Measuring the intensity of challenges and problems of small-scale entrepreneurs in Uttarakhand, India

Abstract

Purpose

This study aims to understand the relative intensity of the challenges and problems faced by small-scale entrepreneurs in Uttarakhand.

Design/methodology/approach

A survey methodology was used for this study. The judgement sampling method was used to select the sample for this study. The data were collected from 240 small-scale entrepreneurs using a self-structured questionnaire. Descriptive statistics, principal component analysis and confirmatory factor analysis were used to analyse the data.

Findings

The survey found that marketing, finance, taxation, human resource and government support–related problems are the major problems of small-scale entrepreneurs in the state.

Research limitations/implications

This study was conducted in both rural and urban areas, but due to the unreachability of rural entrepreneurs, the representation of rural entrepreneurs is less, so the findings are more inclined towards urban entrepreneurs.

Practical implications

The research has highlighted the intensity of the major problems faced by small-scale entrepreneurs in Uttarakhand. Although many support schemes are operational in the state, small–scale entrepreneurs face many challenges, so this study provides solutions for those challenges.

Originality/value

This study is unique in that it measures the intensity of problems and challenges of small-scale entrepreneurs and provides insight into more serious issues prevalent in the state.

Citation

Joshi, K., Mishra, M.K., Jamal, M. and Janotra, J. (2024), "Measuring the intensity of challenges and problems of small-scale entrepreneurs in Uttarakhand, India", Vilakshan - XIMB Journal of Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/XJM-03-2024-0051

Introduction

The significance of micro, small and medium enterprises (MSMEs) in economic and social development is well-recognised in both developed and emerging economies (Sanu and Anjum, 2023). Often lauded as prolific job creators and catalysts for national economic growth, MSMEs are considered the progenitors of large enterprises (Abor and Quartey, 2010) and serve as a breeding ground for entrepreneurship, propelled by individual ingenuity and innovation (Sanu and Anjum, 2023). Small and medium enterprises (SMEs) constitute approximately 90% of businesses and more than 50% of global employment. In emerging economies, SMEs contribute up to 40% of national income (GDP) and are responsible for creating seven out of ten jobs, underscoring their substantial impact on employment (Mer and Virdi, 2024). In India, the MSME sector is a vital component of the economy due to its significant contribution to the country’s output, employment and entrepreneurial base. With a vast network of around 63.4 million units spread across the nation, the MSME sector contributes approximately 31.83% of India’s gross value added (Sanu and Anjum, 2023). MSMEs manufacture over 6,000 diverse products, offering employment opportunities to approximately 70 million individuals. This sector significantly contributes to the economy, accounting for 45% of manufacturing output and 40% of exports (Singla, 2023). MSMEs can substantially enhance the country's socio-economic development by addressing unemployment, reducing regional disparities and correcting economic imbalances (Singla, 2023).

