The Contribution of Corporate Social Responsibility, Network Working and Knowledge Management to Organization’s Performance, plus a Case Study Based on Pfizer, Inc.

The interrelation of organization’s structural components like ethics, human resources management (HRM) and information systems (IS) can influence organization financial performance in positive or negative ways depending on the organization’s performance on these practices. The purpose of this article is to evaluate the contribution of organization’s corporate social responsibility (CSR) as an ethics practice, network working as a HRM practice, and knowledge management as an IS practice, to the organization’s financial performance. The article is based on an expansion of readings provided as part of Units 4, 5 and 6 of Capella’s DBA course DB8002, Vallaster, Lindgreen & Maon (2012) on the CSR practice, Swart & Kinnie (2014) on the network working practice, and Khedhaouria & Jamal (2015) on knowledge management practice.  The articles selected to expand on these topics are, Kanzler, Niedergassel & Leker (2011) on the topic of knowledge management, Popa & Salanţă (2014) on the topic of CSR, and Melese, Lin, Chang & Cohen (2009) on the topic of network working.

Theory – Article Deconstruction

            The formulation of successful strategies on business ethics, HRM and IS practices in an organization represents an opportunity of success or failure depending on the approaches followed by the organization.  The successful side of CSR implementation, the ability of a company to effectively use external talent resources on innovation programs as well as the adequate use of own or externally sourced information creating innovation will be part of the intended expanded discussion of this article. Three articles in these business practices, will be deconstructed, analyzed and critically examined using the M.E.A.L methodology. The scholar articles on these three business practices; CSR (Popa and Salanţă, 2014), network working (Melese, et al. 2009) and knowledge management (Kansler, et al. 2011) are further discussed.

Corporate social irresponsibility (CSI) another way of looking at unsuccessful implementation of company’s social responsibility.

Vallaster, et al. (2015), brought few examples on how CSR can be badly damaged by corporate actions, one of the examples that clearly show a disconnect on CSR strategies was Toyota example of the Prius eco-friendly campaign contrasted to the company’s lobbying against tougher controls on car emissions.  Bringing this example, Vallaster, (2015) brought a concept of social responsibility failure that can degenerate into a corporate social irresponsibility (CSI) practice. Of course, taking this act form Toyota into a CSI level might be an exaggeration, but it is a real conceptual framework brought by Popa & Salanţă, (2014). This is the first concept expansion that is bring to this article; is there a substantial damage risk around a company’s mistake on public social image that could degenerate into a CSI?

Main points. Corporate Social Irresponsibility can be defined as the wrong side of adequate corporate ethic behaviors where wrongful and damaging business decisions managers take can damage corporations public image and their profits. Popa & Salanţă, (2014) intention was to bring the conceptualization of the topic in the hope their suggestions can help academics and company’s managers finding ways to reduce CSI and enhance CSR.

Evidence. Popa & Salanţă (2014) discussed several historical record evidences of CSI in the form of cases or anecdotal evidence from numerous scholar’s work. On definitions, they brought examples of prior authors concluding that CSI reflects company’s practices of irresponsibility such as corruption, bribery, environmental degradation, social injustice (Lange and Washburn, 2012, and Sanchez-Runde, Nardon and Steers, 2013). Popa & Salanţă, (2014) brought a plethora of oil business companies as examples of what in fact, started as CSR initiatives that clearly unmask an exchange of a good against wrong doing on their business practices. The examples brought by Popa & Salanţă (2014) were from Atlantic Richfield Company (ARCO), Lundin Petroleum, and British Petroleum, were wisely brought to illustrate the connection between CSR and CSI, where most of the time companies like these strategically bring CSR programs as a form of paying back against their business irresponsibility’s.

