Establishing the Relative Importance of Challenges in Early-stage Entrepreneurship using Analytical Hierarchy Process

Abstract

Entrepreneurship is of utmost importance in a country’s economic development. Starting one’s own business is not easy and it is even more difficult to sustain in this highly competitive environment. This research helps understand the relative importance of challenging factors that hinders the growth of an enterprise resulting in an entrepreneur deciding whether to continue the business or exit it in the early phase itself. The analytic hierarchy process (AHP) is the technique used here to determine the relative position of these factors in persuading an entrepreneur’s decision to continue or exit the business. A thorough literature review identified the key challenging factors in early-stage entrepreneurship related to, Finance, Administration, Personal, Product & Market, Environment, and Human Resources. Pairwise comparison is done by collecting the opinions of eight specialists from the industry and from academia, for formulating the reciprocal matrix and analysis using AHP. The finding of this study helps entrepreneurs identify the major hurdles and their impact on the successful launch or growth of their enterprise. On the other side, this research can also be useful for the Government and other support agencies in framing policies, to wipe out hurdles that pave way for a conducive entrepreneurial environment.

Keywords

Early-stage entrepreneurs AHP multi-criteria decision making challenging factors

Introduction

Entrepreneurship has many definitions, ranging from “converting an idea into a firm” to “those who search for possibilities to exploit prospective products and services” (Barringer & Ireland, 2019). Entrepreneurship is also defined as “perceiving and developing new economic opportunities” as well as “decision-making on resource location, type, and usage” (Wennekers & Thurik, 1999).

One of the most essential components in a country’s economic progress is entrepreneurship. Economic development is the result of deliberate human efforts like Entrepreneurship, which is practiced for a number of reasons (Zanakis et al., 2012). Employees who are unemployed or underemployed develop a microbusiness to supplement their income and ensure daily survival resulting in entrepreneurship (Zanakis et al., 2012).

Primarily, people who consider starting a new business are concerned about the factors that determine success or failure during the pre-startup phase. With this information, they assess the pros and cons. On the other hand, new ventures fail at an unparalleled rate, with many surviving ventures attaining only “marginal survival” (Cooper et al., 1994). Entrepreneurship is a product of the environment, not an inborn skill that is inherited. It entails a wide range of economic and social behavior. An entrepreneur must be active and responsible to the entire society in order to be successful. Under any circumstance, entrepreneurship is doomed to fail. It can only thrive in the correct atmosphere. The growth of entrepreneurship is influenced by social elements, culture, government policies, the political system, technology, economic situations, legislation, and so forth. Indeed, entrepreneurship cannot remain immune to shifting social values, ideologies, naive goals, environmental concerns, religious views, consumer desires, and societal requirements. Competent entrepreneurs possess the following personality traits according to Dess (2019):

A vision for the company that has the capability to self-transform.

The belief in one’s own ability to succeed is known as self-efficacy.

Dedication and drive require a great deal of effort and perseverance.

A dedication to excel at all levels within the organization.

Dess (2019) outlined traits that help entrepreneurs overcome challenges in both launching a firm and running it on a day-to-day basis. Entrepreneurship entails more than just making money and establishing a name for itself; it involves contributing to society and producing work that has a long-term impact on others.

According to the United States Bureau of Labor Statistics (BLS), around 20% of new enterprises fail in their first 2 years, 45% in their first 5 years, and 65% in their first 10 years. Only 25% of new enterprises survive for 15 years or more. These figures have not altered much over time and have remained relatively constant throughout the 1990s. According to the Bureau of Labor Statistics, 843,320 new enterprises were created in the year ending March 2021. We should predict around 168,664 of these enterprises to fail within the first 2 years based on past data. Businesses have a better chance of success with proper strategy, funding, and flexibility.

Previous research has found that a low probability of firm exit is related mainly to higher levels of human capital (Bates, 1990; Brüderl & Schüssler 1990; Rauch & Rijsdijk 2013), more financial capital (Korunka et al., 2010), a larger number of employees, a wider product portfolio (Kalleberg & Leicht, 1991), and a larger size of the firm (Geroski et al., 2010).

According to the IBM Institute for Business Value (May 2017), 91% of new businesses fail during the first 5 years, with lack of innovation being the most common cause. Starting one’s own company is not possible without overcoming obstacles. However, there are times when the effort is well worth it. According to recent data, 50% of enterprises fail during their first year, and 70% fail within 10 years after their inception. As per the Global Entrepreneurship Monitor Report-India (GEM Report) of 2021, Economic condition, Finance, and personal issues are the major reason for the discontinuance or exit of a business. Here are some concerns for entrepreneurs. The question that emerges is why, in the face of so many challenges, the entrepreneur is not giving up.

Understanding the causes of failure should provide essential knowledge to all stakeholders, including the government, academic institutions, and current and prospective business owners (Liao et al., 2009). It is critical to understand this small company failure causes because understanding small business success is incomplete without understanding these failure causes (Arasti et al., 2012).

This study adopts a two-phase approach for identifying and ranking the key challenging factors that an entrepreneur needs to overcome. Literature research and expert opinions are used to identify the significant hurdles in the first phase. The analytical hierarchy process (AHP) approach is used in the second phase to evaluate and rank the factors and actual reasons under each factor.

