ADVERSE ASPECTS OF WEWORK’S BUSINESS MODEL

Answer

• Introduction: Background Information

WeWork is a multinational firm specialized in providing flexible shared workspaces for other companies. The firm has extensive investments in the commercial real estate industry where it leases cheap buildings on a long term contract, designs it, and creates a virtual and physical shared offices and spaces that can be rented out on flexible terms to freelancers, technology startups, and individuals wanting temporary office spaces (Gauger, Pfnür, & Strych 2020). The company was established in 2010 and it’s headquartered in New York. According to self-reported figures, WeWork had a total of 4 million square meters of space to rent out by end of fiscal 2018. The company operates a relatively new business model. Some commentators have describes WeWork’s business model as a modified form of sharing economy company that specializes in providing office rental and technical working areas. Because of this model, the business has grown significantly and now operates in 120 cities around the world. Nonetheless, the company faces many challenges. Some of which have been brought to the mainstream following the failed IPO in 2019. A follow up from the Wall Street Journal has raised concerns over WeWork’s inflated valuation of its value, flawed business model, poor decision making and bad governance, and mismanaged financials and operations (De Peuter, Cohen & Saraco, 2017). These shortcomings have affected the main elements of the company’s business model which are; customer relations, value proposition, channels, key partners, key activities, key resources, revenue and costs. In turn, these impacts have reduced WeWork’s market performance, organization development, and financial performance. It is upon this backdrop that this research report conducts a qualitative analysis of the company. It uses interview content collected from WeWork managers to establish the negative aspects of WeWork’s business model and how these aspects impacts the development of the business.

• Justification of Choosing Qualitative Research

There are multiple differences between qualitative and quantitative researches. Yates and Leggett (2016) note that quantitative research focuses on collecting numerical and statistical data while qualitative research uses non-numerical data. Quantitative data is characterized by the use of graphs, numbers, and mathematical formula. Quantitative researches are more objectives and can be used to establish facts that are generalizable. Some of the common quantitative research methods include observations recorded using numbers, surveys using close-ended questions, online polls, systematic observations, and experiments. Quantitative research is used to quantify measurable data. The data is then used to uncover patterns or formulate facts. According to Aspers and Corte (2019) the methods for collecting quantitative data are more structured compared to those for collecting qualitative data. The standardized procedures for quantitative research are intended to help derive uniform numerical findings even if the process was repeated over and over, without variations and margins in the numerical data collected.

Qualitative research is also known as exploratory research as it is used to evaluate and understand underlying opinions, motivations, and reasons. For this research paper, the qualitative research method is preferred because it enables collecting non-numerical data on the underlying reasons for the adverse aspects of the WeWork’s business model. Collecting the data will provide a deeper insight into the problems and guide the formulation of inferences and recommendations. The data will be collected using secondary or desk research. However, it is assumed that the data collected was harnessed from interviews with managers from the WeWork Group. Apart from interviews, qualitative data could be collected using focus groups or group discussions, and observations. Kopf et al. (2016) summarizes these findings noting that the key differences between quantitative and qualitative research methods are categorized in terms of objectives, questions, sample, data collection, data analysis, and outcome. For quantitative research, the objective is often to examine variables by testing hypothesis. This makes the research deductive.

On the contrary, qualitative research involves identifying variables by generating hypothesis. This makes the qualitative research inductive or heuristic in nature. The nature of questions under quantitative research are specified before data collection while for qualitative research, the questions are specified gradually as the research progresses. The sample data for quantitative research is normally larger while qualitative research uses a small sample. The data collection for quantitative research is structured whereas it is less-structured for the qualitative research. In regard to data analysis and outcome, quantitative research uses statistical analysis to make generalized findings while qualitative data uses non-statistical analysis to make un-generalizable findings (Bryman, 2017). This research prefers qualitative research because it is simple to apply and perfectly aligns with the goal of the paper which is, using a small sample and less structured data collection derived from interviews to make un-generalized findings on the adverse aspects of WeWork business model.

The semi-structured interviews are advantageous to this research in the following ways. First, Maxwell (2019) notes that this type of interviews are used together with qualitative research. The entail conducting dialogue between participants and researchers guided by flexible protocols for interviews often supplemented by follow-up probes, questions, and comments. This approach enables the researcher to collect open-ended data that highlights the thoughts, beliefs, and feelings of the respondents. The main advantages of semi-structured interviews include its flexibility since it allows the researcher to ask open ended questions thus collecting in-depth information. Secondly, the interviews encourage face two face and two way communications that are important in assessing the mood and attitude of the respondents at each stage of the research process. Another advantage of semi-structured interviews is that they can be customized or uniquely tailored to suit specific respondents. For instance, the researcher can change the nature of the questions to focus more on areas of interest to the research. This advantage is exacerbated by the ability for the interviews to trigger deep conversations that enable the collection of usable data.

