This study examines the status of knowledge sharing within and between banks operating in the United Arab Emirates (UAE). The status of knowledge sharing in UAE banks has been investigated in very few studies (Al-Musalli & Ismail, 2012; Alrawi & Elkhatib, 2009); Therefore, further research is deemed necessary to increase knowledge sharing practices in banks.
A Study on Knowledge Sharing Practices in the United Arab Emirates Banking Sector
Purpose of the Research
- Background of the Study
- Problem Statement
- Research Objectives, Questions, and Hypotheses
- Why was Knowledge Sharing Selected as a Subject for this Research?
- Why the Banking Context?
- Why the Context of the United Arab Emirates?
- Scope of the Dissertation
- Gaps of the study
- Outline of the Research Plan
- Research Structure
There is a significant relationship between profitability and ease of use of technology and KS in the banking sector. The main issue of the dissertation is to determine the current status of KS practices among employees working in the banking sector in the United Arab Emirates.
Literature Review
- Introduction and Purpose of the Literature Review Introduction
Then some theories and models of KS are given along with the constituents of KS success factors.
Purpose of the Literature Review
Knowledge in Organizations
Knowledge is currently viewed as the most important strategic resource in organizations, and the management of this knowledge is critical to organizational success and a lever for competitive advantage. If organizations want to take advantage of the knowledge they have, they need to understand how knowledge is created, shared and used within the organization.
Data, Information, and Knowledge
Knowledge is the highest form in the chain and is defined as the interpretation of information as perceived by the user from synthesized data. In short, knowledge is the meaning that the user derives from information that he or she perceives.
Knowledge Sharing (KS) in Organizations
Recognizing the importance of KS and the challenges it presents to organizations, this study analyzes the organizational, personal, relational and technological factors that either hinder or facilitate KS in organizations and discusses the impact of KS on organizational performance.
Knowledge Creation (KC)
KS is crucial for the creation of new knowledge; KE and KS processes benefit both organizations and employees, facilitating discussions about what they need to know and how to acquire the knowledge (Nonaka & Toyama, 2003). Cross-functional teams are a means of managing social collaboration and concept creation, and KC is the generation of new knowledge through internal and external resources to achieve organizational goals (Gholami et al., 2013).
Knowledge Management (KM)
The previous review found that knowledge is useful to organizations when strategically managed and applied. It is also concluded that knowledge management includes the creation, exchange, transfer and sharing of knowledge (KC, KE, KT, KS).
Knowledge Exchange (KE)
Knowledge Transfer (KT)
Knowledge Sharing (KS)
- Difference between KE, KT, and KS
- Knowledge-Sharing Intention
- Knowledge Sharing in Financial Institutions and Banks
- Why People Share or do not Share Knowledge
- Benefits of Knowledge Sharing
- Knowledge Sharing in the United Arab Emirates
- Knowledge Sharing Theories
- Introduction
The barriers to KS described in the literature are the lack of knowledge, insufficient knowledge. It discusses the benefits of knowledge sharing and presents the state of knowledge sharing in the UAE.
Theory of Reasoned Action (TRA)
The resource-based theory or the resource-based view of the business (RBT or RBV) is discussed by Barney (1991), and the knowledge-based theory or the knowledge-based view (KBV) is discussed by Nickerson & Zenger (2004). 1998) state that TRA is concerned with the intention to perform a behavior and does not look at the results of the behavior; thus TRA distinguishes between the behavioral intention and the target intention.
Self-Determination Theory (SDT)
Based on their 1985 work on SDT, Ryan and Deci (2000) distinguish between the types of motivation based on the reasons or goals that lead to an action. There are two types of motivation: (i) intrinsic motivation, performing an act for self-gratification or for the joy or pleasure associated with the act, and (ii) extrinsic motivation, which arises from certain exceptions outside the person (e.g. , to achieve a certain result) (Ryan & . Deci, 2000).
Social Exchange Theory (SET)
They provide insights that propose a model (as shown in Figure 10) to illustrate their views about the relationship between social exchange transactions and economic relations. Social exchange creates synergy when incorporated with KS, allowing an organization to achieve its organizational goals.