The approach used in selecting key factors has been a serious matter in the research of Entrepreneurial challenges as they frequently demand the examination of several elements at the same time and rely on subjective judgments made by experts in their fields of interest. As a result, among the various Multi-Criterion Decision Making techniques, the AHP method has become popular in circumstances where numerous elements must be addressed at the same time. For that reason, the AHP method has been broadly functional in decision-making situations since the introduction of this method by Thomas Saaty in 1980 (Saaty, 1980).

When establishing a scale number, the classic AHP can be advantageous when the ambiguity level in the problem at hand is low. If not, fuzzy flexible scales should be applied using the fuzzy AHP. In terms of ease of use, the classic AHP appears to be easier to operate, owing to the fact that it only deals with one number at a time. Multiple numbers, such as three or four, are used to express fuzzy numbers. Users who are unfamiliar with fuzzy number operations, such as addition or multiplication, may make mistakes during calculations. So, in this study classic AHP is used to prioritize the challenges faced by Entrepreneurs.

Problem Statement

Many firms fail in their initial stage itself and there are many underlying factors that contribute to this failure. It is highly imperative to identify these factors and to enlist the actual reasons, for such a decision to quit a business in its early stage, that emanate from these factors and to prioritize them in their order of importance.

Research Objective

The current study looks at the concerns and challenges that early-stage entrepreneurs experience when starting a business. The purpose of this article is to accomplish two goals: A.

To identify the major factors and also actual reasons under these factors, that influence the entrepreneur’s decision in the continuance or dis-continuance of a business during its early phase;

To rank the identified challenging factors along with the actual reasons underlying these factors.

The objectives are set in a manner where the factors identified as challenges are ranked using the AHP.

Subsequent sections on review of related literature, research methodology, data analysis, results, conclusions, managerial implications, limitations, and directions for future research are presented, accordingly.

Review of Literature

The understanding of individual elements that enable and impede entrepreneurship has grown dramatically over time. However, it is only recently that scholars have looked into how the peculiarities of the process inhibit or facilitate the formation of new businesses. The key challenging factors are grouped as follows based on the literature review and are given in Table 1.

Table 1.

Key Challenging Factors.

Key Challenging Factors	References
Financial Factors (FF)	Acs et al. (2008), Bates (2010), Giardino et al. (2015), Mohideen (2011), Strang (1971).
Administrative Factors (AF)	Barringer (2019), Kashyap (2015), Reiss (2011), Rocha et al. (2015).
Personal Factors (PF)	Korunka et al. (2003), Lee and Tsang (2001), Makhbul and Hasun (2010).
Product & Market-related Factors (MF)	Coelho and McClure (2005), Jaworski and Kohli (1993), Mukherjee (2018), Squires and Connor (2001).
Human Resource Factors (HF)	Kratz (2011), Sabrina (2019), Van De Woestyne et al. (2010).
Environmental factors (EF)	Bloodgood et al. (n.d.), Forlani and Mullins (2000), Ibrahim and Goodwin (1986), Chen et al. (2018), Sapienza and Carsrud (1995).

Source: The authors.

Financial Factor

Starting a business necessitates a certain amount of money. However, for new entrepreneurs, securing this money may be problematic (Delmar & Shane, 2006; Kim et al., 2006; Reynolds et al., 2006). Obtaining start-up funding, particularly for young people, can be difficult, especially in the context of a developing country (Shoof, 2006). As a result, lack of capital is frequently cited as a reason for aspiring entrepreneurs to leave their ventures (Baughn et al., 2006; Schoof, 2006; van Auken et al., 2006) and is perceived as one of the main challenges against the launching of the new enterprise, both in the case of a developing or a developed country (Davidsson & Wiklund, 2000; Reynolds et al., 2006). The absence of financial resources is a well-known factor that impedes an early-stage business. Teams persevere on their own money or money borrowed from family and friends, especially before qualifying for an external source of capital. They must establish a basic structure and pay the founders or early staff a salary without the income a traditional business would earn. The highlighted problem haunts the nascent entrepreneur until the firm breaks even, or when income exceeds costs. Another form of financial hurdle an early-stage entrepreneur face is the taxation system which is exploitative in nature in many countries.

Product & Market-Related Factor

This area includes issues relating to the creation and evolution of the startup’s product or service for its users or customers. Problems in this area are linked to the inventive nature of startups, which is both a strength and a weakness of these new businesses. The most difficult problem for these organizations, as per Giardiano et al. (2015) is the challenge of prospering amid technological obsolescence faced in deploying new technologies to develop new products or services. The availability of appropriate economic resources is critical for the growth of a business (Czinkota & Ronkainen, 2003; Tustin, 2003). Expertise and raw materials are required to put business ideas into action, to be competitive, to thrive in adverse situations, and to expand (Robertson et al., 2003; Wickham, 2001). This is a developing issue as raw material procurement is done within a local area due to financial constraints, and procurements are much smaller in scale than in the rest of the industry. The medium scale industry that produces a poor-quality product utilizing outdated technologies would not be able to compete in the global market. Customers are usually well-aware of the products available in the market If there is a lack of marketing, advertising, and awareness, the demand for such goods will be small (Mukherjee, 2018). The two sides of the same coin are, developing features in products that no one wants, and failing to future-proof the wants and needs of future clients. In other words, rather than focusing on the genuine needs of customers and producing a product to meet those needs, businesses frequently focus on their first unproven idea and spend their limited resources building a product that no one is likely to buy. According to Bajwa et al. (2017), there have been multiple instances where companies have had to adjust their strategies owing to market concerns. Though companies fail for a variety of reasons, one of the primary reasons is that there is little or no market for the product they have created.

