). Intention is a very accurate variable for predicting behavior. When the individuals have intention to learn something new and feel easy to use it then they will actual enhance the sharing of their knowledge (Ajzen et al., 1985). 2.2.2 Relative advantage. Relative advantage is the degree to which an innovation is superior from previous change or not (Rogers, 1995). Target group must consider to new improvement better and truly advantageous from societal economic and technological perspective (Chakravarty & Dubinsky, 2005). When the use of virtual communities is intended advantageous then actual knowledge sharing will take place. On the basis of above mentioned facts, it can be hypothesized: Hypothesis 3: Perception of complexity of particular technology use for knowledge sharing will be positively related to actual knowledge sharing. Hypothesis 4: Perceived relative advantage of particular technology use for knowledge sharing will be positively related to actual knowledge sharing. 2.2.3 Social Interaction Ties Social Capital Theory belief is to make strong social relationships among people to enhance their productivity and get other advantages (Coleman, 1988). Social capital consists of three distinct dimensions: structural dimension refers to the overall pattern of connections between stakeholders, relational dimension refers to the kind of personal relationships people have developed with each other through a history of interactions and cognitive dimension refers to those resources providing shared representation, interpretations, and systems of shared meanings among the concerned parties (Nahapiet & Ghoshal, 1998). Virtual organizations are different from normal organizational structure. In virtual organizations or in virtual communities; individuals are connected through online communication. Social capital developed in virtual communities is strong support to encourage individuals to facilitate complex knowledge sharing process, and then share valuable knowledge among stakeholders (Nahapiet & Ghoshal, 1998). Well educated and knowledge individuals are asset for every community. They become the source of competitive advantage for firms. Especially for knowledge sharing, intensive firm bond of these intellectuals is necessary. When the social ties among them become strong, they will be able to share the knowledge on actual basis (Alvesson, 2000). People who prefer to use virtual communities are not only getting information but they also try to create link with one another. Social capital theory explains this issue what is the reason of knowledge sharing among communities. Knowledge sharing is actually a behavior which is exhibit by the member of society (Alavi & Leidner, 2001). Majority of the people interact with one another to resolve their problems and share their expertise. Trough frequent interaction of people a strong social relation between them is appeared in the form of social ties. These social ties will enhance the relationship of trust, beliefs and respect with member of communities along with sharing of useful knowledge (Semin & Smith, 2002). Nahapiet and Ghoshal (1998) argued that social network ties influence both access to parties of community to exchange and share the actual and valuable knowledge. Therefore, hypotheses will be built on this pattern: Hypothesis 5: Social interaction ties have a positive effect on intention to share knowledge.

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Innovation diffusion theory Dimensions

Relative Advantage H4

Perceived Complexity

Theory of Planned Behavior Dimensions H3

H2

Attitude towards learning Social Capital Theory Dimension

Intention to share knowledge

H1

Actual Knowledge Sharing

H5

Social interaction ties

Fig 1: Proposed Theoretical Model with Hypotheses 3. METHOD The purpose of this study was to understand the user's attitudes and behavioral pattern towards knowledge sharing in context of blogs. Individuals who frequently use blogs were focus group of this study. Web based survey design was selected for this research. As survey increases the generalizability of the results because researchers cannot direct the state of respondents (Yalcinkaya, 2007); therefore it was chosen for this study. 3.1 Instrument Development Questionnaire was used as an instrument for data collection. This questionnaire was confirmed with the previous researches (e.g. Lin, 2007; Kuo & Young, 2008). In Pakistan, English is the official language of correspondence as well as medium of instruction. Therefore, in the questionnaires all the questions were written in English language. In Pakistan usually, researchers used questionnaires in English (Raja & Johns, 2010). Self-report questionnaire was used for the measure. All variables were rated on 5 point Likert scale. Responses were ranged from 1 depicts “strongly disagree”, 5 “strongly agree”. Questionnaire comprises of two parts. Part first was introductory section which contained information about bloggers’ skills, frequency of use and purpose of using blogs. Part two consisted of questions to measure theoretical constructs. 3.2 Sample Pakistani Bloggers were the target population of this study. In this research individual user of blogs was unit of analysis. Purposive sampling technique was the sampling frame used in this research. This purposive sampling presents the advantages of choosing sample according to specific characteristics and situations (Ellahi & Manarvi, 2010). Simple random sampling technique was used to choose sample from purposive sampling frame. The logic of purposive sampling method was to include those subjects who actually use blogs for knowledge sharing The Electronic Journal on Information Systems in Developing Countries http://www.ejisdc.org

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by having understanding of blogs. The original sample size for this study was 200 but only 120 out of 200 favorable responses were obtained. Researchers have supported1 this sample size. The web based questionnaires survey was distributed among the participants because bloggers were accessed through internet and along with it web-surveys were relatively fast, in-expensive and efficient method for data collection (Snow & Thomas, 2007). Similarly response rate of web-based questionnaires is considerably higher (Kiernan et al., 2005) rather than in paper-based surveys. The participants were assured that their responses would not be revealed other than academic research purpose. After carrying out process of collecting data, two statistical software - SPSS 17 and Smart-PLS - were used to conduct the empirical analyses. 4. RESULTS 4.1 Background Information The background information about bloggers’ skill, frequency of using blogs and purpose of using blogs is given in Table1. According to frequency results majority of (45.8+45%) respondents use blogs for knowledge collection and knowledge distribution. Only 9.2% use blogs just time killing. Similarly majority of (77.5%) respondents use blogs daily. The statistics presented in Table 2 also show that 65% of respondents considered themselves as having excellent level of skill for operating blogs. Only 6% have poor level of skill and 35% have good level of skill. Table 1. Blog Use of Respondents Category Options To collect knowledge Purpose of Using Blogs To distribute Knowledge To Pass time Daily Frequency of Use Weekly Monthly Perceived Level of Excellent Skill Good Poor Total