Uttarakhand is located in the northern part of India. The majestic Himalayas characterise the state's landscape. Establishing large-scale industries in the state is challenging due to geographical and infrastructural constraints; as a result, the MSME sector is essential to Uttarakhand's investment, production and employment generation (Kumar and Gajakosh (2021). Unlike large industries, small-scale industries can be developed even in hilly regions, making them particularly relevant in such areas. Uttarakhand is a state with 70% hilly land and 30% plain. Therefore, it is a perfect region for developing small businesses. The Uttarakhand Government has taken substantial measures to bolster small-scale enterprises within the state. A key milestone was revising the state's industrial policy in 2013, complemented by introducing various entrepreneurial development initiatives such as Startup Uttarakhand, the “Thirteen Districts, Thirteen Destinations” program and the Homestay scheme. To further enhance the entrepreneurial ecosystem, the government has instituted several policies and reforms, including the Single Window Clearance System, Investor Facilitation Centre, Dispute Redressal Mechanism and tax exemption (Joshi et al., 2021). The land leasing process has been significantly streamlined by creating a land bank. On this online platform, leasers and lessees can register and formalise agreements, simplifying land resource access (Joshi et al., 2021). Prioritising sustainable development and leveraging the state's abundant resources and substantial growth potential, the Government of Uttarakhand has strategically identified 13 pivotal sectors to drive entrepreneurial growth. These sectors encompass food processing, agro-processing, fast-moving consumer goods (FMCG), plastic product manufacturing, film production and mini-hydropower plants (Joshi et al., 2021). Despite these initiatives, small-scale entrepreneurs in Uttarakhand continue to encounter numerous challenges. According to the fourth census report cited by Business Standard (2013), nearly 22%, or 7,485 out of 33,565 registered units in the MSME sector, have permanently closed due to various issues such as lack of demand, shortage of working capital, non-availability of raw materials, power shortages and labour and marketing problems. (Kumar and Gajakosh (2021) also found that MSMEs in Uttarakhand face finance, marketing, human resource, technological and innovation-related problems and sociocultural problems. SMEs in emerging markets and developing countries are primarily hindered by limited access to finance, a lack of international perspective, decision-making paralysis and insufficient domestic and international political connections (Rahman et al., 2019; Mer and Virdi, 2024). By addressing the challenges faced by MSMEs and implementing supportive policies and initiatives, governments and stakeholders can unlock the full potential of these enterprises. This approach can significantly drive economic development and enhance regional prosperity (Huseynova, 2024). This study aims to measure the intensity of challenges encountered by small-scale entrepreneurs in the state and to provide actionable recommendations for policymakers and entrepreneurs to effectively address these issues.

Literature review

Small-scale entrepreneurs face many challenges related to finance, raw materials, marketing, power, labour and technical support (Lavanya Latha and Murthy, 2009). The literature on these problems in developing countries is sparse; even where it exists, a proper analysis must often be included (Chaudary, 2006). In the context of Uttarakhand, there is less number of studies; therefore, research available from Uttarakhand, India and other countries has been used to grasp the problems thoroughly.

Small firms are evolutionary; due to this, they often fail to formally plan, which leads to continuous uncertainty and either slow growth or stagnant growth (Bennett, 1993; Singh et al., 2018). Lack of planning makes them vulnerable to external challenges. Complex government regulations pose another major challenge; small business owners and managers struggle with government rules and bureaucracy. Complex frameworks, bureaucratic hurdles and inconsistent law enforcement create a demanding business environment, contributing to inefficiencies and hindering growth (Wilson, 1995; Benzing et al., 2009; Melnyk et al., 2019); for instance, implementing goods and services tax (GST) in India poses significant challenges for MSMEs. Many MSMEs found it difficult and costly to implement GST (Mohan and Ali, 2018), which impacted their profit margins. Consequently, some MSMEs turn to shadow schemes, amplifying uncertainty in the business environment (Huseynova, 2024). Favourable business policies and lower bureaucratic interference are crucial for the growth and flourishing of small firms.

Small firms face high competition, necessitating cost reductions, quality improvements and on-time delivery of products and services (Singh et al., 2010). Inadequate market support, fierce competition from large corporations and globally recognised brands, low market demand and difficulties entering domestic and foreign markets are significant challenges for MSMEs in Uttarakhand (Kumar and Gajakosh, 2021). To foster their growth, it is crucial to increase awareness of national and global trends.

Inadequate infrastructure, including transport networks, power supply and telecommunications, poses significant challenges to the operational efficiency of MSMEs (Dotsenko et al., 2023). Poor infrastructure increases production costs and restricts market access, disproportionately affecting businesses in remote regions (Bensadok and Abid, 2023).

Financial issues are highly reported in the literature. The lack of finance is a major problem, with studies categorising financial difficulties into operational, administrative and sales and debtor-related problems (Okpara, 2011; Ahmad, 2012; Gill and Biger, 2012; Agarwal and Lenka, 2014; Naidu and Chand, 2012). Financing differs depending on the enterprise’s development stage, and most micro businesses do not receive financing from formal institutions. Business owners rely more on non-formal institutions for funding due to their inability to prepare loan applications and a lack of knowledge and training regarding financial issues. High interest rates and lengthy processing times at formal financial institutions also deter borrowing (Prijadi et al., 2020). MSMEs in Uttarakhand face specific financial challenges, such as appropriate capital budgeting, working capital management and proper financial planning (Kumar and Gajakosh, 2021). The scarcity of credit, limited access to financial institutions, absence of banking services, challenges in obtaining equity capital and onerous mortgage requirements further exacerbate these issues (Sawan et al., 2015).