Human Resource Factor

This area is concerned with the team’s makeup, competencies, and responsibilities that have been assigned. The team’s two key issues are a large number of diverse activities it must complete in a short span of time and the difficulty in onboarding more experienced employees in that area. The first is a result of starting a new business and all of the responsibilities that come along with it, hampered by a limited budget. Another typical example can be the requirement of personnel for legal and financial tasks, that need to be completed in the absence of resources to engage professional personnel or outsource this work to another firm. Nonetheless, the team will require a large pool of expertise to develop the company’s core as well as other associated skills (Kratz, 2011). In this market, however, start-ups must compete with established organizations that may offer more money and stability in exchange for a smaller pool of brilliant employees. Business owners understand how tough it is to hire a trustworthy, dedicated employee (Van De Woestyne et al., 2010). The majority of employees want to work less and earn more money. It is challenging to find the right employee who is enthusiastic about offering his or her services.

Personal Factor

Every entrepreneur, especially those who are just starting out, questions their own self-efficacy frequently prior to starting. Having family members who do not support the entrepreneur can also be difficult at times. New entrepreneurs have a tougher difficulty deciding what role they should play and how it will affect the company and its budget. Self-confidence also causes individuals to question themselves on whether their decisions were proper.

One of the constant problems faced by entrepreneurs is the lack of training in specialized areas (Khandelwal & Barua, 2020; Mangla et al., 2017) and not having sufficient managerial skills. For example, medium-scale industries in India typically lack up-to-date information and are unaware of the most recent technologies available the world over. Many lack the managerial skills, entrepreneurial understanding, and technology-intensive education necessary to run a successful medium-scale industry in India (Mukherjee, 2018).

Environmental Factor

In the lack of a suitable industrial and business environment, entrepreneurial ventures cannot become successful in terms of (a) socio-cultural and political support, labor and industrial laws; (b) ease of processing and no harassment in registration and certification; (c) helpful attitude of financial and promotional institutions; (d) conducive taxation policy; and (e) appropriate consultancy service, society and policymakers must ensure that the environment for business is conducive. Natural disasters caused by a warming globe may lead the company’s client base to disintegrate (Forlani & Mullins, 2000). Due to state-run monopolies and constant interference in operations from trade unions, many industries are underdeveloped. They may become inefficient and uncompetitive over the course of time. As a result, a country’s overall potential to prosper in terms of economic growth is negatively impacted.

Administrative Factor

Effective administration and necessary support from the agencies are very essential in the establishment and growth of the business. Stringent government rules and regulations can also hamper the very existence of an enterprise (Rocha et al., 2015). For a business to run successfully, corruption and the kind of harassment an entrepreneur is subjected to face are also to be wiped out. These challenges play a major role in an entrepreneur’s decision to quit. Despite the necessity of effective administration, it is frequently misunderstood and badly handled, owing to the fact that individuals are more concerned with the outcome than with the process.

Research Methodology

This study implements a two-stage method for recognizing and ranking the key challenging factors that an entrepreneur needs to overcome. Literature research and expert opinions are used to identify the significant hurdles in the first phase. The AHP approach is used in the second phase to evaluate and rank the factors and actual reasons under each factor. The AHP’s main distinguishing feature is its inherent ability to weigh several opposing aspects, either qualitative or quantitative, in order to make a decision by establishing priority of one aspect over the other (Chen & Hwang, 1992; Saaty, 1994). The AHP is judged appropriate because the goal is to determine the rankings or priorities of a number of different elements. Based on the literature review, a questionnaire was created and distributed to experts and decision-makers. The experts’ views were solicited in order to recognize frequent obstacles that influence an entrepreneur’s decision to stay in business or abandon it. Eight experts were selected to decide on the barriers and their decisions frame the pairwise choice matrix for this study, and the chosen experts are from both industry and academia. The details of inclusion and the exclusion criterion of the experts are mentioned in Tables 2 and 3.

Table 2.

Inclusion Criterion for Experts.

1	The experts are based in the state of Kerala
2	Experts having more than 10 years of experience in the field of Entrepreneurship.
3	Expertise in different aspects of Entrepreneurship
4	Academicians with Consultancy experience in setting up new ventures.
5	Entrepreneurs who have failed at least once and are successful later

Source: The authors.

Table 3.

Exclusion Criterion for Experts.

1	Experts who are not based in Kerala as the study is pertinent only to the challenges faced by the entrepreneurs of Kerala.
2	Experts have less than 10 years of entrepreneurial experience.

Source: The authors.

The details of the experts are mentioned in Table 4. The experts are selected based on the established knowledge in their areas, such as Social Entrepreneurship, Rural Entrepreneurship, and Entrepreneurs from Service & Manufacturing areas with relevant years of experience. The entrepreneurs mentioned here as established ones had failed experiences (Sl. Nos 7 and 8) with their initial establishments, later they could make their ventures successful.

The profile of experts is briefed in Table 4;

Table 4.

Profile of Experts.