Analysis. The point brought by Popa & Salanţă (2014) on the practices of some companies on using CSR as a form of vindication against a wrong or misjudgment action, or on the other hand, how these wrong or misjudgment actions can lead to declare a company a CSI practitioner, were nicely substantiated by Popa & Salanţă’s article. One of the three examples brought by Popa & Salanţă (2014) was dramatic and is currently a big story at Europe particularly Sweden (ECOS report, 2010).  ECOS report brings accusations to Lundin Petroleum of misconduct and crimes against humanity as a result of their oil extraction operations in Sudan, Africa. This report triggers a criminal investigation by the Swedish Prosecution Authority which is near to conclusion. In a press release issued by Lundin Petroleum, they publicly notified about the Swedish Prosecution Authority declaration on the liabilities that the company is at risk as part of their investigation into past operations in Sudan from 1997 to 2003 (Lundin Press Release, 2018).  Lundin Petroleum denies the allegations and produced a report on their grounds of their business activities and Sudan with great emphasis on their CSR programs at this country (Lundin Report, 2016).  Even though this story is still at its climax and there is no resolution yet upon Lundin’s responsibility on this issue, one thing is true, this is a perfect example of how some companies pay one bad with one good in a CSI vs CSR context.

Linking. Popa & Salanţă (2014) paper was nicely written, providing the historical perspective of both CSR and CSI, with multiple examples of anecdotal and prior research nature of the CSI concept. The example they brought on the petroleum cartels where finely targeted for their purpose, as digging a little bit on them can bring light to the importance of the CSI topic, and that the need of research on it is certainly relevant and actual.

Open innovation networks in biopharmaceutical industry, a form of network working that focus on innovation

            Swart & Kinnie (2014) created a theoretical framework that explains the types of network working in two dimensions. The dimension of the modalities of network working expressed by their research activity, and the definition of a modeling framework for different levels of network working relationships. In this concept expansion, this article brings the involvement of Innovation as a main purpose of a network working approach around the academic and industry collaboration.  Melese et al. (2009) article provides a good description of the importance of academic and industry collaboration in the form of open innovation networks.  The M.E.A.L. analysis of this article should bring light to the question; are academic innovation networks a competitive advantage and growth factor in the biopharmaceutical industry?

Main points. Melese, et al. (2009) article main point was to provide a series of strategies intended to foster academic and industry collaborations that could bring transformative therapeutic procedures and products while overcoming the cost escalation of recent years.

Evidence. The evidence provided by Melese, et al. (2009) was based on case studies and anecdotal experiences and the research of others on the topic. Evidence is provided with data collected from industry and US FDA sources. They also provide information collected from interviews to industry and university members, nevertheless, specific information on the interview procedure was not provided by authors. With the information collected by Melese, et al. (2009) and others, Melese’s recommendations were targeted toward the optimization of the use of academic collaborations in the development of innovations for therapeutic solutions to heath issues.

Analysis. Melese, et al. (2009) started by establishing a series of models that industries are currently pursuing on collaborations with academia. Based on interviews with academic and industrial institutions, eight (8) distinct models were identified; one company-one investigator, one company-one university, one company supports a university consortium, one company support a university institute, industry consortium (pre-or noncompetitive), competition, venture capital investment and fee for services (Melese, et al. 2009, table 1, p. 503).  Each of these models have their advantage and disadvantages, and it was clear from Melese, et al. (2009) that additional work is needed to optimize the models, particularly in open innovation.

Open innovation (Chesbrough, 2002), is a concept where the full knowledge or part of the knowledge gain in a collaboration is shared with other academic institutions, researchers or even with other industry in the objective of advancing the knowledge base and spark additional innovation. This is easier say than doing as constrains exist from either the academic institution or industries in the biopharmaceutical industry.  From the industrial partners, pressure builds upon keeping information secret or safeguarded as part of their strategies on competitor’s blockage, through patents and trade secrecy of knowledge, whatever it is.  From the academic side, limitations to open innovation also comes from use of academic institution’s facilities, as they might have government-based restrictions on the ownership of intellectual property generated on their facilities. In addition, there are cultural aspects that need to be overcome, specifically in terms of the cultures and values of academics versus the culture and values of industry on tactical aspects of project management, knowledge sharing, publications and projects governance aspects like budget, staffing and other issues.