Women entrepreneurs in Uttarakhand face additional challenges, including a lack of awareness about training programs, financial inadequacy and gender stereotyping (Agarwal and Lenka, 2014). Problems for women entrepreneurs during venture creation include maintaining quality, training, access to the market, lack of financial support, social support and confidence (Lenka and Agarwal, 2017). Access to finance and inadequate sales are prime problems for women entrepreneurs in Uttarakhand (Sinha, 2016). Sociocultural issues and managing relationships are major challenges for women entrepreneurs in balancing work and family life (Pareek and Bagrecha, 2017). Women entrepreneurs in Uttar Pradesh face a lack of funds, difficulties balancing family and work, networking difficulties and a lack of expertise and managerial qualities (Agarwal et al., 2018). In Kazakhstan, women entrepreneurs face challenges such as fear of failure, bureaucracy, gender discrimination, balancing family and work life, unsafe environments, crime, traditional perceptions of women, supply of a professional workforce, maintaining high-quality services and bribery (Bui et al., 2018). Limited funding and balancing responsibilities are women entrepreneurs' primary challenges (Majumdar et al., 2023). Abaddi and Al-Shboul (2023) explored the challenges of digital entrepreneurs. They revealed a lack of realistic funding terms and guarantees, insufficient guidance and advisory support from incubators and entrepreneurship centres, the emergence of unexpected risks, stringent economic conditions, competition, legal and legislative obstacles, barriers to market access, team management issues and disorganisation in the entrepreneurial environment.

Research gap

Many researchers have investigated the challenges and problems confronting small-scale businesses worldwide. Therefore, a plethora of literature is available on the problems of small-scale firms. However, only a few studies are available in the context of Uttarakhand. Furthermore, the available studies do not specifically address small-scale entrepreneurs in the state and no previous research has included both the service and manufacturing sectors in its analysis. This region-specific, dual-sector analysis is crucial as it provides insights into the localised impact of national policies and helps in formulating targeted strategies to support the entrepreneurial ecosystem in Uttarakhand. Therefore, the present study seeks to measure the intensity of problems faced by small-scale entrepreneurs in Uttarakhand. The current research shall have more significant implications for small-scale entrepreneurs and policymakers as its findings enable them to understand the challenges and problems in greater depth. Furthermore, the study contributes to the entrepreneurial literature and offers policy measures to overcome the prevailing problems.

Research methodology

A survey was conducted to collect data from 240 small-scale entrepreneurs within the manufacturing and service sectors. This study focuses on these industries due to their significant roles in the regional economy of Uttarakhand. By including the manufacturing and service sectors, the study enables a comprehensive analysis of the challenges faced by small-scale entrepreneurs in the most impactful areas of Uttarakhand’s economy. The current study used judgement sampling to draw the sample. According to Cooper et al. (2015), when a researcher selects sample members based on criteria, this is known as judgement sampling. Only business owners who have been operating their companies for at least five years were chosen by the researcher to participate in the survey. The sample size was determined using the conventional method; Bush and Burns (2006) stated that the conventional method enables researchers to choose the sample size based on the sample size of previous studies. They further recommended averaging the sample size of previous studies to decide the sample size better. The data were analysed using descriptive statistics, principal component analysis (PCA) and confirmatory factor analysis (CFA).

Objectives:

• To measure the intensity of problems encountered by small-scale entrepreneurs in the state.

• To suggest policy measures to overcome these problems of small-scale entrepreneurs.

