Factors affecting orientation and satisfaction of women entrepreneurs in rural India

Abstract

In the present era, the women-owned businesses in the form of women entrepreneurs are one of the fastest growing entrepreneurial populations in the India. The objective of the paper is to study the factors that affect women entrepreneurial orientation and their satisfaction. In this regard, the paper explores the affecting variables and their impact on orientation and satisfaction. The proposed model and hypotheses were tested by using the data collected from boutiques, beauty parlors, carpet making units, and general stores in Jammu and Kashmir (India). Univariate, bi-variate, and multi-variate techniques were used for data analysis. In SEM, 13 paths were created for evaluating the cause and effect relationship between different factors viz., social, psychological, financial, push, pull factors, problems, and entrepreneurial orientation and satisfaction. Out of 13 paths eight relationships are significant while five relationships are insignificant in this structural equation. The key finding of the paper is that all factors affect orientation highly as compared to satisfaction. The implications of research findings for researchers and practitioners are discussed and the suggestions have also been provided.

Key Words:

• women entrepreneurship
• entrepreneurial orientation
• satisfaction
• rural India
• psychological factors

Introduction

Rurality is viewed as a dynamic entrepreneurial resource that shapes both opportunities and constraints. Location, natural resources, and the landscape, social capital, rural governance, business and social networks, as well as information and communication technologies, exert dynamic and complex influences on entrepreneurial activity in rural areas (Stathopoulou, Demetrios, & Dimitris, 2004). Rural entrepreneurship is a key to economic development in many countries across the globe (OECD, 1998 2003; UN 2004). It is one of the newest areas of research in the entrepreneurship field and has become one of the significant supportive factors for rural economic development and agribusiness.

The status of women in India has long been paradoxical. They have had access to professions such as medicine, teaching, and politics and have the right to own property. Among some social classes, women are extremely powerful. Women have been taking increasing interest in recent years in income generating activities, self-employment, and entrepreneurship. This is seen in respect of all kinds of women both in urban and rural areas (Rajani, 2008). Women are taking up both traditional activities (knitting, pickle making, toy making, jam and jelly), and also non-traditional activities (like computer training, catering services, beauty parlor, gym. etc.).

The economic, social, religious, cultural, and psychological factors affect origination and success of women entrepreneurs (Habib, Roni, & Haque, 2005). The reasons and motivations for starting business or economic activities by the rural women are enormous. The important reasons are earning money or attractive source of income, enjoying better life, availability of loans, favorable government policy, influence of success stories, personal satisfaction, desire to utilize own skill and talents, unfavorable present working environment, self-employment and employment of others, assurance of career and family security, fulfillment of creative urge of the borrowers’ experience in family business, self-confidence, non-ability to find suitable job or work, encouragement and advice of the family members, economic necessity, and so on (Afrin, Islam, & Ahmed 2008).

The paper is organized into different sections: the next section provides a review of past research on factors affecting entrepreneurial orientation and sets out the hypotheses of the study. The third section entails the research design and methodology. Section four pertains to the results including measurement and the last section is about conclusion, discussion, and strategic implications.

Past research on factors affecting entrepreneurial orientation (EO) and hypotheses building

Entrepreneurial orientation is viewed as an entrepreneurial characteristic that makes the entrepreneurs innovative and growth oriented. It refers to the processes, practices, and decisions that tackle or accept the existing and forth coming opportunities in a better way. The construct of entrepreneurial orientation consists of three dimensions viz; innovation, risk taking, and pro-activity, which are helpful and contribute positively towards firm performances and makes the entrepreneurs satisfied with overall performance (Covin & Slevin, 1989).

While investigations into the reasons, why women start businesses have been sparse, over the past 20 years a number of studies have examined the reasons as to why men initiate ventures (Birley, Moss, & Saunders, 1987; Cooper, 1981; Dubini, 1988; Scheinberg & MacMillan, 1988; Shane, Kolvereid, & Westhead, 1991; Shapero, 1975). In general, researchers have found that men start their businesses primarily as a result of such ‘pull’ factors as the opportunity to work independently, to have greater control over one's work, and to earn more money (Shane et al., 1991; Shapero, 1975). There is a lesser influence from such ‘push’ factors as limited advancement opportunities, job frustration, and avoiding an unreasonable boss or unsafe working conditions (Routamaa, Hautala, & Rissanen, 2004). With one exception, in none of these studies were women entrepreneurs addressed separately nor did they constitute more than 10% of the sample. The exception, Shane et al. (1991), studied non-US entrepreneurs, including women, and reported that the male entrepreneurs were most motivated by the need to improve their positions in society for themselves and their families, while the female entrepreneurs were most motivated by the need for achievement. However, the authors cautioned that cultural differences across samples precluded generalizing findings to US entrepreneurs. Larwood and Gattiker's (1989) study of career paths of men and women provides evidence that women's careers cannot be well understood by studying the patterns of men.

