EXAMINING REQUIREMENTS AND DIMENSIONS OF KNOWLEDGE QUALITY: A STUDY OF IRANIAN SMES
Mehrnaz Fahimi Rad
1, Naser Valaei
1, Amir Haghbinshomali
11Faculty of Management, Multimedia University, Cyberjaya, Malaysia [email protected], [email protected],
Abstract
Quality is not a new concept. It has been an issue for all companies to obtain high quality products and services. Obviously, it increases the efficiency as well as the effectiveness of a company. The higher the degree of knowledge quality in business setting, the lesser would it be the costs involved in production and procurement. Whenever qualified knowledge is provided, it has an intense potential for higher income and increased sells.
SMEs as small knowledge groups with high level of flexibility and robustness can face issues and challenges through their knowledge acquisition capabilities as well as sharing behavior. However, the desired output is ensured if the company/organization are benefiting from a high standard of knowledge quality. This research investigates the new construct of “knowledge quality” amongst SMEs in Iran. The paper delineates the main dimensions of knowledge quality which are “intrinsic, contextual, and actionable knowledge quality” as well as examining the essential antecedents (requirements) of knowledge quality which are “functional diversity, absorptive capacity, and knowledge networks” [1]. The finding approves that the aforementioned requirements are directly connected to knowledge quality.
Keywords: Knowledge quality, dimensions, requirements, SMEs, Iran
1. KNOWLEDGE QUALITY
Nowadays, due to severe competition and a globalized market, the mere utilization of KM or implementation of a “knowledge management system (KMS)” does not suffice to tackle this turbulent and disordered market. However, it comes to the “QUALITY” of the provided knowledge by entities or systems. A high standard of knowledge implies the knowledge that is not stagnant and flows in the vessels of the organization and its circulation as well as application produces new knowledge.
Therefore, in order to be prosperous and face the difficulties caused by market changes, a high standard of knowledge is required [2].
Most business practices involve developing premium goods or services with high quality to compete with rivals. Since the traditional view of quality is bound to product and service and all businesses overemphasize these issues, the new wave of competition is not subject to product and service quality. Recently, the matter of data and information quality has gotten a great attention amongst businesses [3]. Therefore, there was no emphasis regarding knowledge quality due to its multidisciplinary and obscure concept. As mentioned before, knowledge is the most significant resource available to a company and its effective utilization is contingent to the degree to which it comes with quality [4]. It can be concluded that the research in this matter is not sufficient.
Comprehending the essence of knowledge quality requires a deep understanding of its predecessors i.e. data and information. In order to perceive the concept of knowledge quality, first of all, one should consider the concept of data quality as well as information quality since they have a hierarchical foundation [5]. According to [6], high quality data indicated “accuracy, currency, accessibility, relevance, timeliness, completeness, and consistency”. Furthermore, as mentioned by [7], they categorized data quality into four contexts: “intrinsic data quality, contextual data quality, representational data quality, and accessibility data quality”.
Information quality, likewise, has an important role in these turbulent business circumstances and is considered as one of the main issues for obtaining a core competency over the competitors.
Information quality is observed as a main character in the prosperous “information system (IS)” [8].
Likewise to data quality, the existing literature implies four contexts of information quality which are
“intrinsic information quality, contextual information quality, representational information quality, and accessibility information quality” [7].
Delineating and pinpointing the concept of knowledge quality rose up interest among researchers of KM. According to [1], knowledge quality is “the extent to which the awareness and understanding of ideas, logics, relationships, and circumstances are fit for use, relevant and valuable to context, and easy to adapt”. Some authors believed that knowledge quality is measured and taken into account by its degree of utilization, occurrence (frequency of use), and the degree to which it caused innovation.
Knowledge quality likewise to data quality and information quality has its contexts: “accuracy, consistency, currency, data interpretability, degree of context, degree of detail, degree of importance, sharing, usefulness, and volatility” [9]. As previously mentioned data quality and information quality has multiple aspects so it is construed that knowledge quality, just like data and information quality, has several aspects as well. Aforementioned authors believed that some of data and information quality aspects are quite relevant to knowledge quality.
2. DIMENSIONS OF KNOWLEDGE QUALITY
Knowledge quality has multiple aspects from which “intrinsic knowledge quality, contextual knowledge quality, and actionable knowledge quality” are main concerns (see Fig. 1). The amalgamation of these dimensions makes the concept of knowledge quality credible.