Scale development

An extensive literature review was conducted to generate an adequate pool of items reflecting the problems faced by small-scale entrepreneurs. Lavanya Latha and Murthy (2009), Azam Roomi et al. (2009), Tambunan (2011), Zamberi Ahmad (2012), Naidu and Chand (2012), Painuly (2013), Aruna (2015), Raghavan and Kumar (2015) and Mathai (2015), Sinha (2016), Lenka and Agarwal (2017). Mukherjee (2018) and Mohan and Ali (2018) are some of the studies used for scale development. Based on the literature review, problems faced by small-scale entrepreneurs were identified. Recent literature has given more weight to developing the scale. Furthermore, the degree of these problems was measured on a Likert scale of 1–5. The ratings are as follows: 1 = not a problem, 2 = a minor problem. 3 = major problem, 4 = serious problem, 5=very serious problem. After that, the scale was given to two experts (professors) for evaluation. Finally, a comprehensive questionnaire was designed to measure the intensity of problems of small-scale entrepreneurs in Uttarakhand.

Validity and reliability

Validity and reliability are two fundamental concerns in social science research. Reliability deals with the consistency of the results within the same settings. Validity assesses the appropriateness of the tools, i.e. whether the scale measures the same thing it is supposed to measure. The reliability of the scale has been assessed using Cronbach's alpha. Hair et al. (2019) suggested that the alpha value must be above 0.70. Cronbach's alpha for the present study is 0.85, higher than the threshold value. Hence, the scale is reliable. Table 1 provides a summary of the reliability of the scale.

Face validity was accessed through expert opinion; it is desirable but insufficient; therefore, discriminant and convergent validity were also assessed. To achieve convergent validity, the value of AVE must be greater than 0.5. The present study has achieved an AVE greater than 0.5 for every construct. Furthermore, to assess the discriminant validity, the square of AVE is compared to the inter-square correlation. To achieve discriminant validity, the inter-square correlation must be less than the square of AVE. The present study satisfies the criteria. Hence, discriminant validity is achieved.

Data analysis

Collected data were analysed using descriptive statistics, PCA and CFA. PCA was conducted using Jamovi version 2. PCA is a data reduction technique that allows the researcher to find the underlying dimension in the data set. Before conducting PCA, Kaiser-Meyer-Olkin (KMO) and Bartlett sphericity tests were also performed to fulfil the requirements of PCA. Further CFA was performed to confirm the results obtained from exploratory factor analysis (EFA). CFA was performed using SPSS AMOS.

Data analysis was performed in two sections: the first deals with descriptive analysis and the second covers inferential statistics. Table 2 summarises the demographic profile of small-scale entrepreneurs in the state.

Table 2 shows the demographic and business profile of the respondents. It was found that most small entrepreneurs are male (91.7%), and only (8.3%) are females, indicating that entrepreneurship remains male-dominated in the state. According to age, 14.6% of entrepreneurs are under the age of 30, 33.3% are between the age of 31 and 40, 43.3% are between the age group of 41 and 50 and 8.8% are above the age of 50. The research found that 31.3% of respondents belong to rural areas, whereas 68.8% of entrepreneurs are from urban areas. Furthermore, small-scale entrepreneurs were found to be qualified. In total, 22.5% have completed intermediate education, 40% have graduated from college or university, 23.3% have completed postgraduate degrees and 13.3% were technically qualified; only 0.8% were below high school. One hundred twenty of them were engaged in manufacturing, which includes agro-processing units, FMCG, food processing units, pharmaceutical items, plastic product manufacture, handloom and handicrafts, electrical products and automobiles. The remaining 120 respondents were from the service industry, which includes hotels and restaurants, sports and adventure, information and communication technologies (ICT), wholesalers, wellness and ayurveda centres, advertising agencies, travel agencies and automobile repair.

Principal component analysis

The PCA was conducted on 24 items to identify the underlying dimensions. Before PCA, the KMO test for measuring sampling adequacy was conducted. The value of KMO was 0.75. Kaiser (1970) suggested 0.5 as the minimum acceptance value for KMO. The obtained KMO value is higher than the cut-off value of 0.5, showing that the sample is adequate for performing factor analysis.

Furthermore, Bartlett's test of sphericity was also checked to assess the appropriate correlations among the variables in the data set. The test was significant (χ² = 3915, df = 276 and p-value = 0.001), inferring that variables are sufficiently correlated. Table 3 provides an overview of the Bartlett’s test of sphericity.