Hisrich and Brush (1985) researched to find the reasons for starting the business by women entrepreneurs. Most frequently cited were ‘push’ factors of frustration and boredom in their previous jobs, followed by interest in the business, with ‘pull’ factors such as autonomy a distant third. The research by Sullivan, Halbrendt, Wang, & Scannell (1997) found that women see work environments in large organizations as significantly more hostile and this perception was related to women's turnover intentions. Thus, ‘push’ factors may be a more important influence for women than for men. Recently, the ‘glass ceiling’– the seemingly impenetrable barrier that prevents female mid-managers from moving up to the executive suite has become the focus of attention for the researchers (Greene, Gatewood, & Carter, 2001). Consistent with the research of Hisrich and Brush (1985), these experienced women who leave the large organization to be become entrepreneurs may be leaving their corporate positions due to the glass ceiling, in effect an organizational push motivator. Some of these key motivational factors are presented in the Table below:

Table

Key motivational factors
Pull factors	Push factors
Autonomy and independence	Dissatisfaction in the labor market
Personal satisfaction and achievement	Need for greater income redundancy
Dream of being an entrepreneur	Unemployment
Gap in the market	Last resort
Looking for a challenge
Rejecting stereotypical feminine identities

Source: http://www.emeraldinsight.com/journals.htm?articleid=1852738

On the basis of above literature it is hypothesized that

Hyp. 1 Push factors affect entrepreneurial orientation of the women more than the pull factors.

Hyp. 2 Both push and pull factors affect satisfaction of the women entrepreneurs.

Women who try to enter an industry, either in a managerial or entrepreneurial role, are generally exposed to various environmental constraints. Starting and operating business involves considerable risk and effort on the part of the entrepreneur, particularly in the light of highest failure rate. Perhaps, this rate is even higher in the case of women entrepreneurs who have to face not only the usual business problems but also their family problems. This not only limits the scope of their contribution to the industrialization process, but also undermines the productive utilization of an available human resource, that is most needed in our country (Rajani, 2008).

The development of rural entrepreneurship depends on socioeconomic development of the people. Experts opined that the essentials to develop rural entrepreneurship are the development of capabilities of the borrowers. Once the rural women are self-sufficient, they will be able to initiate their own projects and consequently it will help them to stand on their own feet (Afrin et al., 2008). Much of the research conducted in the 1980s identified business challenges specific to women entrepreneurs and some of the difficulties reported included: obtaining start-up funds, financial management, and development of effective marketing and advertising (Pellegrino & Reece, 1982). The root causes of limited financial success were often attributed to early management practices. A pair of studies examining women's access to capital employed an experimental design methodology to determine whether women faced obstacles in obtaining bank loans. This research found that lending institutions perceived women business owners to be less successful than men (Buttner & Rosen, 1988). Buttner & Rosen (1992) concluded that women were more likely to attribute the denial of a bank loan to gender bias than were men, but there was evidence that some of the differences were based on the gender stereotypes held by the capital providers. Women business owners were also significantly more likely to perceive disrespectful treatment by lending officers (Fabowale, Orser, & Riding, 1995). Institutional arrangements frame not only how many women perceive opportunities and make strategic choices, but also how these women and others view their businesses. Particularly pertinent is how the ‘gatekeepers’ of resources as well as the power holders, be it in the household and community or at the wider societal levels, have an impact, often subtle or hidden, on the entrepreneurial activity of women (Brush, Bruin, & Welter, 2009). Though women are adept at turning social resources into human and economic resources (Inman, 2000) but only few of them enter into the business arena (Brush et al., 2002). Specifically, Brush and her colleagues (2002) found that a lack of social capital and networks were key reasons why female entrepreneurs had less access to venture capital funding in high-growth industries, thus the next two hypotheses are:

Hyp. 3 Financial factors significantly affect orientation and satisfaction level of the women entrepreneurs.

Hyp. 4 Women entrepreneurs have difficulty in procuring loans.