Fig. 1: Knowledge quality dimensions
As illustrated in Fig. 1, the conjunction of these dimensions shape the knowledge quality. These aspects of knowledge quality guarantee the innovation process by assuring credibility/trustworthiness, pertinence/relatedness, and adjustability or adaptability of knowledge. Following sections elaborate these aspects of knowledge quality in detail.
2.1 Intrinsic knowledge quality
This dimension of knowledge quality implies that knowledge has quality by virtue of itself and in its own right. Some authors perceived that knowledge and information quality share some mutual characteristics. For instance, according to [7], intrinsic information quality has “accuracy, believability, and objectivity” which is applicable to knowledge quality. Jarke and Vassiliou [10] added credibility, consistency, and completeness as other characteristics of information quality which are applicable to knowledge quality as well.
2.2 Contextual knowledge quality
Contextual knowledge quality refers to the sort of knowledge that must be relevant to the organizational tasks at hand. It implies that knowledge must be relevant, thorough, proper, and timely to augment the value. Since there is a consensus between the characteristics of information quality and knowledge quality, it can be inferred that they have some similar attributes regarding their contextual characteristics. For instance, contextual information quality shares attributes like “value- added, relevance, completeness, timeliness, and appropriate amount” [7] with knowledge quality.
Furthermore, [11] declared quantity and timeliness as the attributes of contextual information quality.
2.3 Actionable knowledge quality
Knowledge is a weapon to conquer the battles in the market field whenever it is applicable and is put into action. The power of knowledge/knowledge asset can be realized once it is applied in some
context. All the advantages of knowledge are stemmed from its dynamic tenets or in other words, stagnant knowledge is not considered as a strategic asset nor could it be a core competency. Yoo [1]
defined actionable knowledge quality as “the extent to which knowledge is expandable, adaptable, or easily applied to tasks”. Knowledge should be put in action to demonstrate its power as well as prosperity [12].
3. RESEARCH METHODOLOGY
Since this paper considers SMEs in Iran, however companies in Tehran city, the capital of Iran, were chosen to be analyzed. Tehran city has seven industrial parks which are “Parand industrial park, Shamsabad industrial park, Abbasabad industrial park, Nazarabad industrial park, Aliabad industrial park, Firozkoh industrial park and Nasirabad industrial park” [13]. In this study, samples were chosen from three industrial parks which are “Abbasabad industrial park, Shamsabad industrial park, and Nazarabad industrial park”. 200 questionnaires were issued amongst these industrial parks from which 147 companies responded. Amongst the responded questionnaires, 17 pieces were considered as inappropriate values and some parts were not filled up completely. Therefore, a sample size of 130 companies was chosen for analysis amongst these industrial parks. To support the appropriateness of sample size, according to Roscoe (1975), “sample sizes larger than 30 and less than 500 are appropriate for most researches”. Therefore it can be construed that this study’s sample size is appropriate.
For obtaining the primary data, a survey instrument was developed. This study revised the survey instrument developed by [1] with minor modifications. The developed questionnaire consists of three parts: (Part one: “Demographic Information”, Part two: “Knowledge Quality Dimensions”, and Part three: “Antecedents of Knowledge Quality”). The first part reaps information related to personal and demographic data. Part two consists of questions related to the dimensions of “knowledge quality”.
The third part of the questionnaire includes questions pertinent to the requirements of having a standard level of knowledge/quality knowledge i.e. “absorptive capacity, knowledge network, as well as functional diversity”. Part two and three were measured by utilizing a seven-level Likert scale from 0-6 where value “0” is for “don’t know/not sure” and value “6” for totally agree. Data were entered and analyzed by utilizing SPSS 17.0.
4. DEMOGRAPHIC INFORMATION
Most of respondents are in the range of 20-40 years old. Majority of them are executives from different divisions of the company (management, finance, marketing, operation, information management, and so forth) from which 37.7 percent are “chief executive officers” and 21 out of 130 participants are “chief financial officer”. For the purpose of this research, since the research is more accurate on SMEs with more than 30 employees regarding the concepts of functional diversity, therefore those firms were chosen that had employees of more than 30. 49.2 percent of participants mentioned that their companies have employees within the range of 70-99. Majority of SMEs are from the food and beverage industry (30 percent of participants). Electrical equipments and machinery consist of 13.1 percent of participating SMEs.
5. REQUIREMENTS OF KNOWLEDGE QUALITY
In addition to dimensions of knowledge quality, there are some antecedents or requirements of knowledge quality that shape its essence. These determinants/elements of knowledge quality are
“absorptive capacity, functional diversity, and knowledge network” [1]. Considering these determinants or building blocks of knowledge quality, the role of organization lies in hiring employees with different skills who are adept to business processes as well as channeling to obtain external knowledge.