Sl.No	Years of Experience	Profile
1	17 years	Social Entrepreneur, Winner of the UNCTAD Women in Business Award, and One of the 25 Women Changemakers from India.
2	21 years	Former Head, Southern Region, Kerala Financial Corporation
3	18 years	Academic Coordinator for Rural Entrepreneurship, MG National Council for Rural Education under Ministry of India
4	22 years	Entrepreneur in the Hospital Industry, M.D for a group of Hospitals in Kerala
5	12 years	Academician & Industry Specialist with District Industries Cell
6	18 years	Academician & Expert in the area of Entrepreneurship
7	20 years	Established Entrepreneur in the area of Retail Industry
8	17 years	Owner of a group of Manufacturing companies

Source: The authors.

While providing the questionnaires, the descriptions of all the factors and the actual reasons for the challenge under each of these factors were shared. When there is a disagreement of opinion about how to arrive at final pairwise comparison values, a consensus is obtained.

The proposed research design is illustrated in Figure 1.

Figure 1.

Source: The authors.

Analytic Hierarchy Process

AHP is used generally for several ranking applications based on Multi-Criterion Decision Making and the technique is mainly used for ranking various challenges faced by entrepreneurs in their early stage of business.

An Overview of the AHP

The AHP is one of the multi-criteria decision-making techniques which is commonly employed in circumstances when numerous criteria or factors are present (Saaty, 1980). Due to its versatility and adaptability, the AHP has been employed by several academics in a range of areas. Islam and Rasad (2006) used this AHP method to measure employee performance based on numerous parameters. Taylor et al. (1998) used AHP in evaluating employees based on a variety of performance-related characteristics. Singh (2013) used AHP in prioritizing the factors in a coordinated supply chain.

Using the AHP, Riyami and Ashrafi (2016) prioritized the factors influencing public sector employees on social media adoption. Using the AHP methodology (Saaty, 1980), a multi-criteria decision-making problem is broken down into three levels: a goal or an objective, identifying criteria, and various decision alternatives. In AHP a hierarchical model is constructed using these three levels, assesses the priority of the criteria, analyses the different choices for each criterion, and in the final phase ranking is done (Douligeris & Pereira, 1994). AHP relies on expert pair-wise comparisons in ranking the criteria or factors.

Pair-Wise Comparison Matrix

The following are additional details regarding the AHP procedure (Saaty, 1994; Saaty & Vargas, 2000): (a) Creating a pair-wise comparison matrix on a relative relevance scale. When an attribute is compared to itself, it is always assigned the value 1, hence all of the pairwise comparison matrix’s main diagonal entries are 1. The numbers 3, 5, 7, and 9 denote moderate importance, strong importance, “very important,” and “absolutely important,” respectively; 2, 4, 6, and 8 denote a compromise between 3, 5, 7, and 9; and 2, 4, 6, and 8 denote a compromise between 3, 5, 7, and 9. When there are “m” attributes, pair-wise comparisons generate a square matrix, as Matrix A. Pair-wise comparison is stated as the most effective technique to improve judgment because just two qualities are evaluated at a time (Saaty, 1994), and the literature recommends a 1–9 judgment scale (Saaty, 1996).

Table 5 shows the pair-wise comparison scale for AHP and its interpretation.

Table 5.

Judgement Scale Details.

Intensity of Importance	Definition
1	Equal importance
3	Moderate importance
5	Strong importance
7	Very strong importance
9	Extremely strong importance
2, 4, 6, 8	Intermediate values (for compromise between the above values)

Source: Saaty (1996).

Analysis using AHP comprises the following phases:

Phase 1: Problem Definition and Construction of AHP Model

In this step, you will create a hierarchy model of AHP is created that includes the aim, criteria, sub-criteria, and alternatives. The purpose of this research is to rank the most important elements influencing an entrepreneur’s decision to leave or stay in business. The initial level of the hierarchical model is occupied by this purpose. Level 2 includes the key considerations, while level 3 includes the sub-factors that may influence an entrepreneur’s decision. The hierarchal model is represented in Figure 2. Since the main goal of this AHP model is to prioritize the elements, no decision alternatives are been identified in this study.

Figure 2.

Source: The authors.

Figure 3.

Source: The authors.

Phase 2: Gathering Information from Experts

During this phase, experts are consulted for information on pair-wise comparisons of significant factors and actual reasons/sub-factors pertinent to the study. The nine-point scale is used to allot relative values for pair-wise comparisons among several factors (Saaty, 1980), as shown in Table 5. A statistically significant sample size is not necessary because AHP is not a statistics-based methodology (Fu et al., 1996). In addition, with this AHP method, a representative sample is not essential because the unit of analysis is the decisions that are made, not the people who made the decisions. According to Shrestha et al. (2004), the AHP is normally used to assess persons who are experts on the topic as in Delphi Study. Based on the literature study, a questionnaire was created and distributed to experts and decision-makers. The experts’ opinions were solicited in order to identify frequent obstacles that affect an entrepreneur’s decision to stay in business or abandon it. Eight experts were chosen to finalize the actual reasons and frame the pairwise choice matrix for this study, and the experts hailed from both industry and academia. All experts specialized in the area of entrepreneurship and had more than 10 years of experience. An entire 21 reasons were confirmed under six main factors based on the literature review and the opinions provided by the experts as shown in Table 1.