Five factors representing the problems of small-scale entrepreneurs were extracted using PCA. Parallel analysis was used to extract the factors as it is a superior method for extracting them compared to eigenvalue and other methods. Varimax rotation was used as it is a widely used method for rotation and provides good factor loadings. Table 4 represents the factor loading of PCA.

The initial eigenvalues and their corresponding variances provide a clear indication of the factor structure within the data. As shown in Table 5, the first five components exhibit eigenvalues greater than 1, cumulatively explaining 67.5% of the total variance. Specifically, the first component alone accounts for 24.4% of the variance, followed by 14.7%, 11.7%, 10.4% and 6.4% for the second through fifth components, respectively. This confirms that the majority of the variance in the data is explained by these five components, supporting the decision to retain five factors.

The scree plot shown in Figure 1 clearly shows that five factors emerge from the data. After the fifth factor, there is a noticeable drop in the eigenvalues, suggesting that these five factors explain most of the variance, whereas the remaining components contribute much less.

Tabachnick et al. (2007) stated that factors derived from EFA need to be given a unique label that shows their features and explains the factor. The present study extracted five factors from the results of PCA and named each factor: factor 1 – human resource–related problems (HRP), factor 2 – finance-related problems (FRP), factor 3 – marketing-related problems (MRP), factor 4 – taxation-related problems (TRP) and factor 5 – government support–related problems (GSRP). These factors together explain 67.5% of the variance in the data. Hair et al. (2019) said the composite variance should be 60% or above. The present study explains more variance than the minimum suggested by Hair et al.(2019).

Factor 1: Human resource–related problems

This factor explains 24.45% of the variance in the data, see Table 5, with an eigenvalue of 5.85. It consists of seven items: lack of training opportunities, high cost of acquiring workers, difficulty in motivating workers, high labour turnover, managing employees, unavailability of skilled workers and difficulty attracting a talented workforce. The eigenvalue is more significant than one, which means that the factor (HRP) explains more variance than a single observed variable.

Factor 2: Finance-related problems

This factor explains 14.66% of the variance in the data, see Table 5, with an eigenvalue of 3.52. It consists of four items: high-interest cost, lack of access to financial institutions, lack of security-free loans and inadequate working capital. The eigenvalue is more significant than one, which means that the factor (FRP) explains more variance than a single observed variable.

Factor 3: Marketing-related problems

This factor explains 11.66% of the variance in the data, see Table 5, with an eigenvalue of 2.79. It consists of five items: lack of marketing experience, lack of market information, difficulty in retaining customers, lack of market demand and difficulty in promoting the products and services. The eigenvalue is more significant than one; it indicates that a factor (MRP) explains more variance than a single observed variable.

Factor 4: Taxation-related problems

This factor explains 10.37% of the variance in the data, see Table 5, with an eigenvalue of 2.49. It consists of four items: implementation of GST is costly, operation of GST is not easy, implementation of GST is very complex and GST has significantly affected profit margins. The eigenvalue is more significant than one, indicating that factor (TRP) explains more variance than a single observed variable.

Factor 5: Government support–related problems

This factor explains 6.3% of the variance in the data, see Table 5, with an eigenvalue of 1.53. it consists of four items: lack of technical support from the government, poor dissemination of information, transportation facilities still needing to be improved and lack of financial support from the government. Since the eigenvalue is more significant than one, the factor (GSRP) explains more variance than a single observed variable.

Confirmatory factor analysis

During the PCA, five factors emerged. A CFA was conducted to confirm the results of the PCA. The CFA was performed using IBM SPSS AMOS. First, the measurement model was created in AMOS based on the results of PCA and theoretical background. Then, the data set was attached to the diagram.

Further co-variances were drawn among the five factors. Finally, using the analysis function, estimates were calculated and the model was successfully run. The five factors, namely, HRP, MRP, TRP, GSRP and FRP, were found valid and fit. Table 6 provides a summary of the validity of the constructs.

Model fit estimates – measurement model

After obtaining satisfactory reliability and validity for the individual construct and the overall measurement model, the study further determined the model fit for the measurement model. Various model fit criteria such as Chi-Square minimum (CMIN)/df, P-value, comparative fit index (CFI), the goodness of fit index (GFI), adjusted goodness of fit index (AGFI) and root mean square residual (RMSEA) were checked (see Table 7).