• Data Analysis Techniques (Spiggle’s 7 Step Method)

Spiggle’s formulated the 7 step model to guide the data analysis process. In the case of WeWork this model will be used to examine the qualitative data collected from the 7 managers interviewed. The main steps in this model are categorization, abstraction, comparison, integration, dimensionalization, refutation, and iteration. These activities are not discrete and do not occur chronologically or in a sequential manner. These steps are aimed at helping a researcher to sort, organize, extract, and derive meaning from a set of data. The analysis is important in guiding the conclusion.

Categorization

Categorization denotes the process through which data is labelled or classified. Data has to be categorized during the coding process. Fjeldstad and Snow (2018) explains that categorization is important since it enables the researcher to identify chunks of data as either representing, belonging to or being part of a general phenomenon. The process entails giving labels or naming of phenomenon found in a given data set. The part of the data that is not meaningful to the research is often left uncategorized. Coding eases the organization and retrieval of data since coded data with similar characteristics. The process of categorizing is done deductively by locating passages representing priori constructs, ideas, and themes. It could also be done inductively by identifying categories of data that are emergent.

Abstraction

Abstraction is used to build on the foundation formed after categorization. It is more detailed than categorization since it collapses empirically grounded categories into conceptual constructs of higher order. It goes beyond the initial processes of identifying patterns in a set of data. For instance, it groups previously categorized data into conceptual and general classes. According to Cheah and Ho (2019), abstraction involves incorporating categories that are more concrete into fewer yet more general categories depending on the pattern of codes. Abstract constructs have to encompass several concrete instances that are found in a set of data sharing common features. This sentence implies that the theoretical significance observed in a construct emerges from its relationship to other constructs with similar features.

Comparison

This step explores the similarities and differences across incidents within already collected data and presents the researcher the opportunity to collect additional data that fills in an identified research gap. For researches using systematic comparisons, the researcher has to introduce principles of logic to direct the making of inferences. The comparison of data begins during the preliminary stages of analysis, namely when undertaking categorization and abstraction phases. During categorization, the researcher identifies similarities in empirical instances in labels and data. It is noted that the process occurs unsystematically and implicitly as the researcher explores the initial data. As the analysis process progresses, the researcher begins conducting comparisons in a methodological manner.

When comparing data that is currently collected, it is important that the comparison process begins during the initial stages when the researcher is categorizing and abstracting the data. This exercise will enable the researcher to identify similarities in certain empirical instances. Spiggle (1994) proposes that this stage could use the constant comparative method pioneered by Glaser and Strauss. This method guides the researcher in making explicit comparison of each of the identified incidences in the data. At this stage, the researcher is advised to compare the incident data against emerging categories of data and not against other incidents. While making the comparison, the data has to be displayed in templates that align constructs and categories in matrices. The second phase of this step is data collection. The comparison process could enable the researcher to identify the need for subsequent data collection. At this point, a researcher uses either theoretical sampling or purposive sampling to select the people to interview or the data sets to observe. This process enables the researcher to collect data on variables of interest.

Dimensionalization

This step entails identifying properties of constructs and categories. After identifying a category, the researcher can then explore the key characteristics and attributes along a defined dimension. Dimensionalization is best done by constructing graphical representation of data. Spiggle (1994) adds that this step is further associated with sacralization which is the process where people attach an element of sacredness to an object. Dimensionalization is an important step since it enables the researcher to construct theories in two ways. First, through systematic exploration of empirical variations noted across incidents that represent constructs. Second, through identification of properties and dimensions that permit the researcher to define and explore relationships across constructs and categories.

Integration

Integration is the process of building theories based on grounded data. This process goes beyond identifying themes and propositions to generating complex, conceptually woven, and integrated theory. The theories have to be formulated in a progressive and developmental manner in a way that they have a close conjunction with the analyzed data. Integration can be facilitated using two operations; selective coding and axial coding. These two operations help in integrating constructs and categories defined by the analyst. Spiggle (1994) explains that axial coding involves developing constructs or categories using the paradigm model where the researcher specifies conditions that give rise to the context. On the other hand, selective coding entails transposing to a higher level abstraction using the developed paradigmatic constructs, delineating core categories, and specifying relationships around which other constructs and categories revolve and relate. Spiggle (1994)  adds that categorization, comparison, abstraction, and integration are considered to be fundamental steps as they enable basic analytical functions and operations. In addition, they enable a researcher to construct conceptual frameworks that are coherent. Dimensionalization helps in clarifying both the abstraction and comparison steps.

Iteration

Iteration is the process through which the researchers moves through data collection, coding, and analysis in a way that previous operations shape ensuing ones. Iteration does not require researchers to perform specific research functions or move to a particular stage but rather, it encourages moving back and forth between the previous stages. As a result, iteration could occur between data inference and data collection phases of the research process or even during the inference phase.

Refutation

This is the last step of this qualitative framework. Spiggle (1994) explains that refutation entails subjecting a person’s emerging inferences, constructs, propositions, categories and conceptual framework to empirical scrutiny. The three common types of refutation applied to consumer research are; testing by context, purposive sampling, and negative case analysis. Negative case analysis is the process through which a person intentionally seeks specific cases disconfirming their emerging analysis. It is important to distinguish negative incidents from negative cases. According to Spiggle (1994) refutation could be a significant part of the inference.