A series of recommendations or solutions are posted by Melese, et al. (2009) that served on their objective to facilitate and optimize academic collaboration with industry as follows:

  • Collaborative agreements:  Current trend on the preparation of collaborative agreements between academia and industry include the establishment of common governance structures that facilitates the interaction between academic and industry members.  These agreements include the basis for the bilateral exchange of information, creating project plans and management of resources through specific goals and objective milestones.
  • Establishment of public and private funded agencies: Another approach to facilitate the implementation of academic/industry collaborations is through the creation of government funded consortia for the development of fundamental knowledge base that is needed to further create innovation in the biopharmaceutical industry. The example given is the case of Academic Medical Centers (AMC) funded by the US National Institute of Health (NIH), through their Clinical and Translational Science Awards (CTSA).
  • Establishment of pre-competitive collaboration centers:  The purpose of such centers is to create the knowledge in basic questions related to biopharmaceutical innovation in areas like; building defined patients cohorts particularly in rare diseases, biological specimen banks, molecular and pharmacogenomic analysis, obtain molecular biomarkers, develop better clinical trials, elucidate disease pathways and improve decision making on drug candidates.
  • Creation of innovative research networks:  Melese, et al. (2009) proposed the creation of innovative research networks with three primary objectives:
    • Create collaborations with a perceptible value proposition: A value proposition for such network includes, lower cost structures, lower price to patients, better understanding of the molecular fundamentals of diseases and patient’s response to treatment.
    • Manage industry and academic collaborations as it would be an investment portfolio: effort placed on a program will have to be subject to milestones achievements that measure their ability for continuation or dismissal.
    • Adopt a new attitude about sharing information: an open innovation network by definition, which is what is intended with these innovation research networks, requires knowledge to be shared to be effective. To be able to do this in a competitive environment, the authors proposed the classification of information as proprietary and nonproprietary at initium. This would enable companies share their information without fear of setting at risk their competitive advantage.

Linking. Melese, et al. (2009) paper was nicely written, with a good reading flow and deep understanding of the topic. The authors showed superb expertise on the subject knowledge of academic collaboration, providing in depth analysis and recommendations on this industry trend. The description of open innovation network opportunities and how to reach these collaborations optimized, revealed clear domain of the topic by the authors. Although there was not much empirical data presented, the analysis done via interview basis was excellent based on the conclusions reached on their findings.

Digging deeper on knowledge sharing on academic collaborations and its quantitative relationship to cultural aspects.

Transitioning nicely from the previous article analysis and continuing into the third topic on IS related knowledge management, the article subject of this concept expansion touches both the knowledge management aspects and the network working aspects related to academic collaboration. This article from Kanzler et al. (2011), was a nice addition to this article as it consolidates these two subjects of interest.  The article added concepts not discussed by Khedhaouria & Jamal (2015), nor Lin (2015) articles in Unit 6. This article deconstruction will provide additional analysis of the impact of cultural differences between collaborating groups and how these differences impact innovation projects outcomes.

            Main Topic. The objective of Kanzler, et al. (2011) on their article was to provide evidence of cultural diversity impact on knowledge sharing in academic/industry collaborations, and how elements of culture as independent variables (“subjective norm”, “image”, “anticipated extrinsic rewards”) impact the dependent variable of “intention to share knowledge”.

Evidence.  The evidence provided by Kanzler, et al. (2011) was empirical and quantitative.  They performed a study on an R&D institute at a Chinese academic institution in collaboration with German scientist on the area of nanotechnology research. The study was conducted using a survey provided to the Chinese and German PhD’s and professionals working on a nanotechnology research center at China. An 80 % of the survey submitted were answered for a total of 43 surveys that split into 17 from Chinese respondents and 26 from German respondents. The survey questions were adapted from several previous researches to establish the relationship of the three independent variables of subjective norm, image, and anticipated rewards to the dependent variable of intention to share knowledge. The study quantification procedure used the Likert scale of five (5) points. The model uses the structured equation model procedure (SEM) establishing the relationship between each independent variable to the dependent variable for each experimental group, being the Chinese and the German survey respondents making comparisons of the resulting statistics to the total sample analysis.

