Critique of Quantitative Methods Journal Paper

Critique of Quantitative Methods Journal Paper


Aalbers, Dolfsma, and Koppius, (2013) in their quantitative journal paper titled Individual connectedness in innovation networks: On the role of individual motivation, present an analytical study in which the relationship between intrinsic and extrinsic motivations of an individual employee affects his position within the structural network of knowledge sharing. In the literature, the authors identify several contributions that have been made by other researchers. Intuitively, the authors acknowledge that so much has been done in this area of study, including the design of the relationship between an organization’s competitive advantage and its level of knowledge sharing among employees (Aalbers, Dolfsma, Koppius, 2013). They emphasize that the literature provides a background on which the connection between innovativeness and creativity in an organization with effective knowledge sharing explicitly manifest.  Inasmuch as the study recognizes the tremendous contributions that have been made in the past by other researchers, they make it evident that the number of variables that have so far been analyzed to be influential in knowledge sharing is limited. In addition, the article articulates that though previous researches have struck a relationship between innovativeness and knowledge sharing, it is still unclear how this system of knowledge sharing can be optimized to create the desired creativity within an organization (Aalbers, Dolfsma, Koppius, 2013).

Moreover, it emerges from the review of past literature that the influence of the position of an individual within the structural network of knowledge sharing has not been extensively discussed (Wen-Chung & Chen-Ling, 2010). Thus, the study comes up with a theoretical framework, which is then developed into practical testing, that an employee who is strategically placed within a knowledge transfer network could unintentionally influence the mode and value of information sharing. It is identified further that this relationship between the position of an individual as influenced by motivational factors and knowledge transfer still lacks sufficient information and data. While it is true that the research recognizes that past studies have given significant attention to the emerging connection between individual characteristics or attributes with willingness and ability to share knowledge, this study posits that the resultant psychological differences among individual employees has not been widely studied, for it has a considerable effect on knowledge sharing.

Based on these gaps, the authors of this article develop a thesis that transcends beyond the mere influence of individual attributes in knowledge sharing, to include other aspects such as position within the knowledge sharing network, external and internal motivation, as well psychological differences among individuals and how these affect a person’s willingness to engage in a constructive knowledge sharing. With this in mind, the researches seek to have a gainful knowledge on the best and most effective strategies that could be employed to foster knowledge sharing within an organization, so that the same could translate to creativity and innovativeness (Aalbers, Dolfsma, Koppius, 2013).

Despite the fact that previous studies have looked at how intrinsic and extrinsic motivation affect the willingness and ability of an individual to share knowledge, the authors recognize that a gap still exists on the number of variables that influence the process, as well as the significance of group psychological differences.  Based on these gaps identified, the research objectively sought to analyze how motivation influences connectedness of an individual employee within the network of knowledge sharing, as well as the contribution that psychological differences make in enhancing a knowledge sharing behavior in an organizational setup (Aalbers, Dolfsma, Koppius, 2013).


Theoretical Evaluation 

The theory that proposes the relationship between knowledge sharing and organizational creativity and innovativeness has been proposed by many researchers. Hsiu-Fen (2007), developed a study based on this theoretical framework and established a connection between effective knowledge transfer and organizational competitive advantage. It was found that the level of knowledge sharing behavior within every business entity translates into an exponential increase in innovativeness and creativity, since people are open to a diverse range of ideas. In the same manner, knowledge sharing behavior is closely linked to good organizational culture, which Menguc, Auh, and Chan, (2011) hold as a basic prerequisite for organizational success.

While many of the previous studies that have been centered on this theory differ in their evaluation of variables, basically all of them recognize the influence of intrinsic as well as extrinsic motivation factors in the process of organizational knowledge sharing. Menguc, Auh, and Chan, (2011) posit that the top management is very crucial in setting things straight for the process of knowledge sharing to proceed effectively. For instance, it could adopt motivational practices such as performance appraisal, offering of paid leaves among others so as to directly motivate the employees to have a positive and loyal stance towards the success of the organization. This could be particularly useful in cultivating the spirit of teamwork among employees, which could easily foster the behavior of knowledge sharing (Hung, Durcikova, Lai, & Lin, 2011). The action of the top management mixed with the type of technology that is adopted within an organization is crucial for the smooth process of information interchange. However, the study does not consider these two elements to be very influential in the development of the theory of information sharing, but emphasizes the importance of individual characteristics as affected by motivation.

In Hsiu-Fen’s (2007) article, the theory of individual motivation and willingness to share knowledge is extensively discussed, particularly in the health sector where there is need for practitioners to frequently update their knowledge on medical processes. The researchers identify that sharing of knowledge is very important in achieving a common aim and objective, as it adds to the level of effectiveness of an entire team. However, Galia (2008) introduces a limit to this relationship by arguing that sharing of knowledge cannot lead to an exponential increase in the level of innovativeness and creativity in an organization; but that this relationship is a function of several other factors such as the value of the knowledge being transferred, the willingness of participants to share extensively, and the accompanying psychological differences from one person to another.