The researcher measured the intensity of the challenges of small-scale entrepreneurs in the state. The results of the PCA show that small-scale entrepreneurs are facing five major problems in the state. Extracted factors from PCA were named as FRP, HRP, TRP, GSRP and MRP. These factors were further analysed using CFA. The results of the CFA confirm five factors, namely, HRP, MRP, TRP, FRP and GSRP.

Finance acts as a lubricant for any business. The challenge of getting adequate finance will hamper the growth of entrepreneurship. The present study found that small-scale entrepreneurs are facing finance-related problems, which include high cost of interest, lack of access to financial institutions and insufficient working capital. The results of the current study are consistent with those of Agarwal and Lenka (2014) and Sinha (2016). To promote an entrepreneurial culture in the state, the government should increase access to finance. The government should increase interest rate subsidies to minimise the interest burden on small-scale entrepreneurs.

Furthermore, the study revealed that small-scale entrepreneurs are facing HRP. These problems are the high cost of acquiring labour, difficulty in attracting a talented workforce, lack of training opportunities, difficulty in motivating workers and high labour turnover. Pareek and Bagrecha (2017) also reported that turnover of employees, retention of talented workers and management of employees are challenges faced by small-scale entrepreneurs.

The survey also found that small entrepreneurs confront marketing-related problems. These problems are lack of marketing information, lack of marketing experience, lack of customer retention and difficulty in promoting the products and services. Wole (1992), Lavanya Latha and Murthy (2009) and Yadollahi Farsi and Toghraee (2014) also reported these marketing problems in their studies. The government should try to overcome these problems by providing online support. It could be a website that can be used for marketing products and services, disseminating information about the products and services, or any other marketing-related information. Some other online platforms, like social media, can also be used to promote their products and services.

The study reveals that small-scale entrepreneurs experience taxation-related problems. These problems are GST has significantly affected the profit margin, implementation of GST is costly, implementation of GST is very complex and operation of GST is not easy. These tax-related problems are consistent with the previous study by Mohan and Ali (2018). The government may resolve these problems by providing training on GST operations. In addition, small-scale entrepreneurs may offer assistance in implementing GST in their organisations. Furthermore, the government might grant some tax relief to small business entrepreneurs to mitigate the impact of GST on profit margins.

The study also revealed that small-scale entrepreneurs face GSRP, which include a lack of financial support from the government, lack of communication network, lack of technical support from the government and transportation facilities are still lacking.

For developing entrepreneurship in the state, adequate consideration must be given to eradicating small-scale entrepreneurs' problems and using entrepreneurial potential. This will entail adopting practical actions to raise awareness of entrepreneurial prospects, expand their knowledge and skills and increase their enthusiasm to pursue a career as an entrepreneur. Moreover, an effective mechanism must be developed to promote innovation and problem-solving approaches in small-scale entrepreneurs.

Conclusion

This study aims to identify and assess the constraints faced by small-scale entrepreneurs in Uttarakhand, India. The research used exploratory factor analysis to identify the primary obstacles and subsequently performed confirmatory factor analysis to validate these findings. The results indicate that the challenges to small-scale entrepreneurs can be broadly categorised into five areas:

1. HRP;

2. FRP;

3. MRP;

4. TRP; and

5. GSRP.

Due to these constraints, many small-scale entrepreneurs need help with operational difficulties or are forced to shut down. According to Business-standard (2013), 22% of MSMEs in Uttarakhand have shut down due to a lack of demand and a shortage of working capital. Inadequate market support, intense competition from large corporations and globally recognised brands, low market demand and challenges in accessing both domestic and international markets are among the major market-related issues faced by MSMEs in Uttarakhand (Kumar and Gajakosh, 2021). To address these challenges, the government needs to prioritise and resolve these issues promptly. Promoting small-scale entrepreneurship will generate employment in the state, helping to reduce migration and accelerating economic development. Small-scale businesses are significant employers and their growth can substantially impact the local economy. The findings of this study are beneficial for the government, policymakers and small-scale entrepreneurs, providing valuable insights for decision-making aimed at fostering a more supportive environment for MSMEs in Uttarakhand.

