Research Philosophies
The two research philosophies covered within the current paper’s framework are positivism and interpretivism. Positivism can be defined as an approach to studying human behavior and societal trends with the help of scientific methods (Eden, Nielsen, and Verbeke, 2020; Quinlan et al., 2019). According to Ghauri, Grønhaug, and Strange (2020) and Saunders, Lewis, and Thornhill (2015), positivism is a research philosophy that is based on the idea that there can be certain social norms to explain human behavior and the overall impact of society on individuals. On the other hand, the aim of positivism is to discover the underlying contributors to people’s behaviors and explain the decision-making process as a mechanic or pre-set procedure (Bell, Bryman, and Harley, 2018; Myers, 2019). It is much more common for positivist studies to feature quantitative research methods, such as questionnaires and surveys, where the results are later analyzed with the aid of statistical tools.
On the other hand, interpretivism can be defined as an approach that is much closer linked to motives, beliefs, and intentions. According to Gupta and Gupta (2020), an interpretive study would be reasonable to implement when the business reality has to be re-assessed with the purpose of predicting certain behaviors. From the point of interpretivism, all individuals are perceived as complex creatures with different experiences (Frias & Popovich, 2020; Saunders, Lewis, and Thornhill, 2015). Therefore, the core idea behind interpretivism as a research philosophy is that all people view reality in different ways. The fundamental aim of interpretivism is to understand the reasons behind specific individual behaviors (Bougie & Sekaran, 2019; Hair, Page, and Brunsveld, 2019). In order to collect information and remain in line with the interpretivism research philosophy, the scholar would utilize qualitative methods, such as unstructured interviews or observation.
Based on the evidence above, it can be concluded that positivism and interpretivism can be successfully utilized to study behaviors and decision-making within diverse business environments. Nevertheless, it should be remembered that positivism is somewhat restricted by the existence of social norms, while interpretivism studies could feature non-scientific methods. The complexity of business environments is underlined by the fact that individuals are perceived differently based on the methods used to collect and assess the data.
Critical Appraisal of Research Methods
Exploratory Research Methods
In the article written by Liu and Harwood (2019), one could witness the inductive approach to research, as the work offers research discretion and capitalizes on it throughout the scientific work. Based on the description of the methodology, it can be concluded that there is a relatively high degree of flexibility attained by Liu and Harwood (2019) when going through all of the essential research processes. Correspondingly, it could be considered a benefit that the researchers took a relatively small group of respondents to investigate the research issue at hand. The inductive approach to research can be witnessed in how the academics determined their study design and collected the data (Liu & Harwood, 2019). The attempts to develop creativity taken by Liu and Harwood (2019) turned out to be successful because they were guided by the willingness to concentrate on authenticity and utilisation of obtained results. Throughout the study, the inductive approach to research becomes more evident because Liu and Harwood’s (2019) exploratory investigation adds to the existing literature. Hence, it could be an early study becoming the first step toward a comprehensive causal investigation.
From the organizational point of view, exploratory research could become a valuable contribution to the overall functioning because no management unit wants to invest time and resources in something inefficient. This is why studies similar to the one conducted by Liu and Harwood (2019) are carried out to facilitate investigations on previously under-researched topics. While learning about the issue of driving creativity in marketing students, authors of causal studies could look back at all the collected qualitative evidence to find the optimal ways to carry out their unique research.
Causal Research Methods
The deductive approach to research was taken by Lou and Yuan (2019) in their causal investigation on the topic of social media messages and their impact on consumers and brand image. As a causal study, it was focused on expanding the existing knowledge on social media communication between brands and consumers learning about those brands from influencers. The degree of statistical strength of Lou and Yuan’s (2019) article can be described as average. In order to avoid any significant errors, the researchers took on modeling and prediction methods and tried to carry out an in-depth research project that might help them identify the biggest issues bridging the gap between consumer trust and communication styles employed by brands and influencers on social media (Lou & Yuan, 2019). With various estimations at hand, the academics also gained access to the biggest contributors to the problem and verified the most likely causes impacting the degree of consumer trust.
One of the biggest benefits that Lou and Yuan (2019) were able to exploit when conducting their study was the ability to achieve a high level of flexibility in sources under review. Surveys completed by study participants were utilized to drive additional discussions on how personal characteristics could influence consumer trust during social media interactions with famous brands. The deductive approach to research also helped Lou and Yuan (2019) achieve their goal of discovering that influencer trustworthiness was one of the key links between consumer trust and brand awareness. As a relatively large, formal study, it also took on the question of utilizing statistical methods to predict the impact of influencers on consumer trust (Lou & Yuan, 2019). In other words, the marketing and sales implications were considered deductively, paving the way for even larger studies on the subject that might follow the same methodological path.