• Importance of Each Step in Analyzing WeWork

Each of the seven steps is important in analyzing the adverse aspects of WeWork’s business model. The first step is categorization. In the case of WeWork, this step requires that the data collected from the 7 managers is classified or labelled. The resultant data is then coded to represent part of a key phenomenon. This process involves labelling phenomenon found in the collected data. These steps were done to enable the formulation of table 1 below. Categorization step led to abstraction which was used to present more details on the conceptual constructs made during the first step. For the case of WeWork, the data collected was grouped and categorized into general classes. This process further involved incorporating categories that were concrete into narrower yet generalized categories depending on the patterns depicted in the codes. Abstraction guided the researcher into the third step which is comparison. At this point, the data collected, coded, and categorized was further subjected to a critical analysis to identify differences and similarities. The incidents reported in the raw data were identified. Systematic comparisons were made.

Given that the research involved conducting systematic comparisons, the researcher was obliged to use logical principles to direct the process of making inferences. Surprisingly, it was observed that the data comparison process begun during the categorization and abstraction phases. The comparison step enabled comparing current and subsequent data collection. For instance, subsequent data collection was necessitated by the gaps identified after the current data was analyzed. For WeWork, the subsequent data was collected using purposive sampling where managers were selected randomly to provide the needed information on the important aspects of the business model applied by WeWork. The dimensionalization step was applied to the qualitative data collected on the organization of interest. The step involved identifying properties of categories and constructs. When the researcher identified categories, it became easier to explore and understand the correlation between key characteristics and attributes that the productivity of the business model adopted by WeWork.

For this paper, dimensionalization was tabulated, instead of generating graphical representations (table 1 below). This step enabled the researcher to compare the findings to theories on business models and their impact on organizational performance.  Key focus was directed towards understanding how the business model affects customer relationships, value proposition, key partners, channels, key resources, revenues, and key activities (Ritter & Lettl, 2018). Formulating the theories was reinforced by the integration step as it guided the identification of themes. While performing the previous steps, the researcher engaged in iteration which is the process of moving through data collection and analysis in a manner that influences ensuing inferences. The last step was refutation. According to Patnaik (2019) this step involves subjecting the findings made to empirical scrutiny. For data collected on WeWork, refutation was done using negative case analysis. Using this approach, the researcher purposefully specific cases that disconfirmed their emerging analysis.

• Summary of the Results

The results collected and analyzed using Spiggle’s 7-step method are summarized in table 1 and 2 below.

Table 1: Adverse Aspects

Table 2 below results from the integration of the aspects highlighted in table 1 above. These aspects affect WeWork’s market performance, organization development, and financial performance as elaborated below

Table 2: Impact on Development

Ethical Consideration

Some of the ethical principles considered for this research include obtaining informed consent from the respondents, minimizing the risk of harm to the managers involved in the research study, protecting confidentiality and anonymity of the research participants, and giving the participants the options to be involved in the research (Holmlund, Witell & Gustafsson, 2020). It was equally important to inform the respondents that their research would be used in assessing the business model of WeWork. At an individual level, the research made sure that the analysis was objective and devoid of subjective ideologies. This means the researcher avoided adopting deceptive practices that would alter the findings and inferences made.

• Discussion and Conclusion

This paper presents a critical analysis of the adverse aspects of WeWork’s business model. This research is necessitated by a desire to identify the reasons behind the failed IPO and plummeting performance of the organization of interest. As a result, 7 managers were involved in a qualitative research that enabled collection of war data. Interviews were administered to collect the data which was analyzed using Spiggle’s 7 step method. Using this method, the data was categorized into; customer segments and relationships, value proposition, channels, key partners, key activities, key resources, revenue stream and cost structure. Categorization was important in identifying key features that would enable better understanding of the business model. The items selected for categorization represent the key elements of a business model. The second step of the model is abstraction which entailed redefining the categories. A comparison of the categories was made thus allowing the researcher to explore similarities and differences in consequences and incidences. Dimensionalization led to a more detailed dissection of the coded data by classifying it into constructs, properties and dimensional range. These findings were used for integration where the categories were aligned with descriptions to better understand the data and how it is represented. While undertaking these steps, the researcher engaged in iteration with the goal of analyzing the research and using the findings to redefine the whole research process. A critical analysis of the findings summarized in table 1 and table 2 calls for refutation against WeWorks business model. In line with the findings, the following recommendations are made.

First, the company needs a new business model that will enable it to commercialize on its resources without worrying about the stability of its revenues. This recommendation is backed by the reality that the failed IPO was a result of extensive scrutiny of WeWork’s books of accounts which showed that the firm’s business model is unsustainable in the long term. In return, the management opted to falsify its financial information to conceal this reality. The second recommendation is that WeWork should improve its organizational development by hiring experienced and ethical leaders and managers, effectively managing its finance, avoiding negative publicity, and leveraging itself against possible pandemics.