The article attempts to articulate these theoretical frameworks in its research body and scope, but a few inconsistencies are noted with regard to current state of the theory as well as emerging trends. For instance, the study concludes that neither intrinsic nor extrinsic motivational factors are influential in the connectedness of an individual within the enabling framework of knowledge sharing. Well, Olatokun and Nwafor (2012), who conducted a recent study related to the topic of interest fund out that this claim is not entirely true, and agree with other previous scholars that any motivational factor is very crucial in determining the willingness and strategic ability of an individual to engage in a constructive network of knowledge transfer. All said and done, the authors develops an interesting piece of quantitative study around the theory, thus building on the gaps that have so far been left by others in past attempts.

Quantitative Methods and Analysis

The study consists of well-organized set of hypotheses, research questions, study design and sample. Based on the above discussed gaps identified by the authors in the literature, and following the preposition of the theory on which the research is built, the article is developed around two main hypotheses. Firstly, the authors hypothesize that the degree of connectedness of an individual within the network of innovative knowledge transfer is positively influenced by their personal internal or intrinsic motivation. While this hypothesis seems to support the prepositions that are available in the literature, the final result of this study rebuttals the claim. In a quantitative research like this, though, it would have been better if the authors used alternative hypothesis instead of the null hypothesis, then work around trying to prove it (Kuvaas, Buch, & Dysvik, 2012). The second hypothesis that is developed by the researchers is the degree of connectedness of an individual within a network of innovative knowledge transfer is also positively correlated with extrinsic motivation. While in this scenario the authors use the term “inter-unit ties” to refer to connectedness, they adopt the same approach as in the first hypothesis, and ultimately prove their own hypothesis to be wrong. All in all, the hypothesis is inclusive and speaks entirely about the research questions and purpose of the study, as they ought to have.

The study employs a quasi-experimental design, where there are dependent, independent and control variables. This is kind of design works well for a study of this nature, and it succeeds to address one key gap that was always left by previous researchers: handling a good number of variables. In this study, just as earlier discussed, the authors move in to fill the gap of limited number of variables that have so far been studied with regard to this theoretical framework, and include such elements as gender, tenure, unit, number of ties, closeness centrality, intrinsic motivation, extrinsic motivation, and inter-unit ties. With all the statistical scores and correlations of all these eight variables, the study could be considered as reliable and very authentic. In event, it satisfies the authors’ original claim that personal characteristics alone could not be independently taken as the sole factors influencing the process of innovative knowledge sharing among employees. The number of variables employed in the study, basically, could be a source of statistical problems especially when it comes to interpretation and analysis of data. While this high number increases confidence levels in the results, the amount of interferences and deviations could also be high, thereby predisposing the results to considerable amount of uncertainties.

The sampling method used is reasonable up to some extent. Using the Snowball sampling method is appropriate for this kind of study where the target participants are generally unclear, since it involves the stepwise delivery of questionnaires. At the end, it ensures that the right and best respondents have been chosen for the study (Ghaznavi, Perry, Logan, & Toulson, 2011). However, since this study is meant for the global audience, its choice of population was appropriate, but not the sample. The two organizations chosen, though being headquarters of their businesses, cannot be taken to represent all other organizations across the globe. There could be obvious biases related to organizational culture, geographical location, and cultural differences among individual employees that could not have been perfect representatives of other organizations in other parts of the world. On the other hand, the mentioning and description of the sample is extensively done, including the location of the companies, their line of business, the number of employees in each of them, their economic status, and level of innovativeness. Actually, the type and level of innovation in each of the organizations chosen is befitting to the kind of study being carried out, since the main objective of the study lies in the relationship between knowledge sharing and innovativeness of an organization.

The authors have made use of various statistical techniques to test the hypotheses. For instance, the results are presented in descriptive data in the form of numerical and tables, which simplifies the interpretation and reading of the data obtained. The authors also calculate various statistical elements for each dependent, independent, and control variable including mean, standard deviations, zero-order correlations, skewness, kurtosis, histograms, homoscedasticity, and regression analyses. All these statistical calculations were made to aid in better interpretation of scores from variables, and also to test for the confidence and reliability levels of the data obtained. The mean, for instance, represents the average of the scores, usually calculated by dividing the sum of observed scores by the total number of observations made. The mean is a rough estimate of most probable data points, which is a representation of the subsequent and previous scores (Norkett, 2013).

On the other hand, the standard deviations have been calculated to show the extent of dispersion from the average the data obtained manifest. Usually, a value that is more than one represents a huge deviation, but those that are below one have no significant meaning and impact on the final results (Norkett, 2013). The values obtained in the study thus show a high degree of confidence and validity of results since they are mostly below one, except for inter-unit ties, ties, and tenure variables. Standard deviation refers to the measure of reliability of statistical results. The above mentioned variables are the only ones that show some degree of unreliability, but generally, the results appear valid and authentic.