The Resource-Based View of the Firm (RBV)
The Knowledge-Based View of the Firm Theory (KBV)
Knowledge Sharing Models in Organizations
- Introduction
- Knowledge Sharing Models in Organizations Pee and Min (2017) Model
A simple model of online KS was proposed by Pee and Min (2017), who take the position that employees' KS behavior is affected by the fit or misfit of the person environment (PE) through its influence on the person's affective. Complementary fit is a mutual feeling between the person and the organization when an employee feels that his needs or aspirations are met by the environment through rewards or necessary resources, or when he feels that the organization utilizes the skills he possesses.
Yeo and Gold (2014) Model
Goh (2002) Model
Wang and Noe’s (2010) Model
Ipe’s (2003) Model
Knowledge Sharing Success Factors
- Introduction
- Factors that Impact Knowledge Sharing
Motivation is the fourth factor in the individual personality dimension and is presented by Wang and Noe (2010). Technology is another factor that has received a lot of attention in the literature (Norizzati, Ismail, & Taherali, 2009).
Organizational Factors
Holsapple and Joshi (2000) devised a framework showing that leadership is at the top of the influences on KM and KS. Management support is one of the factors leading to KS and better employee performance (Boumarafi & Jabnoun, 2008); and it is one of the organizational factors that influence KS (Nooshinfard & Nemati-Anarak, 2014).
Individual Personalities Factor
According to Rhodes et al (2008), a fair incentive system strengthens employees' willingness to willingly share knowledge and reaffirms their respect for the organizational culture of trust. Gagné (2009) argues that the motivation to share is based on people's attitude, which is influenced by the collective behavioral beliefs that emphasize this motivation.
Relationships Factor
Graven and Lerman (2003), citing Wenger (1998), argue that CoPs exist everywhere but are not noticeable because they are ubiquitous and informal. The four elements of CoP are meaning that is a product of learning; practice or professional affiliation; community - refers to a group with similar interests; and identity, i.e. social identification.
Technology Factor/Information Communication Technology
KS is made easier and stronger through effective use of technology. Therefore, the selection of the right technology is critical to the success of KS by matching the skills of employees with the needs of the organization (Riege, 2005). Levels of KS are directly linked to the facility and ease of use of the technology infrastructure; therefore, collaborative technology must be selected carefully.
Organizational Knowledge Sharing or Transfer
Tsai (2001) believes that inter-unit network positions promote social interactions and synergize unit KT practices. No individual employee, professional group or department in the organization is solely and exclusively in charge of creating new knowledge.
Organizational Performance
Proposed Framework for Enhancing KS
All these four input factors work together synergistically and are observed in their interaction in the second stage of the process, i.e. the knowledge sharing (or transfer) stage of the organization, either through inter-unit (i.e. cross-functional) or intra-unit sharing . The relationship factor is embodied in the communities of practice and the technology factor (ICT) encompasses the benefit and ease of use of.
Research Methodology
- Introduction
- Research Design
- Research Methodology
- Research Methods and Approaches Research Methods
According to this framework, the researcher takes a position regarding the nature of knowledge, and this choice will influence the whole process as well as the theoretical perspective he adopts, which in turn translates into the research questions and methodology chosen that will guide the methodology they will apply. They define methodology as the science of methods and the frame of reference and theoretical perspective (paradigm) for conducting research and suggest that it is about the research paradigm (ie, the theoretical framework).
Research Approaches
Researchers using quantitative research methods are neutral in the process of data collection and their results are usually generalizable; however, this method does not offer respondents the opportunity to state their feelings or perspectives on the phenomena being studied beyond the boundaries of the pre-set questions (Yilmaz, 2013).
Furthermore, they allow for longitudinal analysis because they are usually collected over a longer period of time; therefore, qualitative data analysis is a systematic search for meaning (Onwuegbuzie & Leech, 2007). Researchers have distinguished between quantitative research that follows a structured approach to inquiry and quantitative research that uses an unstructured approach.
In mixed methods research, a combination of both qualitative and quantitative methods is used to complement the strengths of both approaches. Furthermore, more and more frequently, combination occurs at the technique level and is referred to as mixed methods.