Analysis.  The study analysis consisted on establishing the statistics for the hypothesis pairs of the previously mentioned variables using SEM methods. The hypothetical relationship of the variables was established by a careful examination of the behavioral literature of Chinese and German cultural constructs. Based on their literature analysis Kanzler, et al. (2011) established the following hypothesis for statistical confirmations:

  • H1: Subjective norm should have a larger and positive influence on the predisposition to share knowledge in Chinese group when compared to the German group
  • H2: Image should have a larger and positive influence on the predisposition to share knowledge in Chinese group when compared to the German group
  • H3: Anticipated extrinsic rewards should show equal results on the predisposition to share knowledge between Chinese and German groups

The interpretation of Kanzler, et al. (2011) of the survey statistical analysis indicated that there were no statistical significant differences between the Chinese and the German groups in either of the three (3) hypothesis test (Kanzler, et al. 2011, Table 1, page 13). Interestingly, the SEM results are in contradiction to the original base information that the authors brought in their preamble to the study design, specifically on the first two hypothesis. Some noticeable points on the data provided by Kanzler, et al. (2011) are:  

  • In the case of the subjective norm variable, the R2 showed that the percent variability explained by this variable relationship to the predisposition to share knowledge indicate that this model explains 85.6 % of the relationship variability. This is an indicator that the result obtained, which showed that there is no statistical difference between the Chinese and German groups with respect to this variable influence on predisposition to share knowledge (p < 0.01) is strong and should be taken confidently.
  • The other two study variables were weaker than the first one on the model ability to predict its relationship to the predisposition to share knowledge. The R2 value for the Image variable showed to be around 50 % capable of explaining the prediction variability, while the R2 of the anticipated reward showed only a 35 % capability of explaining the prediction, both data sets R2 are from the total sample analysis. In this regard although the authors may be able to draw adequate conclusions of the relevance of these two variables since for psychological based evaluations, having low values of R2 is normal, carefulness on conclusions might still be needed, provided that other means for the fit evaluation are not provided in the statistical evaluation, e.g. residuals analysis or root mean square of estimates (Minitab blog article, 2013).   

Despite the R2 data, let’s take Kanzler, et al. (2011) conclusions to be valid, the question raised is: Why the pre-determined behavioral and cultural constructs did not reflect the expected hypothesis statistical observtion for the first two variables?  The authors bring two possible explanations. The first explanation was that the PhD’s and professionals taking the survey were mainly young persons, which may represent a generational bias. The second explanation which might be more plausible was that rather than the individual’s culture, the predominant psychological construct might be influenced by the organization culture or expectations of the scientist alignment to the organizational expectations. The preamble to the study although proven by the empirical data to be contrarian by the results, showed a logic structure and their conclusions to prepare the hypothesis was fair. This indeed enforces the conclusions that these misleading cultural and phycological constructs are being challenged by strong circumstances, leading to the formulation of an appealing new hypothesis that requires additional empirical evaluation.

Linking. Kanzler, et al. (2011) article is well written, with a good reading flow and good understanding of the topic. The preamble to the study design showed good command of the prior cultural/psychological information. Regarding the exactness of the modeling aspect, the statistical analysis could had been improved by providing additional information for the model fit.

Case Study Application: Relevance of CSR, Network Working and Knowledge Management and Organizational Performance at Pfizer, Inc.

The information presented in the Unit 6 discussion and the extended discussion brought in this article with additional scholar research, indicates that CSR, network working, and knowledge management should have a significant impact on corporations that adequately play these business core components. Evidence collected at Pfizer, Inc. web page as well as external sources of information tend to indicate that indeed, Pfizer, Inc. masters the execution of adequate performance in these three business core component processes.

CSR at Pfizer, Inc.