Recommendations

Based on the study's findings, the following recommendations are offered to address the challenges faced by small-scale entrepreneurs in Uttarakhand. Firstly, the government should guarantee the accessibility of banks and financial institutions for small-scale entrepreneurs by creating an online platform to facilitate these interactions. This platform can streamline the process and give entrepreneurs more accessible access to financial services. Secondly, to address MRP, small-scale entrepreneurs should be linked to various online platforms such as Shopify, Meesho, Flipkart and Amazon. These platforms can help entrepreneurs expand their customer base, promote their products and build brand value effectively. Thirdly, the issue of human resource constraints, such as a lack of training opportunities, can be addressed by establishing dedicated training centres. These centres can provide essential skills development and training programs tailored to the needs of small-scale entrepreneurs. Fourthly, tax-related challenges can be mitigated by offering tax holidays and providing training and support to entrepreneurs to manage tax-related obligations; this can help reduce the burden of complex taxation processes. Finally, the state must promote infrastructure, network and entrepreneurship development centres. Establishing incubation centres can provide much-needed support and resources to small-scale entrepreneurs, fostering innovation and growth in the entrepreneurial ecosystem of Uttarakhand.

Managerial implications

Investing in employee training and skill development programs is crucial for alleviating human resource-related problems. Entrepreneurs should seek partnerships with educational institutions and industry experts to provide relevant training and implementing effective human resource management practices can improve employee retention and productivity, thereby addressing skilled labour shortages. To navigate market and marketing challenges, entrepreneurs should enhance their knowledge by staying updated with national and global trends through continuous market research and leveraging digital platforms for marketing and sales. Developing robust marketing strategies to differentiate their products and services from larger competitors and exploring niche markets with unique selling points can be highly beneficial. Understanding and complying with taxation laws is essential for managing taxation-related problems. Entrepreneurs should seek advice from tax professionals and advocate for a more MSME-friendly regulatory environment. Leveraging government support is another vital strategy. Entrepreneurs should actively seek government support programs and incentives designed for MSMEs, including grants, subsidies and training programs. Building relationships with government agencies and participating in public–private partnerships can enhance access to support services and resources. Building a solid network of fellow entrepreneurs, mentors and industry experts is crucial for support, sharing best practices and collaborative opportunities. Joining business associations and attending industry events can expand entrepreneurs' networks and expose them to new ideas and opportunities.

Limitations and future research

The study acknowledges several limitations. Firstly, while it encompassed both rural and urban areas, the inaccessibility of rural entrepreneurs resulted in their underrepresentation, thereby skewing the findings towards urban entrepreneurs. Secondly, the study did not focus on any specific industry or sector, suggesting that future research could benefit from a targeted approach within a particular industry. Finally, constraints in time and budget precluded the possibility of a longitudinal study. Future research could address this by adopting a longitudinal design. In addition, a comparative analysis between entrepreneurs operating in plain versus hilly regions could provide further valuable insights.

Figures

Figure 1.

Scree plot

Table 1.

Reliability

Scale reliability statistics	Cronbach's α
Scale	0.857

Source: Jamovi output

Table 2.

Demographic and business profile of the respondents

Variables	Frequency	%
Gender
Female	20	8.3
Male	220	91.7
Total	240	100.0
Age
20–30	35	14.6
31–40	80	33.3
41–50	104	43.3
above 51	21	8.8
Total	240	100
Background
Rural	75	31.3
Urban	165	68.8
Total	240	100.0
Qualification
Below 10th	2	0.8
Graduate	96	40.0
Intermediate	54	22.5
Postgraduate	56	23.3
Technically qualified	32	13.3
Total	240	100.0
Marital status
Married	222	92.5
Unmarried	18	7.5
Total	240	100
Nature of the business
Manufacturing
Food processing units	15	6.3
Agro-processing units	15	6.3
FMCG	15	6.3
Plastic product manufacturing	15	6.3
Automobiles	15	6.3
Pharmaceutical products	15	6.3
Electronic products	15	6.3
Handloom and handicraft	15	6.3
Services
Sports and adventure	15	6.3
Hotel and restaurants	15	6.3
ICT (information communication technologies)	15	6.3
Wholesale traders	15	6.3
Advertising agency	15	6.3
Travelling agency	15	6.3
Wellness and Ayush	15	6.3
Automobile repairing	15	6.3
Total	240	100

Source: Authors’ calculation

Table 3.