Frequency 55 54

Percentage 45.8 45

11 93 23 4 78 36 6 120

9.2 77.5 19.2 3.3 65 35 5 100

Reliability analysis depicts the internal consistency of scale items. It is used to ensure that scale used is producing consistent results over times. Cronbach’s alpha is widely and commonly used measure for reliability analysis. Its value range is between 0 and 12. The value closer to 1.0 confirms significant reliability of scale. Table 2 shows Cronbach’s alpha values. In light of previous researches and on basis of existing theories, six constructs were studies included: actual knowledge sharing, intention to knowledge sharing, attitude towards knowledge sharing, social interaction ties, perceived complexity and relative advantage. Each construct was indicated with sign, such as actual knowledge sharing was denoted with AKS such as AKSi, AKSii and AKSiii. 1

Bartlett et al. (2001) pointed out that if in a study, factor analysis and regression is planned then sample size should not be less than 100 observations. 2

George and Mallery (2003) provided the following rules of thumb: Cronbach alpha = “> 0.9 =Excellent, > 0.8 = Good, > 0.7 = Acceptable, > 0.6 = Questionable, > 0.5 – Poor, and < 0.5= Unacceptable” The Electronic Journal on Information Systems in Developing Countries http://www.ejisdc.org

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Table 2 .Reliability Analysis of Constructs Cronbach Alpha

0.937

0.922

0.912

0.902

0.846

0.907

Items

Statements

Actual Knowledge sharing was measured by three item scale adopted from study of van den Hooff and de Leeuw van Weenen (2004) AKSi When I learn new useful knowledge, I share it on blogs AKSii Knowledge sharing in blogs is considered normal AKSiii When blogs members learn something new, they share it on blog Intention to share knowledge was measured by three item scale adopted from study of Kuo and Young (2008) IKSi I would like to use blogs for knowledge sharing since it help me to collect and share knowledge easily IKSii I will continuously use blogs for knowledge sharing IKSiii I intend to share knowledge on blogs Attitude towards knowledge sharing was measured by three item scale adopted from study of Kuo and Young (2008) AKSi Using blogs for knowledge sharing is a beneficial idea AKSii I enjoy to share knowledge on blogs AKSiii Using blogs for knowledge sharing is valuable Social interaction ties was measured by three item scale adopted from study of Chiu, Hsu and Wang (2006) SITi I spend a lot of time interacting with some members in blogs SITii I maintain close social relationships with some members in blogs SITiii I have frequent communication with some members in blogs Perceived complexity was measured by three item scale adopted from study of Cobanoglu (2006) PCi Using blogs for knowledge sharing takes up too much of time PCii When I share knowledge, I find difficult to post it in blogs PCiii To share knowledge on blogs requires a lot of mental effort. Relative advantage was measured by three item scale adopted from study of Selamat et al. 2009) Rai Blogs facilitates the rapid sharing of knowledge RAii A blog is more than a simple place for knowledge sharing RAiii Blogs promote interaction among members for knowledge sharing

4.2 Results of Structure Model In order to evaluate the theoretical relationships among relevant constructs, hypotheses testing and factor analysis were conducted using Partial least squares (PLS) technique. This PLS analysis was conducted using Smart-PLS software. PLS works same like structural equation models. PLS was preferred in this study because it is best for dealing issues of small sample sizes, missing values and multicollinearity (Pirouz, 2006). Figure 2 shows the results of analysis of structure model. It includes value of R2,factor loadings and path coefficients. 4.2.1 Factor Analysis The construct validity was confirmed by computation of loading values of items on their respective constructs as shown in figure 2. The model estimation result show values of factor The Electronic Journal on Information Systems in Developing Countries http://www.ejisdc.org

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loadings on their respective items which were assigned to their corresponding indicator. It is clear from figure that factor loading values are in suggested range, confirming validity of constructs in model. The values of factor loadings show the strength of relation of items with their respective constructs. The items giving higher loadings that are near to 1 depict strong relation with constructs on which their loadings are computed. The highest factor loading value in this model was of AKSiii (0.95) and the lowest value was of AKSi (0.69). Thus, all factor loadings are significant and fairly high. 4.2.2 Path Analysis: Along with factor loadings, path coefficients values are also shown in figure 2. The values for path coefficients are showing hypotheses support for four out of five hypotheses. The R2 values of dependent variables model were 0.87 and 0.75, show explanatory power of model. In this regard model depicted 87% variance in intention to knowledge sharing and 75% variance in actual knowledge sharing behavior. The value range of standardized coefficients of paths was ranged from 0.16 to 0.90 as shown in figure 2. These values shows that relative advantage of blogs for knowledge sharing, attitude toward knowledge sharing and social interaction ties significantly effect intention to knowledge sharing and intention to knowledge sharing significantly effects actual knowledge sharing. However, the value of perceived complexity was showing positive relation with intention to knowledge sharing. This result was inconsistent with suggested hypothesis. 4.3 Hypotheses Testing Evaluation Hypothesis 1: Intention to knowledge sharing has a positive effect on actual knowledge sharing. The statistical results show that intention to knowledge sharing was positively related with actual knowledge sharing behavior in blogs. The highly significant path coefficient value of 0.90 (sig P