Phase 3: Calculating Normalized Priority Weights of Factors & Sub-Factors

The steps for defining normalized weights are as follows:

For All Factors and Sub-Factors, Construct Pair-Wise Comparison Matrices

The pair-wise comparisons are used to define which component is more important than the other. After that, the results are transformed into integers. If the ith element is more important than the jth, the integer is recorded in the ith row and jth column of the comparison matrix, while the reciprocal is written in the jth row and ith column. Both positions are assigned a value of one if the elements being compared are equivalent.

Hence, Comparison matrix C = [cij] is a square matrix of order n

where, n is the number of factors to be compared, with reciprocated elements

that is,

C_{j i} = \frac{1}{C j i}; (i, j = 1, 2, \dots, n)

Construction of Aggregate Comparison Matrices

The replies are collected from all the experts on pair-wise comparisons of various variables and sub-factors are averaged using the geometric mean approach to find the accumulated judgment for each item of the comparison matrix. For a given attribute, the aggregated comparison matrix A is created as A = [a_ij], where a_ij is the geometric mean of the decisions of N decision-makers and can be as given below:

a_{i j} = {(\prod_{i = 1}^{N} C_{i j})}^{1 / n}

(1)

Priorities Calculation

For each component and subfactor, a normalized matrix N is created for priority calculation. The normalized matrix N is produced in the same way as the comparison matrix A:

N = [n_{i}] where n_{i j} = \frac{a_{i J}}{\sum_{i = j}^{n} a_{i j}}

(2)

Then, by calculating the average of the elements in each row of N, the priority or weights conforming to all of the factors are derived. W = [wi] = w is a n X 1 column matrix with the priority vector W = [wi] = w.

w_{i} = \frac{\sum_{J = 1}^{n} n_{i j}}{n}

(3)

Validating the Results by Examining Each Comparison Matrix’s Consistency

To make sure that the estimated priority vectors are accurate, the levels of consistency of the comparison matrices are to be determined as it is well known that humans are inconsistent when giving answers to questions. The consistency ratio (CR) helps to determine consistency in a pair-wise comparison. The level of inconsistency in comparison matrix A is acceptable if CR is less than 0.10, and the ranking results can be accepted (Saaty, 1980). The ranking results cannot be recognized if the CR is more than 0.10. During this instance, the decision maker should examine the assessment process once again. Matrix A is consistent if

A W = n W

(4)

Equation (3) is an Eigenvalue problem. If the main Eigen value λ_max ≥ n, the closer λ_max is to n, and the matrix A is more consistent. To calculate the CR corresponding to a comparison matrix A and to check its consistency, the following steps are followed:

Calculate λ_max by solving the following equation:

AW = λ_{\max} W

(5)

Calculate the consistency ratio (CR) by using the formula:

C R = \frac{C I}{R I}

(6)

where CI is the consistency index given by the following formula:

Cl = ∣ \frac{λ \max - N}{N - 1}

(7)

and RI is a random index. The different number of criteria (n) corresponds to different values of RI as in Table 6.

Table 6.

Table of Random Index (Saaty, 1980).

n	1	2	3	4	5	6	7	8	9	10	11	12	13
RI	0	0	0.58	0.90	1.12	1.24	1.32	1.41	1.45	1.49	1.51	1.58	1.56

Source: Saaty (1980).

Calculation of Global Weights

The main factor’s local weights and actual reasons or sub-factors are found by Equation (3) The key components’ global weights, or overall weights, are identical to their local weights. The sub-factor’s global weights, on the other hand, can be calculated as follows: The product of a sub-global factor’s weight or actual reason is the Weight of the matching main factor and local weight of the sub-factor/actual cause.

Cl = ∣ \frac{λ \max - N}{N - 1}

(8)

Results and Discussion

MS Excel is employed to examine the data that has been collected. The replies provided by all experts on pair-wise comparisons of several components and sub-factors were combined using the geometric mean method. (Equation 3) and the comparison matrix of the main factors is shown in Table 7. Tables 7 –15 display the comparison matrices, weights, and consistency checks for each of the primary factors and real reasons/sub-factors of the model. It shows that if the CR values are smaller than the threshold value of 0.10, it indicates that the comparison matrices are consistent and the priorities/weights obtained can be accepted.

Table 7.

Pair Wise Comparison Matrix for the Main factors.

Pair Wise Comparison
	FF	PF	MF	EF	AF	HF
FF	1	4	3	3	2	2
PF	1/4	1	1/4	1/4	1/4	4
MF	1/3	4	1	1/4	3	3
EF	1/3	4	4	1	4	4
AF	1/2	4	1/3	1/4	1	3
HF	1/2	1/4	1/3	1/4	1/3	1

Source: The authors.

Table 8.

Construction of Aggregate Comparison Matrices & the Priorities of Each Factor.

	FF	PF	MF	EF	AF	HF	Criterion Weights
FF	0.342857	0.262295	0.336449	0.6	0.208696	0.133333	0.31394
PF	0.085714	0.065574	0.028037	0.05	0.026087	0.2	0.07590
MF	0.114286	0.262295	0.11215	0.05	0.313043	0.2	0.17530
EF	0.114286	0.196721	0.448598	0.2	0.313043	0.2	0.24544
AF	0.171429	0.196721	0.037383	0.05	0.104348	0.2	0.12665
HF	0.171429	0.016393	0.037383	0.05	0.034783	0.066667	0.06278

Source: The authors.

Table 9.

Comparison Matrices & Calculation of Criterion Weights of Each Sub-factor.

