This paper annotates a mixed-method research article published in the International Journal of Innovation Management in 2020, in volume 24, issue 2. One can access it via Research Gate, and its online publication does not have page numbers. The annotated article’s title is a Business model reconfiguration and innovation in SMEs: A mixed-method analysis from the electronics industry. The authors of the work include Thomas Clauss, Ricarda B. Bouncken, Sven Laudien, and Sascha Kraus. The scholars used quantitative surveys and follow-up detailed interviews in their study to identify different types of business model reconfiguration and their implications.
The article aims at closing the significant research gaps in the field of business model innovation studies. Rather than focusing on the successful examples of business model innovation conducted by large corporations, they choose to focus on small and medium businesses. Moreover, the authors choose to study business model reconfiguration (BMR) supposing moderate modifications since small and medium entrepreneurs (SMEs) might not have enough resources to reshape their whole business models. The scholars’ objectives are to identify and describe the variation among business model reconfigurations and understand the BMR’s impact on SMEs’ performance. At its core, the research design is causal since it seeks to establish a cause and effect relationship between the types of business model reconfiguration and SMEs’ success. The study follows two theoretical models: the business model and BMR. The former they define as a structural model of business functioning consisting of value creation, value proposition, and value capture (Clauss et al., 2020). BMR implies adjusting at least some of the three business model components (Clauss et al., 2020). The authors conclude that changing all three is more effective than partially altering them.
Methods & Empirics
The mixed-method that the scientists used in their study involved a quantitative cluster analysis based on a survey and qualitative interviews within the identified clusters. The survey included several measures assessed by the SMEs’ employees, such as new capabilities, new technology, new partnerships, new markets, new channels, new revenue models, and performance. The informants could evaluate these measures based on five-point Likert scales. The researchers based this part of their work on Clauss’s framework; they also used it in their previous BMI study. They rigorously and thoroughly developed and tested the framework, which appeared to have a non-biased scale, and proved readable and valid. In addition to the questionnaire, the scholars measured the environmental turbulence and the SME’s size per Jaworski and Kohli’s framework. Then they evaluated the degree of the firm’s performance in comparison to the other businesses in the market per Venkatraman and Ramanujam’s scale. The next step was to use a three-step classification to divide data into clusters by identifying outliers, employing Ward’s hierarchical clustering, and non-hierarchical K-means clustering. As for qualitative methods, the scholars utilized semi-structured interviews with CEOs selecting multiple cases per cluster.
The study centered on German, American, Chinese, British, French Italian, and other SMEs working in sensor, measuring, and testing technologies. The data sample constituted 216 firms employing fewer than 500 people and having not more than 50 million euros in turnover. The authors selected the target population from the participants of the Sensor & Test fair in 2014. They collected data in person by directly addressing the firms’ strategic top management personnel during the sensors’ event. To follow up, the scholars came to the businesses and conducted interviews with 13 CEOs selected per purposeful sampling. The researchers recognize that selecting a target population on a business exhibition could be problematic and non-representative. However, they argued that the Sensor & Test fair was the most significant event in the sensor, measuring, and testing market; thus, it was necessary for most business to business firms to build connections. Therefore, the sample must have been representative of the industry as a whole. The measuring instrument used as a correlation marker to test for potential common method bias was the firms’ ethical customer interaction. All of the measures satisfied the authors’ methodological testing.
To ensure the reliability and validity of the research, Thomas Clauss, Ricarda B. Bouncken, Sven Laudien, and Sascha Kraus began by testing the survey’s components’ reliability, validity, and unidimensionality. They evaluated indicator and composite reliability and discriminant validity. Three of the measures did not meet the cut-off threshold of indicator reliability, but they satisfied the other requisites. They were kept in the survey since they benefited composite reliability. The scholars also checked the constructs for the Fornell-Larcker criterion and average variance extraction. When some of the measures did not meet the criterion’s requirements and average variance extraction requirements, the authors tried alternative models. After unsuccessful trials, they eventually returned to the initial one, which was flawed but the most accurate among the tested options. The researchers analyzed data employing the correlation matrix, linear regression analysis of the cluster variables, and normality testing. They did not find multicollinearity or deviations from normality, and most of the inter-measure correlations were within the normal range as well. As for the cluster analysis, the authors checked their validity and reliability via cross-validation, showing that the clusters differed across all variables.
It is always fascinating to read the articles combining qualitative and quantitative methods of analysis since they manage to find a balance between the two opposite schools of thought. he study by Thomas Clauss, Ricarda B. Bouncken, Sven Laudien, and Sascha Kraus represents a large portion of the technology firms’ market. At the same time, it details in depth the specifics of business model strategies of the few selected case firms. This level of general accuracy and particularity is only achieved through employing survey-based quantitative cluster analysis together with interview-based qualitative cluster case studies. What is more, the researchers managed to sample a representative target population from a single location, which united various global SMEs from the selected industry. This approach to sampling was very efficient and showed proper planning and in-depth knowledge of the industry’s nature by scholars. Notably, the researchers also omitted to standardize cluster variables, since they based all of the measures on the same scale. It seems vital to save time conductive extensive research; thus, the efficiency of the article’s authors is exemplary and should be followed by other academics.
Another essential thing to consider is the research paper’s structure and its compliance with the academic norms. Thomas Clauss, Ricarda B. Bouncken, Sven Laudien, and Sascha Kraus presented an appropriately formatted paper. The scientists did not resort to excessive citing, but when they referred to other authors’ ideas or frameworks, they always cited them according to the APA style requirements. Even though the researchers reside in Germany and likely use German as their primary language, the article follows the standards of American academic writing. The paragraphs are well-structured and cohesive; the paper is logically organized and divided into several parts. Moreover, the paper’s presentation is immaculate: it is clean, carefully proofread for any logical, grammatical, or style mistakes, and has all the graphs and tables necessary to fully understand the research process and its results. Generally, it appears that the article meets all the standard expectations from the papers in the field of innovative business management research. Furthermore, the paper’s advantage compared to other works on this subject is its mixed-method approach, which allows for greater depth and better generalization than either qualitative or quantitative research separately.
Clauss, T., Bouncken, R. B., Laudien, S., & Kraus, S. (2020). Business model reconfiguration and innovation in SMEs: Amixed-method analysis from the electronics industry. International Journal of Innovation Management, 24(2). Web.