Therefore, the role of organization/company in controlling the building blocks of knowledge quality is crucial. In order to have a prosperous organization, a company should heed to the degree of knowledge quality by making the right decisions in areas related to knowledge quality building blocks.
Elements of knowledge quality are elaborated in the next subsections.
5.1 Absorptive capacity
Absorptive capacity/capability is tied with learning. Absorptive capacity is all about exploiting human knowledge as well as taking advantage of knowledge stored in the organizational memory [14].
According to [15] “absorptive capacity refers to an organization's ability to identify, assimilate, and apply external information and knowledge streams to product, service, and process innovation”. The key functions that ease absorptive capacity are the degree to which knowledge is acquired and distributed along the business setting. Furthermore, an organization can improve its absorptive capability to obtain knowledge from intra-organizational sources to integrate it with inter-organizational sources thereby boosting the innovation process. An organization must vitalize its learning-based resources to strengthen its decision making process.
This construct is one of the main requirements of having quality knowledge. This capability involves the extent to which the company adopts a learning strategy and utilizing the existing knowledge as well as turning the available knowledge to new knowledge (knowledge creation). Table 1 shows the descriptive statistics of all variables regarding absorptive capacity. It showed a lesser level of disagreement towards the statement that the employees are capable of utilizing the existing knowledge with a mean of 3.78.
Table 1: Descriptive statistics of Absorptive Capacity
5.2 Functional diversity
Functional diversity involves the assortment of different people from different training, expertise, and unique capabilities. The functional diversity takes place in such a way that all entities complement each other’s specifications and expertise and support all functional needs for an innovative organization. As mentioned by [16], functional diversity is the amalgamation of functional expertise sorted in a company/team and the level of dispersion of entities across a range of pertinent and connected disciplines.
An organization needs the knowledge flow from different contexts of business divisions to be streamlined into the process of making a high qualified products and services. Another advantage of functional diversity is that different employees with different experience and expertise will have multiple perspectives toward business dilemmas rather than a specific and narrow perspective. A company should take advantage of the opportunity of being functionally diversified because it will emerge a condition from which new ideas spark and flourish employees’ insights and intuitions.
Table 2 shows all variables regarding functional diversity of participant SMEs. Respondents mentioned that their companies are not wholly diverse with a mean of 3.0. Furthermore they disagreed that the company has experts and specialist from diverse business divisions and functional areas with a low mean of 2.82. They somehow disagreed that the appointed representatives affected or influenced the project at hand. Based on these results, it can be summarized that Iranian SMEs are not fully diverse with employees from different pool of knowledge and different expertise.
Table 2: Descriptive statistics of Functional Diversity
5.3 Knowledge network
A long lasting core competency does not stem from managing the existing knowledge but the administration of continuously producing new knowledge. To do so, it needs a knowledge networking approach that utilizes the knowledge from all entities involved in organization/company from customers, stakeholders, shareholders, suppliers, employees, front-line employees, middle managers, and senior managers. According to [17] knowledge networks is defined as “a number of people, resources and relationships among them, who are assembled in order to accumulate and use knowledge primarily by means of knowledge creation and transfer processes, for the purpose of creating value”.
Knowledge networks are distinguished as “emergent” and “intentional” knowledge networks. The
“intentional” knowledge networks are those that are initiated from the beginning. The “emergent”
knowledge networks are those that exist but need to be refined and nurtured thereby delivering high performance. Through the advent of Internet and advancement in technology and emergence of web 2.0 (social networking tools) as well as web 3.0, the process of dynamic knowledge network surfaced from which knowledge is transferred easily and circulated among individuals who could be prospective customers to a company, therefore contributing new knowledge to the process of creating a new product or service.
As tabulated in table 3, most variables were graded with the disagree option. This indicates that Iranian SMEs do not consent to strengthening their connections with their competitors. It can be postulated that these companies are practicing a red ocean strategy towards their rivals. Respondents somehow agreed that they have had applicable contacts outside the organization with a mean of 3.52.
Besides, they pointed out that acquiring and accessing the external knowledge is a cumbersome activity and it requires much effort to tackle these issues with a low mean of 2.95 and 2.94 respectively.
Table 3: Descriptive statistics of Knowledge Network
6. RESEARCH FRAMEWORK
Conceptual/research framework is developed based on the systematic review of literature. In previous sections all dimensions and requirements of quality knowledge were discussed. The research framework of this study is adopted from [1] which is illustrated in Fig. 2.