	FF1	FF2	FF3	FF4	Criterion Weights
FF1	0.214592	0.222222	0.167224	0.409277	0.25333
FF2	0.070815	0.111111	0.165552	0.04502	0.09812
FF3	0.643777	0.333333	0.501672	0.409277	0.47201
FF4	0.070815	0.333333	0.165552	0.136426	0.17653

Source: The authors.

Table 10.

Comparison Matrices & Calculation of Criterion Weights of Each Sub-factor.

	PF1	PF2	PF3	PF4	Criterion Weights
PF1	0.214592	0.409277	0.167224	0.333333	0.28111
PF2	0.070815	0.136426	0.165552	0.222222	0.14875
PF3	0.643777	0.409277	0.501672	0.333333	0.47201
PF4	0.070815	0.04502	0.165552	0.111111	0.09812

Source: The authors.

Table 11.

Comparison Matrices & Calculation of Criterion Weights of Each Sub-factor.

	MF1	MF2	MF3	MF4	Criterion Weights
MF1	0.214592	0.165829	0.07148	0.3	0.18798
MF2	0.070815	0.502513	0.643317	0.3	0.37916
MF3	0.643777	0.165829	0.214439	0.3	0.33101
MF4	0.070815	0.165829	0.070765	0.1	0.10185

Source: The authors.

Table 12.

Comparison Matrices & Calculation of Criterion Weights of Each Sub-factor.

	EF1	EF2	EF3	Criterion Weights
EF1	0.60241	0.428571	0.692841	0.5746
EF2	0.198795	0.142857	0.076212	0.1393
EF3	0.198795	0.428571	0.230947	0.2861

Source: The authors.

Table 13.

Comparison Matrices & Calculation of Criterion Weights of Each Sub-factor.

	AF1	AF2	AF3	Criterion Weights
AF1	0.60241	0.428571	0.692841	0.57461
AF2	0.198795	0.142857	0.076212	0.13929
AF3	0.198795	0.428571	0.230947	0.28610

Source: The authors.

Table 14.

Comparison Matrices & Calculation of Criterion Weights of Each Sub-factor.

	HF1	HF2	HF3	Criterion Weights
HF1	0.60241	0.428571	0.692841	0.57461
HF2	0.198795	0.142857	0.076212	0.13929
HF3	0.198795	0.428571	0.230947	0.28610

Source: The authors.

Table 15.

Global Weights & Ranks of Actual Reason/Sub-factors for Challenge in Entrepreneurship.

Main Factor	Factors/Sub-Factors	Global Weights	Global Rank
Financial factors	Exploitative tax system	0.148184	1
Environmental factors	Political intervention	0.141032	2
Financial factors	Lack of collateral for getting the loan	0.07953	3
Administrative factors	Lack of support from agencies	0.072772	4
Environmental factors	Unconducive economic climate	0.070222	5
Product & market-related factors	Heavy market competition	0.066465	6
Product & market-related factors	Outdated product due to obsolescence	0.058025	7
Financial factors	Limited accessibility to loans & credits	0.05542	8
Administrative factors	Stringent Govt rules and regulations	0.036234	9
Human resource factors	Shortage of skilled employees	0.036071	10
Personal factors	Lack of family support	0.035827	11
Environmental factors	Natural calamities	0.034187	12
Product & market-related factors	Lack of availability of raw materials	0.032951	13
Financial factors	Higher operating costs	0.030805	14
Personal factors	Role conflicts	0.021337	15
Human resource factors	Lack of trustworthy employees	0.01796	16
Product & market-related factors	Availability of cheap substitute products	0.017854	17
Administrative factors	Corruption & harassment by officials	0.01764	18
Personal factors	Lack of training	0.011291	19
Human resource factors	High employee attrition rate	0.008744	20
Personal factors	Lack of good managerial skills	0.007448	21

Source: The authors.

It can be observed from Table 8 that among the six main factors, the financial factor with a Priority Weight of 0.31394 occupies the highest ranking, trailed by the Environmental factor (weight = 0.24544), Product & market factor (weight = 0.17530), and administrative factor (weight = 0.12665). These findings infer that the financial factor is the key challenge faced by the majority of early-stage entrepreneurs. Providing them with the necessary support, relaxing the norms of the stringent tax system, and making loans available to needy entrepreneurs will help them to overcome the financial barriers.

The second most significant component that has an impact on entrepreneurs is discovered to be the environmental factor. Unnecessary intervention by political and government officials in the various stages of an enterprise can hamper its growth. Unexpected pandemics, Floods, and other kinds of calamities also can have a negative influence on business. In the lack of a suitable economic environment, entrepreneurial ventures cannot become successful.

The third crucial component, which has been identified as being problematic for entrepreneurs, is the Product & Market Related factor. Enterprises which produce a poor-quality product utilizing outdated technologies would be unable to compete in the global market. Customers are usually well-informed about the products in the market. If there is a lack of marketing, advertising, and awareness, the demand for the goods will be small and can influence the sale of goods.

Consistency Test:

λ_max = 6.581322

CI = 0.116264

RI = 1.24

CR= 0.093762 < 0.10

Meanwhile for other factors also, similar steps were followed where the Pairwise comparison matrix is followed with the Calculation of aggregate comparison matrices and criterion weights thereafter.

Within the Financial factor as in Table 9, the Exploitative Tax system with the weight of 0.4720 is found to be more important than the other three factors. This indicates that Government should take necessary steps to relax the stringent tax regulations and further opportunities to secure funding should be provided to entrepreneurs without having to put any collateral down the line.