Measures of non-normal distributions such as skewness and kurtosis have also been used in the interpretation of the results. The former is the measure of the extent to which a real-valued random variable is to a given side of the mean. Skewness can be positive, negative, or undefined, but generally values that are not greater than one speak of a high degree of reliability and lack of non-normal distributions (Norkett, 2013). Similarly, kurtosis is the measure of “peakedness” of a probability distribution of a real-valued random variable. In effect, kurtosis and skewness have the same manner of measure of measuring for reliability and presence of non-normal distributions. Moreover, zero-order correlations have also been used in order to enhance the regression and prediction levels. The zero-order correlation is important in the understanding and comparing of two variables (Ghaznavi, Perry, Logan, & Toulson, 2011). It helps to reduce the level of redundancy during statistical analyses of data obtained from independent variables. While these values are not shown in the numerical presentation of results, it is important that the authors included them in the analyses since they help to increase the degree of validity in the measure of hypotheses. Histograms were also used, though not included in the published version of the report. These are graphical representations of numerical data in terms of bars, in which the number of scores corresponds to the area of each bar. The use of histograms is positive as it helps in easy interpretation and reading of results. Lastly, the measures of linear multiple regression corresponds to the use of zero-order correlations, since it also offers comparative analysis of variables. Regression analysis helps researchers understand the nature in which a dependent variable changes as a result of alteration in the level of independent variables (Norkett, 2013). The use of all these statistical calculations is important in giving the assurance that the research is valid and the data obtained are reliable. However, the ultimate finding that neither intrinsic nor extrinsic motivation has any effect on the connectedness of an individual in the network of innovative knowledge transfer is unusual as it disagrees with past studies and theoretical literature.


From the critical analysis of the article presented herein, it emerges that the authors did quite a significant piece of work on the development of the theoretical background of the topic of study. For instance, the study has come up with additional variables that can significantly affect the relationship between and individual’s connectedness within the knowledge sharing network, and his ability and willingness to participate in that course of action. Additionally, the study paints a clear picture of the importance of psychological differences among individuals in the process of innovative knowledge sharing, as well as the impact of both intrinsic and extrinsic motivations. The study provides practical and lucid suggestions for managers to actively take up their role in motivating employees, so as to create an enabling environment for knowledge transfer. However, it does not provide a complete set of strategies for optimizing knowledge sharing behavior among individual employees, and how this could be translated into organizational creativity and innovativeness. If this segment could have been included, the study could have been more robust and significant. Lastly, the selection of samples could have adopted a global face, where sets of respondents could be picked from various organizations to enhance diversity and representativeness of the results obtained.


Aalbers, R., Dolfsma, W., Koppius, O., (2013), Individual connectedness in innovation networks: On the role of individual motivation, Research Policy, Vol. 42, pp. 624-634.

Galia, F. (2008). Intrinsic-Extrinsic Motivations and Knowledge Sharing in French Firms. ICFAI Journal Of Knowledge Management, 6(1), 56-80.

Ghaznavi, M., Perry, M., Logan, K., & Toulson, P. (2011). Knowledge Sharing in Ego-Centered Knowledge Networks of Professionals: Role of Transactive Memory, Trust, and Reciprocity. Proceedings Of The International Conference On Intellectual Capital, Knowledge Management & Organizational Learning, 681-688.

Hsiu-Fen, L. (2007). Effects of extrinsic and intrinsic motivation on employee knowledge sharing intentions. Journal Of Information Science, 33(2), 135-149.

Hung, S., Durcikova, A., Lai, H., & Lin, W. (2011). The influence of intrinsic and extrinsic motivation on individuals’ knowledge sharing behavior.(Report). International Journal Of Human – Computer Studies, (6), 415.

Kuvaas, B., Buch, R., & Dysvik, A. (2012). Perceived training intensity and knowledge sharing: Sharing for intrinsic and prosocial reasons. Human Resource Management, 51(2), 167-187.

Menguc, B., Auh, S., & Chan Kim, Y. (2011). Salespeople’s Knowledge-Sharing Behaviors with Coworkers Outside the Sales Unit. Journal Of Personal Selling & Sales Management, 31(2), 103-122.

Norkett, L. (2013). Quantitative research. This practice profile is based on NS673 Hoe J, Hoare Z (2012) Understanding quantitative research: part 1. Nursing Standard. 27, 15-17, 52-57. Nursing Standard, 27(43), 59.

Olatokun, W., & Nwafor, C. (2012). The effect of extrinsic and intrinsic motivation on knowledge sharing intentions of civil servants in Ebonyi State, Nigeria. Information Development, 28(3), 216.

Wen-Chung, L., & Chen-Ling, F. (2010). The effect of different motivation factors on knowledge-sharing willingness and behavior. Social Behavior & Personality: An International Journal, 38(6), 753-758.




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