Fig. 2: Research framework
6.1 Hypotheses of the study
Based on the theoretical framework and the context of Iranian SMEs, four hypotheses are developed which are:
H1: Companies with upmost degree of “absorptive capacity” will obtain a decent degree of “knowledge quality”.
H2: Companies with upmost degree of “functional diversity” will obtain a decent degree of “knowledge quality”.
H3: Companies with upmost degree of “knowledge network” will obtain a decent degree of knowledge quality”.
H4: Antecedents of “knowledge quality” significantly explain the variance of “knowledge quality”.
7. DATA ANALYSIS
Four hypotheses were developed to gauge the level of knowledge quality amongst Iranian SMEs. To test the hypotheses, regression analysis is applied to examine the relationships amongst “dependent and independent variables”. Based on the research framework, knowledge quality is considered as the dependent variable and “absorptive capacity, functional diversity, and knowledge network” are regarded as the independent variable of this study.
7.1 Absorptive capacity
By applying the regression analysis, these hypotheses are tested. First hypothesis examines the relationship between having a diverse functional business environment as a stimulator of knowledge quality. Table 4 shows that R-square is 0.685. The value of R-square suggests that the degree of having knowledge quality can be explained by absorptive capacity (independent variable) with a P- value of 0.000 which is significant (the P-value below 0.005 is considered significant).
Table 4: Model summary of Absorptive Capacity
By considering the ANOVA table (Table 5) a P-value of 0.000 relies on the fact that absorptive capacity can be considered to model knowledge quality. Coefficients of absorptive capacity are illustrated in table 6 from which the significant role of absorptive capacity with a coefficient of 0.773 on having quality knowledge is proved. Based on these tables, first hypothesis is substantiated.
Table 5: ANOVA of Absorptive Capacity
Table 6: Coefficients of Absorptive Capacity
7.2 Functional diversity
Second hypothesis examines the relationship between having a functionally diverse organization and the potential to obtain quality knowledge. Table 7 shows that R-square is 0.452. The value of R-square implies that the degree of having knowledge quality cannot be explained by functional diversity (independent variable) with a P-value of 0.000 which is significant. Indeed the value of R-square does not illustrate a very strong relationship between functional diversity and knowledge quality.
Table 7: Model summary of Functional Diversity
By considering the ANOVA table (Table 8), a P-value of 0.000 relies on the fact that functional diversity did not show as a good measure to model knowledge quality. Coefficients of functional diversity are demonstrated in table 9 from which the role of functional diversity with a coefficient of 0.615 on having quality knowledge is verified. However, based on these tables, second hypothesis is rejected.
Table 8: ANOVA of Functional Diversity
Table 9: Coefficients of Functional Diversity
7.3 Knowledge network
Third hypothesis examines the relationship between knowledge network as an independent variable and knowledge network. Table 10 shows that R-square is 0.54. The value of R-square shows that the degree of having knowledge quality can be explained by knowledge network with a P-value of 0.000 which is significant.
Table 10: Model summary of Knowledge Network
As illustrated in ANOVA table (Table 11) a P-value of 0.000 relies on the fact that knowledge network is a good measure to model knowledge quality. Coefficients of knowledge network are shown in table 12 from which the important role of knowledge network with a coefficient of 0.615 on having quality knowledge is verified. Based on these tables, third hypothesis is substantiated.
Table 11: ANOVA ofKnowledge Network
Table 12: Coefficients of Knowledge Network
7.4 Knowledge quality
Fourth hypothesis examines all independent variables i.e. “knowledge network, functional diversity, and absorptive capacity” with knowledge quality. Table 13 demonstrates that R-square is 0.736. The value of R-square relies on the fact that the degree of having knowledge quality can be examined by independent variables i.e. “knowledge network, functional diversity, and absorptive capacity” with a P- value of 0.000 which is significant. Therefore these independent variables can be applied to predict knowledge quality.
Table 13: Model summary of Knowledge Quality
As illustrated in ANOVA table (Table 14) a P-value of 0.000 relies on the fact that aforementioned independent variables are appropriate measures to model knowledge quality. Coefficients of these independent variables are shown in table 15 from which the role of absorptive capacity and knowledge network is evident with coefficients of 0.537 and 0.283 on having quality knowledge is verified.
Functional diversity obtained a low coefficient of 0.014 means that Iranian SMEs are not functionally diverse. However, based on these tables, fourth hypothesis is substantiated.