            Pfizer’s corporate responsibility programs are design to strengthen public health, access to medicines, philanthropic effort of Pfizer’s colleagues, disaster recovery and other programs (Pfizer’s Corporate Responsibility Program, 2019). Pfizer had elevated their CSR programs as one of the four corporate imperatives for future success, being, innovation, capital allocation, corporate responsibility and culture (Pfizer’s Annual Report, 2017).  Some of the initiatives sponsored by Pfizer on their CSR program discloses amount of investment in this area, nevertheless, the total amount of investment on CSR program is not fully disclosed. For example, just in the employee’s community engagement program Pfizer, Inc. invested US $35 million in 12,000 nonprofit organizations. Based on the information provided in Pfizer’s web page Corporate Responsibility Program the amount investment in the community engagement program should be just a fraction of the total investment as could be appreciated by the many other programs including, product donation, and other means of support to these programs.

Network working programs at Pfizer.

            Pfizer, Inc. had established many collaboration agreements with private and public institutions as well as spinouts from university research groups that collaborate in the creation of new drugs and disease treatment programs. (Pfizer’s Research & Development Collaborations, 2019). The objectives of Pfizer, Inc. with respect to network working and collaboration are very similar to the proposal brought by Melese, et al. (2009) where academic institution, government and industry, in this case Pfizer, Inc. establish new ways of creating innovative solutions to health issues, overcoming the financial hurdles explain in Melese, et al. (2009) article in what they call an R&D ecosystem of the future. Dr. Jose-Carlos Gutierrez-Ramos explain in his video clip that Pfizer’s current strategy is shifting from aa model that is based on bringing talent and doing research work internally with targeted interaction with external researchers, to a model where other component of the biomedical community are collaborating with Pfizer with the objective of creating new drug developments. The new model brings academia, patients association, venture capital, and regulators in a synergy to establish new interactions to accelerate of new drug discovery, enable new forms of funding, exploring new science and achieving new drugs (Pfizer’s Research & Development Collaborations, 2019). Based on these collaboration agreements with spinout companies Pfizer, Inc. had nourished their pipeline in core therapeutic areas like, mRNA vaccines with BioNTech Collaboration agreement, cancer immunotherapies agreements with Kineta Laboratories, and other agreements in the line of academic, disease advocate consortium like the Autism drug discovery (Pfizer’s Research & Development Collaborations, 2019).

Knowledge management and knowledge sharing at Pfizer, Inc.

            Pfizer’s collaboration ecosystem programs are the best example of the company’s program on knowledge sharing, at least those that are made public (Pfizer’s Collaborations Ecosystem, 2019).  The collaboration ecosystem is formed by three main strategic approaches:

  • Research Collaborations; where Pfizer, Inc. is part of research centers, strategic alliances and consortia, like those discussed in the network working section.
  • Targeted Business Development; where Pfizer, Inc. invest on emerging companies developing products or technologies that are expected to enhance Pfizer product portfolio.
  • Innovative collaborations: This is a good example of using knowledge toward creating innovations, the Pfizer’s Center for Therapeutic Innovation (CTI) which is designed to bridge the gap between early scientific discovery toward drug development.

Pfizer’s case study summary and relevance to the three business core competencies

            Pfizer’s strategies around CSR, network working, and knowledge management are well integrated into Pfizer’s strategic imperatives. The CSR is one of the four company’s strategic imperatives, and network working, and knowledge management are fundamental components of the Innovation imperative. The approach of Pfizer on integrating the Ethics, Human Resources and Information Systems organization’s structural components into their Strategic Imperatives project this organization as a leader in the biopharmaceutical business sector.

            The financial strength of Pfizer, Inc. confirms that these strategies are successful strategies. Company financial growth, cost stability and steady profit increase, Table 1 shows a reproduction of Pfizer’s last three (3) years financial reports.  The following highlights can be taken from that report that are relevant to the impact of Pfizer’s strategic imperative impact on their financials (Pfizer’s Financial Reports, 2014 and 2017).