Bartlett’s test of sphericity

χ²	df	p
3,915	276	< 0.001

Source: Jamovi output

Table 4.

PCA results of factor loading (challenges of small-scale entrepreneurs) component loadings

	Component
Variables	1	2	3	4	5	Uniqueness
Lack of training opportunities	0.835					0.2333
High cost of acquiring workers	0.832					0.2290
Difficulty to motivate workers	0.788					0.3349
High labour turnover	0.767					0.3459
Managing employees	0.741					0.4302
Unavailability of skilled worker	0.699					0.4057
Difficult to attract talented workers	0.695					0.4313
High cost of interest		0.946				0.0849
Lack of access to financial institutions		0.939				0.0981
Lack of security-free loan		0.932				0.0915
Inadequate working capital loan		0.616				0.5902
Lack of marketing experience			0.874			0.1962
Lack of market information			0.861			0.2168
Difficult to retain customer			0.798			0.3394
Lack of market demand			0.633			0.5208
Difficulty in promoting the products and services			0.632			0.4463
Implementation of GST is costly				0.855		0.2511
Operation of GST is not easy				0.814		0.3170
Implementation of GST is very complex				0.771		0.1998
GST has significantly affected on profit margin				0.625		0.5127
Lack of technical support from the government					0.775	0.2878
Poor dissemination of information					0.745	0.3287
Transportation facilities are still lacking					0.729	0.3602
Lack of financial support from the government					0.658	0.5460

Note:

Varimax rotation was used

Source: Jamovi output

Table 5.

PCA results of variance explained

Initial eigenvalues
Component	Eigenvalue	% of variance	Cumulative (%)
1	5.8596	24.415	24.4
2	3.5202	14.668	39.1
3	2.7990	11.663	50.7
4	2.4902	10.376	61.1
5	1.5330	6.387	67.5
6	0.9133	3.805	71.3
7	0.8577	3.574	74.9
8	0.7950	3.313	78.2
9	0.7189	2.996	81.2
10	0.6716	2.799	84.0
11	0.6268	2.612	86.6
12	0.5533	2.306	88.9
13	0.4564	1.902	90.8
14	0.3686	1.536	92.3
15	0.3529	1.470	93.8
16	0.3036	1.265	95.1
17	0.2639	1.100	96.2
18	0.2090	0.871	97.1
19	0.1787	0.745	97.8
20	0.1532	0.638	98.4
21	0.1300	0.542	99.0
22	0.1024	0.427	99.4
23	0.0837	0.349	99.8
24	0.0588	0.245	100.0

Source: Jamovi output

Table 6.

Validity measures of constructs

Factors	CR	AVE	HRP	MRP	FRP	TRP	GSRP
HRP	0.868	0.526	0.725
MRP	0.864	0.622	0.184*	0.788
FRP	0.913	0.736	0.211**	0.064	0.858
TRP	0.823	0.542	0.304***	0.197*	0.127†	0.736
GSRP	0.778	0.513	0.406***	−0.139†	0.210**	0.461***	0.716

Notes:

*Denotes significance at the 0.05 level (p < 0.05); **denotes significance at the 0.01 level (p < 0.01); ***denotes significance at the 0.001 level (p < 0.001); †sometimes represents non-significant or borderline significance (p > 0.05 but close)

Source: AMOS output

Table 7.

Model fit indices (CFA)

Indices	GFI	p-value	CFI	CMIN/df	AGFI	RMESA	p close
Recommended value	≥0.90	≥0.05	≥0.90	<5	≤0.80	≤0.80	≥0.05
Model fit indices	0.91	0.000	0.951	1.89	0.87	0.61	0.091

Source: AMOS output