Table 14: ANOVA of Knowledge Quality
Table 15: Coefficients of Knowledge Quality
8. CONCLUSION
It is concluded that Iranian SMEs do not possess an upmost absorptive capacity but they do partially and to a medium extent possess this capability. Based on these results, Iranian SMEs are not fully diverse with employees from different pools of knowledge and disparate expertise. According to the findings, most variables were graded with disagree/somehow disagree options. This indicates that Iranian SMEs do not consent to strengthening their connections with their competitors. To summarize this requirement of knowledge quality, the result implies that Iranian SMEs do not contemplate regarding their knowledge networks. It should be noted that one of the substantial parameters in having a knowledge network is organizational culture. As a consequence, knowledge sharing behavior is resorted to organizational culture. Therefore, the upward chain of knowledge sharing behavior, organizational culture, and knowledge network is a must for quality knowledge.
Amongst the developed hypotheses, the second hypothesis was not substantiated. It can be concluded that having a functionally diverse company may not be suitable for SMEs whereas most of them have employees from fifty five to ninety nine, they are more focused on a specific product or service rather than a portfolio of products. In the case where a company produces a bunch of goods or services, it should consider having a diverse company within which, different people with various knowledge are recruited. The fourth hypothesis which gauged all the requirements of knowledge quality showed a positive relation i.e. “antecedents of knowledge quality significantly explain the variance of knowledge quality”.
REFERENCES
[1] Yoo, D.K., Vonderembse, M.A., and Ragu-Nathan, T.S. (2010). Knowledge Quality: Antecedents and Consequence in Project Teams, Journal of Knowledge Management, 15(2), pp. 329-343.
[2] Nonaka, I. and Teece, D.J. (2001). Managing Industrial Knowledge: Creating, Transfer, and Utilization, Sage, London.
[3] Nelson, R.R., Todd, P.A. and Wixom, B.H. (2005). Antecedents of Information and System Quality:
An Empirical Examination within the Context of Data Warehousing, Journal of Management Information Systems, 21(4), pp. 199-235.
[4] Yu, S., Kim, Y. and Kim, M. (2007). ‘‘Do We Know What Really Drives KM performance?’’, Journal of Knowledge Management, 11(6), pp. 39-53.
[5] Nonaka, I. (1994). A Dynamic Theory of Organizational Knowledge Creation, Organization science, 5(1), pp. 14-38.
[6] Cappiello, C., Francalanci, C. and Pernici, B. (2003). Time-related Factors of Data Quality in Multichannel Information Systems, Journal of Management Information Systems, 20(3), pp. 71-91.
[7] Wang, R.Y., Lee, Y.W., Pipino, L.L. and Strong, D.M. (1998). Manage Your Information as a Product, Sloan Management Review, 39(4), pp. 95-105.
[8] DeLone, W.H. and McLean, E.R. (1992). Information Systems Success: the Quest for the Dependent Variable, Information Systems Research, 3(1), pp. 60-96.
[9] Rao, L. and Osei-Bryson, K. (2007). Towards Defining Dimensions of Knowledge Systems Quality, Expert Systems with Applications, 33(2), pp. 368-78.
[10] Jarke, M., Vassiliou, Y. (1997). Data Warehouse Quality: a Review of the DWQ Project, Proceedings of the Conference on Information Quality, Cambridge, MA, pp. 299–313.
[11] Zmud, R. (1978). Concepts, Theories and Techniques: An Empirical Investigation of the Dimensionality of the Concept of Information, Decision Sciences, 9(2), pp. 187–195.
[12] Droge, C., Claycomb, C. and Germain, R. (2003). Does Knowledge Mediate the Effect of Context on Performance? Some Initial Evidence, Decision Sciences, 34(3), pp. 541-68.
[13] www.sme.ir
[14] Cohen, W.M. and Levinthal, D.A. (1990). Absorptive Capacity: a New Perspective on Learning and Innovation, Administrative Science Quarterly, 35(1), pp. 128-52.
[15] Cohen, W., and Levinthal, D. (1994). Fortune Favoured the Prepared Firm, Management Science, 40(2), pp. 227-51.
[16] Bunderson, J.S. and Sutcliffe, K.M. (2002). Comparing Alternative Conceptualizations of Functional Diversity in Management Teams: Process and Performance Effects, Academy of Management Journal, 45(5), pp. 875-93.
[17] Seubert, E., Balaji, Y. and Makhija, M. (2001). The Knowledge Imperative, CIO Special Advertising Supplement, March 15.