Compared to the study conducted by Liu and Harwood (2019), Lou and Yuan’s (2019) investigation features significantly more statistical elements. The researchers present their findings as concisely as possible to focus on the statistical significance of influencer-generated brand advertising. Even though it was not a large-scale investigation, it provided the authors with an opportunity to predict influencer and consumer behaviors while looking into how mistakes in communication could be avoided (Lou & Yuan, 2019). In the future, strategic planning and the deductive approach to research could be utilized to investigate other areas of influencers’ impact on social media.
Research Design in Relation to Research Methods
The exploratory approach was employed by Liu and Harwood (2019) in order to collect and investigate a vast amount of qualitative data on student-centered strategies. This method of sampling and data gathering allowed the researchers to set up focus groups and then learn more about their insights into the creativity module with the help of interviews. Thus, the research conducted by Liu and Harwood (2019) could be considered a case study approach mixed with a phenomenological study. Individuals from the academic and firm teams were both involved in the module. The exploratory approach taken by Liu and Harwood (2019) could be supported by the deployment of the method of content analysis. It allowed the researchers to gain additional insight into how marketing research could be improved.
On the other hand, there was a correlational design employed by Lou and Yuan (2019) when completing their causal study on the topic of the connection between social media influencers and audience responses to branded content posted by those influencers. The partial least squares structural equation modeling and covariance-based structural equation modeling were utilized by Lou and Yuan (2019) to test the research hypothesis. Therefore, the causal study on social media influencers and the promotion of branded content could aid the researchers in terms of developing a new theory aimed at closing the gap between consumer trust and brand awareness (Lou & Yuan, 2019). The correlational design might have been employed by Lou and Yuan (2019) to see if branded advertising could be utilized by influencers to attract more consumers to their own personal brand and create a larger follower base.
Critical Ethical Issues and Research Limitations
One of the limitations of the study conducted by Liu and Harwood (2019) was that the researchers did not focus on just one perspective. Instead, they decided to address both the solution as a practice and the process of developing that creative solution (Liu & Harwood, 2019). Accordingly, there were little to no differences in regard to the opinions expressed by the firms participating in the study. The ethical issue that stems from this limitation is that the organizations expected to see students adopting specific solutions instead of focusing on developing something new. This relative conflict of interests also gave rise to another limitation related to how the focus on the process averted students from gaining fluency and skills (Liu & Harwood, 2019). It was unclear if adaptability was prioritized over conventional solutions or vice versa. Overall, study participants were deprived of the opportunity to recognize failure as a component of learning and build resilience.
The biggest limitation that affected the research conducted by Lou and Yuan (2019) was the presence of general attitudes only. In other words, the researchers collected information regarding influencer-generated branded posts and the degree of trust end-users had in the brand being advertised. There was no specific approach employed to find out what kind of beliefs and attitudes social media users held about influencer-generated branded posts since there were too many different platforms and significant content variations used by influencers (Lou & Yuan, 2019). The possible ethical issue here is the presence of conclusions that are based on relatively insignificant statistical outcomes achieved for influencer-generated content and brand awareness. Thus, end-users responses to social media posts cannot be considered dependent on influencer communication strategies just yet (Lou & Yuan, 2019). Future studies on the topic should take a bolder approach to control the participants’ knowledge regarding social media influencers in order to achieve objective and statistically significant results.
Reference List
Bell, E., Bryman, A. and Harley, B. (2018) Business research methods, Oxford University Press.
Bougie, R. and Sekaran, U. (2019) Research methods for business: a skill building approach, John Wiley & Sons.
Eden, L., Nielsen, B. B. and Verbeke, A. (2020) Research methods in international business, Palgrave Macmillan.
Frias, K. M. and Popovich, D. (2020) ‘An experiential approach to teaching mixed methods research’, Journal of Education for Business, 95(3), 193-205.
Ghauri, P., Grønhaug, K. and Strange, R. (2020) Research methods in business studies, Cambridge University Press.
Gupta, S. L. and Gupta, H. (2020) Business research methods, Tata McGraw-Hill Education.
Hair, J. F., Page, M. and Brunsveld, N. (2019) Essentials of business research methods, Routledge.
Liu, W. L. and Harwood, T. (2019) ‘Practising creativity to develop students in marketing’, Student Engagement in Higher Education Journal, 2(3), 54-76.
Lou, C. and Yuan, S. (2019) ‘Influencer marketing: how message value and credibility affect consumer trust of branded content on social media’, Journal of Interactive Advertising, 19(1), 58-73.
Myers, M. D. (2019) Qualitative research in business and management, Sage.
Quinlan, C. et al. (2019) Business research methods, South Western Cengage.
Saunders, M., Lewis, P. and Thornhill, A. (2015) Research methods for business students, Pearson Education.