Why a quantitative approach has been used in this research
Population, Sampling, and Sampling Method
Population
Sampling
Surveys
Thompson, (2015) advises that when designing a survey, both the research questions and the data analysis schedule should be defined early in the design process. Surveys have several advantages: they are self-administered, they are cost-effective and cover large geographic areas, they provide anonymity and give respondents time to think about their answers, and the researcher cannot influence the answers.
The Instrument – “Questionnaire”
A self-administered questionnaire for the study was developed, covering variables related to organizational factors (culture, structure, trust, leadership and management support), personal factors (commitment, self-efficacy and motivation), relational factors (Communities of Practice) and technological factors.
Definition, Purpose, and Design of a Questionnaire
The organizational knowledge variable can be expected as a mediating factor between the independent factors and the dependent factor performance. All questions offer five answer options, and respondents must select one answer for each question.
Research Questions
The objectives of the study represent the goals that the study tries to achieve, and they are mentioned in the introduction of the research, along with the hypotheses. Therefore, the questions for all variables in this research were adapted from previous studies that were conducted by different researchers.
Piloting the Questionnaire
As a rule of thumb, a questionnaire should contain as many questions as necessary, but as few as possible (Bird recommends searching the literature and using existing questionnaires if possible and available. Follow this advice to avoid potential reliability problems and validity, this research chose to work with test questions.
Responses and Scales
Data Collection
It was also sent to trainees at the Emirates Institute for Banking and Financial Studies (EIBFS). The collected data were compiled in a database and coded according to the rating scales in the questionnaire.
Data Analysis Methods
This chapter is a discussion of the research methodology and a presentation of the conceptual framework and structures the research has developed in the literature. The chapter elaborates on how the research questions, responses and scales were selected and concludes with a review of the data collection and analysis methods.
Data Analysis and Results
- Introduction
- Data Treatment
Treatment of Missing Values
Two methods of data collection were used to maximize participant response to the survey. An electronic version of the survey was sent to participants as an online survey, with a cover letter explaining the purpose of the survey and providing guidelines for completing the survey.
Treatment for Outliers
Examination for Normality
Assuming that normality is useful in a variety of modeling frameworks, the most commonly used and convenient indicators of normality are skewness and kurtosis. NCSS Statistical Software (n.d.) states that parametric statistics based on assumptions of normality can be used if the variable is normally distributed.
Common Method Bias
Researchers have suggested mixing items from different constructs on the same questionnaire to reduce common method variance (Kline et al., 2000). Researchers have attempted to mix items from different constructs on the same questionnaire to reduce common method variance (Kline et al., 2000).
Non-response bias
Another procedural device followed in this study was a psychological separation of the predictor and criterion variables (Podsakoff et al, 2003). In this survey, only 15 responses had non-responses, which is 7% of the total number of survey responses.
The goodness of the fit statistics
Unit non-response occurs when a questionnaire is not returned because the person in the sample unit refuses to participate, cannot be contacted, or is unable to respond to the survey (Fisher, 1996). Researchers have suggested that the nonresponse mechanism is ignored for sampling-based and likelihood-based inference if the data are missing completely at random (Rubin, 1976, Little and Rubin, 1987).
The convergent and Divergent Validity
Data Analysis – Reliability Statistics
Kanyongo, et al (2007) define reliability as "the proportion of raw score variance explained by true score variance". The above data indicates that the 15 variables have achieved a minimum reliability coefficient of 0.747 and a maximum reliability coefficient of 0.937, i.e. they all fall within the acceptable reliability range.
Demographic Analysis
The response rates for online surveys are somewhat lower than the hard copy surveys (Anderson & Gansneder, 1995; . Kittleson, 1995).
Feedback from this experienced group of employees enriches the study through their long and substantial experience.
First, the proportion of responses from managerial and executive positions was 30.1% of the total responses. Secondly, the total response data gives a normal distribution of the respondents, which almost forms a slightly sloping bell curve.
Again, the ratio of responses can be seen as a fair representation of bank employee demographics.
Hypothesis Testing
A statistically significant, positive correlation was found between organizational structure and KS (r = 0.521; p = 0.000), as shown in Table 22. A statistically significant, positive correlation was found between job satisfaction and commitment and KS (r = 0.641; p = 0.000), as shown in Table 27.
