Business Model Innovation in the Hospitality Industry

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Table of Contents

Summary
Context
Literature Review
Methodology
Results and Discussion
Conclusive Critical Reflection
References

Summary

The International Journal of Hospitality Management is a quarterly scientific journal that discusses significant issues and developments in various disciplines in the hospitality industry. The journal has been ranked as a Q1 journal by Schimago Journal & Country Rank (SJR) (SJR, n.d.). In 2019, the journal had an impact factor of 6.701, making it one of the most impactful journals in the hospitality industry (SJR, n.d.). The journal was published in the United Kingdom by Elsevier Ltd. This paper critically reviews Breier et al.’s (2020) article titled “The role of business model innovation in the hospitality industry during the COVID-19 crisis”. It analyzes the selected aspects and identifies relevant themes, theories, and study limitations.

Context

Breier et al. (2020) aimed to discover why hospitality firms can successfully recover from a crisis through business model innovations (BMI). The authors claimed that many hospitality industry firms were adversely affected during the COVID-19 crisis. They suggest that BMI can bring new opportunities to increase firm performance and help firms recover from a predicament. The researchers provide little but quality evidence to support BMI, possibly solving the hospitality industry’s current problem.

Literature Review

The article’s literature review is relevant to the study, and it allows the reader to understand its relevance and compatibility with previous research. The authors use recent studies to support the position that BMI can solve the hospitality industry’s current crisis. I agree with the literature review because the surveys are credible (most were peer-reviewed) and focus on the review’s central theme. The literature also supports and justifies the need for the research. The study was justified because the pandemic has affected many hospitality firms worldwide, and BMI is also empirically relevant in the industry. Moreover, the authors claim that research on BMI in the industry is scarce, further justifying the study.

The authors sought to answer two research questions; first, can hospitality firms use BMI to overcome the COVID-19 crisis? Second, what are the drivers of BMI, and what role do stammgasts play in the industry? The survey discussed the relationship between BMI, market turbulence, and firm performance. They postulate that BMI mediates the relationship between market turbulence (COVID-19) and an enterprise’s performance. The researchers hope to contribute to BMI’s antecedent current by highlighting its drivers by conducting this study. The study also contributes to the literature on stammgasts’ role in the industry.

Conceptual Framework

The researchers used a conceptual framework, which provided dictionary meaning and empirical findings to define the identified conceptualizations. The study’s main conceptions were crisis management, innovation, open innovation, and business model innovations (BMI). BMI is described as the insignificant changes in a firm’s operating business model (BM). Arguably, the study’s BMI definition is inadequate, considering that BMI is its central concept. The authors should have expounded on “insignificant changes” and the “business model element” to help the reader fully understand the concept’s scope. A BM was defined as a firm’s structure or configuration that creates and captures value through innovative ideas and technologies, which on their own do not provide any value. Innovations were described as anything that differs from the status-quo or usual business practices that can bring value to the company. I agree with this framework’s contributions; this conceptual framework highlights the relevant data’s scope limit by emphasizing specific variables. It further delineates the definite viewpoint adopted by the researchers in evaluating and interpreting the gathered information. There was no need for a theory because the conceptual framework was comprehensive.

Methodology

Research Design and Sample Setting

The researchers conducted a case study, particularly a multi-case research. I agree with the methodology used in the study because it is appropriate for the survey. A multi-case review is suitable for real-life situations where theoretical knowledge is limited. The authors indicated that theoretical cognizance of the issue was scarce due to little research. The ontological and epistemological assumptions have not been explicitly stated in the study. The researchers purposefully selected six hospitality firms that were adversely affected by the pandemic but showed recovery signs. The sample included one hotel, two bars, and three restaurants that depend on guests for income. This approach helped the authors draw similarities and differences among the selected cases. The chosen subjects had different ages (time between a firm’s creation and the present time in years). The authors conducted two interviews for each selected case: one with the business owner and the other with the stammgast. The interviews were semi-structured, which allow the interviewers to adjust the interview based on the respondent’s answers. The authors recorded the interviews with the respondent’s consent, and the data were triangulated with information from other publicly available data.

Although the study is well-designed, the survey is subject to a few limitations. First, the findings heavily relied on participants’ responses, making them subjective. A study conducted by Tempelaar et al. (2020) showed that self-reported research pieces are subject to bias and can cast doubts on the validity of measured constructs. This survey is particularly subject to such prejudice given that the interviews were semi-structured, which are considered self-reported instruments. Coupled with the fact that there was no randomization or control variable, this study’s findings can be ranked as level IV evidence. Apart from the possibility of bias resulting from the study’s design, the sampling technique is also a drawback. Purposive sampling is vulnerable to bias and is considered to have low reliability. According to Etikan et al. (2016), findings from a purposive sampling study cannot be generalized. Because purposive sampling is based on a researcher’s judgment, it is difficult to determine the sample’s representativeness objectively.

Analytical Methods

The study results were analyzed through a “within-case analysis” and “cross-case analysis.” The participant’s responses were first transcribed, and then each author independently read the transcripts and coded the interviews and archival data. The authors then iterated between data and theory in the coding process. The “cross-case analysis” was done to identify the similarities and differences in the selected cases and find common themes verified by interactive loops.

The analytical tests used were appropriate for the study’s design. Coding was utilized to analyze qualitative data by tagging and categorizing it and distinguishing common themes and relationships. The analytical method is relevant because the data was qualitative, and the authors also sought to identify common themes. The identified common themes were verified by interactive loops, a systematic technique that can decode qualitative data. The loop analysis is methodologically and conceptually problematic, but it offers a practical approach to analyzing qualitative data (Dhirasasna & Sahin, 2019). Although subject to limitations, this study’s statistical methods are useful and justify and validate the authors’ interpretation of their findings.