  • Revenues showed consistency year by year
  • The percent cost of sales is being kept constant 19.7 to 23.3 % of revenues
  • The percent cost of research and development is also being kept constant from 14.6 to 15.7 %
  • While net profit showed a dramatic increase from 13.7 and 14.2 % in 2016 and 2015 respectively to an astonishing 40.6 % increase in 2017, driven by income from continuing operations and taxes benefits on 2017 vs previous years.
  • Pfizer’s 2018-year end reports (Pfizer’s press release, 2019), showed that 2018 was in line to 2017 with respect to revenues with $53.6 B, and  cost of sales of $11.2 B, and R&D Cost of  8.0 B, but a reduction in net income to $11.15 B from $21.3 B in 2017, due to the unfavorable tax provision paid in 2018 vs the favorable tax benefit received on 2017. Still an increase of on an adjusted income based was experienced by Pfizer in 2018, for 17.9 B in 2018 from 16.2 B in 2017 (Pfizer’s press release, 2019).
Note: 1. Reproduced from Pfizer’s Annual Financial Report, 2018


The interrelationship between ethics, HRM and IS processes like CSR, network working, and knowledge management was shown to be important aspects of organization’s business model. The additional discussion provided with the added scholar articles included in this assignment article showed that there is still a lot to learn about these topics. The additional discussion enhances the importance of these processes and structural components for organizations. The case study of Pfizer, Inc. showed excellent correlation of a company strategic imperative adoption of the CSR, network working and knowledge management processes in their core business model and their impact on the company’s business results.


Chesborough, H.W. (2003). Open innovation: The new imperative for creating and profiting from technology. Boston, Mass. Harvard Business School Press.

ECOS report (2010).  Un-paid debt: The legacy of Lundin, Petronas and OMV in Block 5A, Sudan 1997 – 2003. Retrieved from: _DEBT_fullreportweb.pdf

Graves, C. (2017). Why every ad today feels political (even if it isn’t). Harvard Business Review Digital Articles. Retrieved from

Kanzler, S., Niedergassel, B. and Leker, J. (2011). Knowledge sharing in academic R&D collaborations: does culture matter? Journal of Chinese Entrepreneurship.  V 4 (1), 6-19.

Khedhaouria, A. and Jamal, A. (2015). Sourcing knowledge for innovation: Knowledge reuse and creation in project teams. Journal of Knowledge Management, 19 (5), 932-948.

Lange, D. and Washburn, N.T. (2012) „Understanding attributions of corporate social

irresponsability”, Academy of Management Review, Vol. 29, No. 4, pp. 300‐326.

Lin, H-F. (2015). Linking knowledge management orientation to balanced scorecard outcomes. Journal of Knowledge Management, 19 (6), 1224-1249.

Lundin history in Sudan (2010, October): retrieved from wp-content/uploads/2017/05/legacy-document_en.pdf

Melese, T., Lin, S. Chang, J.L. and Cohen, N.H. (2009). Open Innovation networks between academia and industry: an imperative for breakthrough therapies. Nature Medicine. V 15 (5), 502-507.

Minitab (2013, May 30). Regression analysis: How do I interpret R-squared and assess the goodness of fit [blog post]. Retrieved on 02-16-19. adventures-in-statistics-2/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit.

Pfizer Annual Report (2017). Retrieved from: _reports/annual_reports/2017/index.html?cid=vn_annualreport

Pfizer’s Collaboration Ecosystem (2019). Retrieved from: science/collaboration/collaboration-ecosystem

Pfizer’s Corporate Responsibility Program (2019, February 16). Retrieved from:

Pfizer’s Press Release on 2018 Year end financial results (2019). Retrieved from:

Pfizer’s Research and Development Collaborations (2019, February 16). Retrieved from:

Popa, M. and Salanţă, I. (2014). Corporate social responsibility versus corporate social irresponsibility. Management & Marketing. Challenges for the Knowledge Society. V9 (2), 137-146.

Sanchez‐Runde, C.J., Nardon, L. and Steers, R.M. (2013) “The Cultural Roots of Ethical

Conflicts in Global Business”, Journal of Business Ethics, Vol. 116, No. 4, pp. 689‐701.

Vallaster, C, Lindgreen, A., and Maon, F. (2012). Strategically leveraging corporate social responsibility: A corporate branding perspective. California Management Review. Retrieved from 0&sid=f17e062c-b6bf-4864-a248-d80ea90ab26e%40sdc-v-sessmgr03&bdata= JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl#AN=75273699&db=bth

Leave a Comment