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4. RESEARCH APPROACH

4.3. Research Method

The paper uses both primary as well as secondary data, offline and online sources in order to answer the research questions. The reason for this choice is embedded in the fact that sharing economy is a relatively understudied field of innovation (Martin and Upham, 2015), specifically the data for the developing countries is very limited, and hence the theoretical research and resources available are restricted from the respective context. In order to overcome the resulting gaps, the researcher employed the use of interviews from the respective market, in order to gain a more practical and realistic insight on the situation.

4.3.1. Secondary data collection

This thesis makes extensive use of secondary data to set the direction of the paper and to feed the strategic models applied. Secondary data comprehends information summarized from data sources developed for other purposes than the research problem of this paper (Creswell, 2013). The paper uses

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both offline and online data sources including books (e.g. Hofstede et al. 2010, Malhotra et al., 2017), academic papers, governmental publications and websites (PTA (Pakistan Telecommunication Authority), PBS (Pakistan Bureau of Statistics), market reports (PWC, 2014; McKinsey, 2019), credible blogs (Hofstede), global ranking reports (e.g. UNDP world happiness report, World Bank Doing Business 2020 report, UNDP human development report, Global Information Technology Report 2016, rule of law index etc), research publications (e.g. Moody’s 2019) journals, news articles, news and information related sites such as CNN, Bloomberg, Gulf News, Forbes, Business Insider etc as the secondary data sources. The secondary data is used to form the basis to answer the research questions, and to analyze the strategic models used in this study.

The disadvantages of using secondary data are that the data are not collected for the purpose of this study which means the researchers have no real control over the data quality, validity and reliability of the secondary data sources, being limited to what is available and published by the authors of these sources (Saunders, Lewis and Thornhill, 2009). However, for all the chosen secondary data, a thorough investigation of the data sources was conducted to make sure that the information needed was consistent with the data from the sources and by this, ensuring validity. For instance, secondary data sources were selected not only for the relevance of its content, but also in relation to the reliability of its source, including the number of citations and rating/credibility of its publishers.

4.3.2. Primary data collection

4.3.2.1. Qualitative Methods of Research

This section first sheds light on the need for primary research, and then the primary data collection methods.

In-depth interviews were selected as primary data collection method for three main reasons. Firstly, it was observed that there is a limited material available on the subject from the market in context. Most of the secondary data sources used were from the United States, or other developed Western countries which could potentially result in a deviation from the ground reality. In-depth interviews were conducted in an attempt to cover the gaps between literature and the factual situation.

Secondly, a major advantage in this method of data collection lies in its informal and flexible style, building a comfortable atmosphere that supports the elaboration of qualitative content where the emphasis is to understand the meaning of participant’s experiences and life worlds. The researcher can tap into these

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experiences through in-depth interviews by creating a social and interpersonal interaction (Malhotra et al., 2017, p.209). Additionally, in-depth interviews enable to probe for further information by allowing to ask follow-up questions in a conversational style to gain more detailed insights (ibid) which is helpful in exploratory study

4.3.2.2. Choice of Interview Participants

Data collected through interviews is the main source of primary data and therefore a lot of emphasize was placed in selection of the most reliable and useful “respondents” in relation to the study. Three kind of respondents were picked for interviews: 1) potential users, 2) Industry expert, 3) Entrepreneur in sharing economy, who has started a ride hailing venture recently in Pakistan.

Interviewing the potential service users was considered important to understand the kind of reaction they may have to the proposed business idea. It was considered further important to interview potential female users due to the cultural and social dynamics of the market under study where females are not driving motorbikes on roads regularly.

Regarding the selection of industry expert, researcher defines an expert as a person with significant industry and local experience and valuable knowledge on a particular research area. Hasaan Khawar, interviewed as the industry expert, holds extensive knowledge with regards to projects undertaken in South Asia. He has worked as an International Development Consultant on more than 100 projects, one of which includes providing a consultancy to one of the sharing economy multinational companies for launching their services in Pakistan (name of the company cannot be disclosed due to privacy agreement beween Mr.Khawar and the company). Besides, Khawar acts as the Lead Advisor for Planning Commission’s flagship program in Pakistan, has worked with the World Bank, Government of Nepal, Government of Pakistan and several private national and multinational companies as a consultant. He is also a leading journalist and his articles cover national issues such as economic and political situation in the country. Khawar also works as a visiting professor at some of the best universities in Pakistan. He is associated with the government of Pakistan from around two decades, firstly as a bureaucrat, and in the last few years as a consultant. He is Member of Board of Directors of certain government projects such as Punjab thermal power (Pvt.) Ltd. and Punjab energy holdings (Pvt.) Ltd. Khawar is soon to publish a writeup on sharing economy and how it can benefit the business landscape in Pakistan. The article, however, is still in writing due to which the link could not be provided in this paper.