Consistency Test:

λ_max = 4.224644

CI = 0.074881

RI = 0.9

CR = 0.083201 < 0.10

Within the Personal factors as in Table 10, Family support with a weight of 0.47201 outweighed other factors, namely, Lack of managerial skills (weight=0.09812) Lack of Training (weight = 0.14875), and role conflicts (weight = 0.28111). This indicates that to overcome the challenges in an entrepreneurial journey, support from family members is inevitable. The adequacy of skills possessed and the training acquired also can be a hurdle at times as it becomes a deficiency. Not understanding the kind of role to be played, the owner or an employee, and the kind of conflicts that can also act as a challenge to the budding entrepreneur.

Consistency Test:

λ_max = 4.143315

CI = 0.047772

RI = 0.9

CR = 0.05308 < 0.10

Within the Marketing factor as in Table 11, Heavy market competition with the weight of 0.37916 is found to be the factor that influences the entrepreneur most followed by Outdated products (weight = 0.33101), Lack of availability of raw materials (weight = 0.18798) and Availability of cheap substitute products (weight = 0.10185). Competition reduces the market share and customer base, particularly if demand for products or services is initially low. In order to stay competitive, a competitive market may compel to decrease the prices, lowering the return on each item.

Consistency Test:

λ_max = 4.095343

CI = 0.031781

RI = 0.9

CR = 0.035312 < 0.10

The weighted analysis of the Environmental factor’s sub-factors is shown in Table 12. Among its various sub-factors, Political Intervention (weight = 0.5746) is perceived to be the most important sub-factor, followed by Unconducive economic climate (weight = 0.2861) and Natural calamities (weight = 0.1393). The demand patterns of a corporation may be impacted by government actions, such as interest rate adjustments. Numerous factors, including the political environment, can have an impact on business organizations. It could introduce a risk factor and result in a significant loss. Firms face numerous obstacles as a result of natural disasters. Property damage after a hurricane, earthquake, or flood is often the costliest; however, business interruptions contribute significantly to losses and delay recovery.

Consistency Test:

λ_max = 3.127814

CI = 0.063907

RI = 0.58

CR = 0.010185 < 0.10

Among the sub-factors of administrative factors as in Table 13, the most challenging factor is Lack of support from agencies (Weight = 0.57461) followed by Stringent government rules & regulations (Weight = 0.28610) and Corruption & Harassment by officials (Weight = 0.13929). For many countries, corruption is a roadblock to economic development. Bribes to government officials help some businesses survive and expand. These companies’ success and expansion are not based on the value they provide to customers.

Consistency Test:

λ_max = 3.127814

CI = 0.063907

RI = 0.58

CR = 0.010185 < 0.10

Among the sub-factors under Human Resources Factor as in Table 14, the Shortage of skilled employees (Weight = 0.57461) is the key challenging one when compared to the High employee Attrition rate (Weight = 0.28610) and lack of trustworthy employees (Weight = 0.13929). According to a recent poll conducted by Robert Half (2020), more than half of small business owners named the skills gap as their top hiring concern. To avoid risking their current firm, several small business owners turn down new projects and prospects for expansion due to a scarcity of skilled professionals. At a start-up, one of the most difficult problems is finding and keeping competent personnel. Employees who leave their positions early for a variety of reasons can be a problem for start-ups, especially, which typically have lean teams.

Consistency Test:

λ_max = 3.127814

CI = 0.063907

RI = 0.58

CR = 0.010185 < 0.10

Table 15 lists the overall weights and rankings of the 21 sub-factors that the entrepreneur considers when deciding whether to keep the business going or close it down.

It can be observed from Table 15 that the Exploitative Tax system, Political Intervention, Lack of Collateral for getting a Loan, Lack of support from administrative agencies, and the top five reasons or sub-factors that are seen as important hurdles and have an impact on an entrepreneur’s decision to stay in business or to exit the business are unfavorable economic conditions. Hence, the authorities and Government agencies should focus on these factors and provide the necessary support to an entrepreneur so that more businesses thrive under the challenge.

Sensitivity Analysis

The weights assigned to the primary criteria have a significant impact on the final rankings of the alternatives. Therefore, little changes in the relative weights can result in significant changes in the final ranking. The stability of the ranking under different criterion weights needs to be examined because these weights are typically based on judgments that are extremely subjective. Sensitivity analysis can be carried out for this purpose based on scenarios that represent potential future developments or other viewpoints on the relative importance of the criteria (Chang et al., 2007).

It is possible to examine changes in the priority and ranking of the options as a result of decreasing or increasing the weight of each particular criterion. Sensitivity analysis thus offers information on the ranking’s stability. It is advised to carefully check the weights if the ranking is very subtle to even minor changes in the criteria.

Observations from Sensitivity Analysis on the Main Factors

FF (Criterion Topper among Main Factors with priority 31.39%) is 6.85% higher than the next lowest EF (24.54%).

EF (Criterion second topper among Main Factors with priority 31.39%) is 7.01% higher than the next lowest MF (17.54%).

MF (Criterion third topper among Main Factors with priority 17.54%) is 4.86% higher than the next lowest AF (12.66%).

AF (Criterion fourth topper among Main Factors with priority 12.66%) is 5.07% higher than the next lowest PF (7.59%).