Reliability and Validity

The study addressed the reliability and validity of their findings. According to the researchers, the survey’s reliability and validity were assured by the fact that they used a multiple-case study and the iterative joint data consolidation and independent coding. The authors argue that because the research was a multiple-case study and used independent coding, their findings are reliable and valid. However, as previously mentioned, the selected research design and sample selection already invalidate its credibility.

Determining this study’s validity and reliability is critical in determining its quality and credibility. Since the measures and tests used to assess reliability and validity in quantitative studies cannot be applied to qualitative surveys, the researchers can use the criteria developed by Forero et al. (2018) to ascertain the qualitative survey’s credibility. According to Forero et al. (2018), a qualitative study’s validity and reliability can be determined through data triangulation, accounting for personal and sampling biases, and validating the participants and their responses (demonstrate participants’ thought processes clarity). Of these measures, Breier et al. (2020) triangulated their data with publicly available information. Also, the cross-case analysis added weight to the author’s findings. Therefore, it can be argued that the study’s findings are valid and reliable due to the data triangulation employed by the authors. I would have used a comparative case study rather than a multi-case cross-study. A comparative case study integrates quantitative and qualitative data and generates better evidence than a case study (Etikan et al., 2016). I would also improve on my sample selection technique to reduce error bias.

Results and Discussion

The study’s results showed that BMI could help hospitality firms to recover from the crisis. BMI drivers are under pressure to change to survive the crisis, time availability, and the role of stammgasts to facilitate BMI initiation. However, time availability is not sufficient enough to initiate BMI on its own. The BMI inhibitors include government support and high liquidity, making the firm want to maintain the status quo rather than change it. The second research question was negated: stammgasts are not BMI drivers. Instead, they can facilitate a firm’s decision to innovate by providing firm owners with the psychological safety needed to initiate BMI.

The researchers integrated these results into the literature review by explaining how these findings align with and support existing literature. The authors use the literature review to point out the agreements and disagreements between their conclusions and past studies. The study confirmed that government support could help firms overcome crises. However, they also identify BMI as a new strategy for overcoming a problem, which is their main practical contribution. BMI can generate firm revenue and help firms to prepare for future crises sustainably. Based on these findings, the authors recommend that hospitality firms adjust their business models continuously. They contribute to the existing theoretical knowledge by proposing a model to help firms survive crises. I agree with this theoretical conceptualization because it is evidence-based; other studies have supported this stance (Schomaker & Bauer, 2020). The researchers argue that a crisis can create a breeding ground for innovation. The need to survive the crisis will pressure a firm to innovate, which will trigger BMI.

Conclusive Critical Reflection

Although this study has methodological limitations, the evidence is convincing and justified. The findings contribute to the practice and research of business model innovation that can influence firm performance during a crisis. It is well-designed, and the conceptual framework is logical and adequately described. I agree with the authors that firms need to innovate to make it through a crisis. This stance is not only because of the evidence supporting it but because innovation is becoming a critical survival tool for business, with or without a problem (Schomaker & Bauer, 2020). However, I would use a different research design and select a different sampling technique to increase its credibility. I would use convenience sampling because the sampling error can be quantified. Alternatively, if purposive sampling is mandated due to circumstances, I would use the expert sampling technique as it has a lower error probability than other purposive sampling techniques (“Purposive sampling,” n.d.). Expert purposive sampling involves using a team of experts to select samples.

Agree

Disagree

I agree with the following elements in research:

Conceptual framework

The conceptual framework is logical and adequately described

Analytical methods

The analytical tests used were appropriate for the study’s design.

Findings

The study’s findings are valid and reliable because the authors used data triangulation, Data triangulation accounts for personal and sampling biases, and validating the participants and their responses.

Study’s validity and reliability

The study’s findings are valid and reliable due to the data triangulation employed by the authors.

Theoretical conceptualization

I agree with this theoretical conceptualization of the study’s finding because it is evidence-based. The authors relate use high-quality studies to support their explanations of the results.

I disagree with the following elements in research:

Research Design

I would use a different research design and select a different sampling technique to increase its credibility.

Sampling technique

I would use convenience sampling because the sampling error can be quantified. Alternatively, if purposive sampling is mandated due to circumstances, I would use the expert sampling technique as it has a lower error probability than other purposive sampling techniques.

Table 1: Summary of the analysis’s concept.

References

Dhirasasna, N. & Sahin, O. (2019). A multi-methodology approach to creating a causal loop diagram.Systems, 7(3), 1–36.

Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling.American Journal of Theoretical and Applied Statistics, 5(1), 1–4.

Forero, R., Nahidi, S., De Costa, J., Mohsin, M., Fitzgerald, G., Gibson, N., McCarthy, S., & Aboagye-Sarfo, P. (2018). Application of four-dimension criteria to assess rigour of qualitative research in emergency medicine.BMC Health Services Research, 18, 1–11.

Purposive sampling. (n.d.). Laerd Dissertation.

Schomaker, R. M., & Bauer, M. W. (2020). What drives successful administrative performance during crises? lessons from refugee migration and the Covid‐19 pandemic. Public Administration Review, 80(5), 845–850.

SJR – scimago journal and country rank (n.d.). Scimago Institutions Ranking.

Tempelaar, D., Rienties, B., & Nguyen, Q. (2020). Subjective data, objective data and the role of bias in predictive modeling: Lessons from a dispositional learning analytics application. PLOS ONE, 15(6), 1–29.

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