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The third interviewer was an entrepreneur in sharing economy, Mr.Ahmed Ayub. There are only two Pakistan based sharing economy ride hailing businesses operating nationwide in the country, Bykea and Airlift (www.rideairlift.com). Ahmed Ayub, a software engineer by education, is the co-founder of Airlift, which started in 2019. Airlift is a decentralized urban mass transit startup that allows customers to book fixed rate rides on buses and vans in their network. Besides Airlift, Ahmed is founder of another IT company, EXPERIA and has held several leadership positions in his professional life spanning over 20 years.

He was considered the most relevant resource with the firsthand experience in starting a ride hailing sharing economy venture and hence the most suitable candidate for the interview in order to find out the challenges, barriers and the feasibility of mobike inspired business idea in Pakistan.

4.3.2.3. Interview Guide

In order to gather necessary data, the researcher had to select the procedures of data collection to ensure that the objectives of the data gathering process are met. According to Malhotra et al. (2017) there is not a defined way or universal tactic to conduct the in-depth interviews, however, it is recommended to follow an unstructured question guide to conduct in-depth interviews. This approach is generally defined by an informal, flexible and spontaneous questionnaire of open-ended questions (Rogers, 1945). One of the main advantages of the unstructured guide lies in fostering an informal and flexible dialogue with the respondent. In addition, open-ended questions allow participants to answer freely without limiting their responses. Both these advantages taken together are considered to construct a highly effective method in generating qualitative content (Malhotra et al., 2017). In contrast, Analoui (1999) criticizes that the lack of structure in the unstructured interview process can adversely impact the degree of control researchers have and therefore makes it challenging to ensure if the objectives of data collection process have been met. Following Analoui’s (1999) argument, tailored, semi structured interview-guides were designed prior to all interviews in an attempt to keep part of the flexibility and informativity while trading it partially for the purpose of ensuring the coverage of all important questions (Andersen, 2010). This document contained a set of questions deemed to be sufficient in meeting the data collection objectives, as well as a guide to the researcher during the course of the interview, especially by helping to follow specific question sequences (Cohen & Crabtree, 2006). Moreover, it was deliberately kept semi structured as too much prior structuring has the possibility to blind important features emphasized by the experts or cause misreading of the informant’s perceptions (Andersen, 2010; Dubois & Gadde, 2002). Lastly, tailored

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interview guides were prepared according to the individual knowledge set of each respondent and in light of the desired objectives and outcome from each interviewer.

4.3.2.4. Data Analysis

This section explains the stages of qualitative data analysis of the in-depth interviews. The data analysis process unfolds through a four-stage process, starting with data-assembly and ending with the evaluation of respective findings in retrospectivity of the research problem.

The first stage of the process is Data Assembly and involves the actual collection of data through in-depth interviews (Malhotra et al., 2017) with the potential users of the business, industry expert and entrepreneur in the field of sharing economy. At this stage, the researcher makes use of semi-structured interview guides to help administer the interviews and perform data gathering by recording the interviews with the consent of interviewers. This allows the researcher to review the data at a later stage if the need arises. The second stage, Data Reduction, involves the organizing and structuring of the gathered data. In the first step of this stage, called decoding, the researcher goes through the collected data in order to filter the most relevant content for the study at hand. After that the data is translated into English and transcribed carefully. Transcriptions were considered an important primary data source in this qualitative data analysis (ibid). For reader’s review, all the interview transcriptions are available in the Appendices section. The third stage, Data Display, involves summarizing and presenting the data in a logical manner to enable the comprehension and compilation of the findings into a single structure. It is the coding process in which data is reorganized in an easily readable manner. In the fourth and last stage, Data Verification, researcher comprehends the verification of the contributions/findings extracted from the in-depth interviews, which also increase the validity of the study (ibid).

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Figure 4: Stages of data analysis

Source: Own creation, data taken from Malhotra et al. (2017)