PF (Criterion fifth topper among Main Factors with priority 7.59%) is 1.31% higher than the next lowest HF (6.27%). The details are mentioned in the Table 16.

Table 16.

Observations from Sensitivity Analysis.

Rank	Factors/Sub-Factors	Global Weights	Variables in the Product that Determine Global weight
1.	Exploitative tax system	0.148184	FF multiplied by FF3
2.	Political Intervention	0.141032	EF multiplied by EF1
3.	Lack of collateral for getting the loan	0.07953	FF multiplied by FF1
4.	Lack of support from agencies	0.072772	AF multiplied by AF1
5.	Unconducive economic climate	0.070222	EF multiplied by EF3
6.	Heavy market competition	0.066465	PF multiplied by PF2
7.	Outdated product due to obsolescence	0.058025	PF multiplied by PF3
8.	Limited accessibility to loans & credits	0.05542	FF multiplied by FF4
9.	Stringent Govt rules and regulations	0.036234	AF multiplied by AF3
10.	Shortage of skilled employees	0.036071	HF multiplied by HF1

Source: The authors.

Observations from Sensitivity Analysis on the Sub-Factors

FF (Criterion Topper among Main Factors with priority 31.39%) is 6.85% higher than the next lowest EF (24.54%) multiplied by FF3 (Criterion Topper among sub-factors for Finance with priority 47.2%) is 21.87% higher than FF1, which is the next lowest, whose criterion weight is only 25.33%.

FF (Criterion Topper among Main Factors with priority 31.39%) is 18.73% higher than the next lowest AF (12.66%) multiplied by FF1 (sub-factors for Finance with priority 25.3%) is 15.5 % higher than FF2, which is the next lowest, whose criterion weight is only 9.8%.

It can be seen that by shifting the current value to different values, there are no major changes happening in the final ranking, as the variation is higher among the variables. This behavior can also be detected for further challenging factors. Generally, based on the sensitivity analysis, it can be established that the final decision is reliable and consistent.

Conclusion

Entrepreneurship is essential for a country’s economic progress. It can only thrive in a conducive environment. The growth of entrepreneurship is inclined by government policies, economic situations, social elements, culture, the political system, technology, legislation, and so forth. So, it is essential to identify the major challenges an entrepreneur faces in this competitive environment. The purpose of this study was to identify and prioritize many problematic elements that influence an entrepreneur’s decision to quit or maintain their firm using AHP. Prioritization aids in determining the relative importance of issues while designing plans to encourage entrepreneurship in the country. Financial factor has emerged as the most important challenge followed by Environmental factor, Product & Market related factor, administrative factor, Personal factor, and Human Resource factor. By identifying the major challenges, entrepreneurs can take necessary preventive steps before getting into the thought of exiting the existing business. To sustain the entrepreneurial climate, it is necessary for the entrepreneur to identify & prioritize the challenging factors.

Managerial/Practical Implications

This research takes a noteworthy step forward by addressing the aspects that practitioners must consider while implementing various techniques to promote entrepreneurship. The AHP analysis of the relative importance of these elements discloses the significance of the respective factor. This approach varies from previous studies in that the challenging factors that the entrepreneur considers are not only discovered but also ranked in depth using an MCDM technique.

These findings suggest that government agencies and other support organizations should work on these issues in order to encourage entrepreneurial activities and help the entrepreneur to successfully move ahead with the idea of starting the enterprise. Academic and/or corporate organizations, as well as governments, can establish training programs and can help present and future entrepreneurs escape the reasons for failure and focus on the sources of success.

Furthermore, these failure causes could be used by business schools and institutions to design courses and training programs in efficient business management that explicitly help to avoid them. The government should also look into the matter by giving more importance to high-priority challenge factors for the conducive growth of entrepreneurship in the economy. Table 15 reveals that Finance Factors and Environment Factor-related challenges top the list. If this 20% which contributes to 80% of the challenges can be given importance, more success can be reaped in converting launches into successful businesses.

Future Scope of the Study and Limitations

The factors and reasons for challenges/sub-factors were derived through a survey of the literature. Despite the fact that the authors attempted to collect all-important criteria from experts, it is expected that a more comprehensive hierarchy of components might be established in future research because of the dynamic nature of factors affecting entrepreneurship.

There are significant drawbacks to this approach (AHP). Having an independent nature and depending on feelings to be translated into numerical decisions, the computational required even for smaller tasks, as well as the added time and effort claimed more pair comparisons. The characteristic static character of the choice, which reduces the procedure ineffectually in the event that a medium- or long-term decision is required in the future, is another critique of the AHP. The most crucial stage of AHP is the pairwise comparison of the options (Kumar et al., 2021). Expert opinion is very crucial in this technique but at the same time, the bias of experts is a problematic situation. Another issue when alternatives are introduced or withdrawn is rank reversal. Another issue of AHP is the unreliable expert judgment in pairwise comparison. This disadvantage is substantially removed by preserving the Consistency Ratio (CR) below 10%. Whitaker (2007) provided a critical analysis of the AHP’s shortages and recommended that the criteria be ranked carefully.

An analytical network process (ANP) may be a preferable alternative in this scenario. As a result, by using the ANP in the revised model, this study can be further extended by addressing some more problems that tempt an early-stage entrepreneur to leave the business. Moreover, structural equation modeling can be used to validate the given theoretical model.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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