Entrepreneurs are driven by unique attitudes, needs, and values. These characteristics are thought to drive the entrepreneur to behave in a certain fashion. Several psychological components include the various needs (McClelland, 1961), propensity of risk taking behavior, and personal values (Ferguson & Streib, 1996). According to McClelland's theory, the need for self-achievement is associated with entrepreneurial motivation. A longitudinal study assessed the relationship between psychological characteristics and business organizing activities, using measures of achievement motivation, locus of control, risk perception, and creativity. The most significant difference between men and women entrepreneurs was found in scores on innovation and achievement/activity (Shaver, Gartner, Gatewood, & Vos, 1996). A surprising finding emerged through an adaptation of Miner's model that allowed for the consideration of attribution styles. These results showed female entrepreneurs and managers were more likely to take risks than their male counterparts. The authors suggested that women may be more willing to accept entrepreneurial risk because they face a more hostile and prejudicial work environment (Bellu, 1993). Based on these and other supporting arguments we hypothesize that:

Hyp. 5 Psychological characteristics influence the orientation and satisfaction of women entrepreneurs.

Hyp. 6 Women entrepreneurs are willing to take business risks.

How a society thinks about entrepreneurship may influence the pool of potential entrepreneurs. The pull between family and work and the multiple other social roles that women play can be seen in how role conflict is experienced – regardless of family structure or time spent at work. This conflict was found to be more prevalent in owners with lower self-esteem or self-worth (Stoner, Hartman, & Arora, 1990). One study found the relationship between time commitment to work and time commitment to family mediated the effect of role demands (Parasuraman, Purohit, Godshalk, & Beutell, 1996). As part of the consideration of these roles, the contribution of both expressive and instrumental support from the spouses was often provided anecdotally (Greene, 1993). In a study of 48 attendees at an entrepreneurship education program, Birley et al. (1987) found that men received support from their spouses in their business enterprises more often than did women. Biggart's (1988) Charismatic Capitalism includes many incidences of women involved in direct selling activities, who were required to work around their spouses, rather than receiving support from them. For women entrepreneurs, motherhood or family embeddings will directly influence how the entrepreneurial process unfolds. Family role will influence information networks used to identify the market opportunity. Hence, women with high commitment to family will be less likely to interact in market/financial/industry networks, possibly affecting the growth prospects or even novelty of the venture (Brush et al., 2009). On the basis of above discussion the next hypothesis is:

Hyp. 7 Social support affects the EO and satisfaction of the women entrepreneurs.

Women entrepreneurs have worker-related problems such as labor absenteeism, lack of skilled labor, difficulty in retaining workforce and low productivity of labor (Ganesan, Kaur, & Maheswari, 2002; Nigam & Sharma 1997). Lack of financial assistance creates problems for purchase raw material and other infrastructural facilities to start their ventures (Starcher, 1996). Thus the next hypothesis is Hyp. 8 The women entrepreneurs have work-related problems.

The concept of EO refers to the processes, practices, and decision activities leading to new entry or opportunity for an individual/firm (Covin & Slevin, 1989). In the entrepreneurship domain, especially in the case of women entrepreneur, the construct of entrepreneurial orientation was operationalized by Miller (1983) and Covin and Slevin (1989). The individuals with strong entrepreneurial orientations are willing to take on high-risk projects in exchange for potentially high returns and satisfaction at individual level. The firms with weak entrepreneurial orientations are highly risk-averse, non-innovative, and reactive and less satisfied (Miller, 1983). Thus it is hypothesized that:

Hyp. 9 The higher the level of entrepreneurial orientation, the higher is the satisfaction level of the women entrepreneurs.

Research design and methodology

The study is exploratory cum evaluative in nature and following steps were taken to make it accurate and reliable:

Sample selection

This study was conducted in J&K (India). Enterprises taken up for the study were boutiques, beauty parlors, carpet making units, and general stores. The total population arrived at 1,556. To determine the sample size, a pilot survey of 50 respondents, selected conveniently, was conducted to work out the mean and standard deviation in the population with the help of following formula (Mukhopadhyay, 1998, pp. 21–32): Where SD = Standard Deviation, N=total population, n=sample population, and mean = sample mean.

Data collection form and generation of scale items

The data collection form was developed as per the guidelines of literature and extensive discussions with experts. The statements of the self-designed schedules were finalized after reviewing the existing literature viz. Nair and Pandey (2006), Mitchell (2004), Ntseane (2004), Mohanty (2004), Ganesan et al. (2002), Kalyani and Chandralekha (2002), Bennett and Dann (2000), Carter (2000), Charumati (1997), Nigam and Sharma (1997), Mukherjee (2006). Likert's 5-point scale (5–1) has been used for measuring attitudes, where 1 stands for strongly disagree, 2 for disagree, 3 for indifferent, 4 for agree, and 5 for strongly disagree. The negative items were reversed during data feeding process. Besides, the demographic profile items, the self-designed schedule comprised of 78 statements under five sections; namely, social factor (10 statements), psychological factor (12 statements), financial and economic factor (14 statements), problems or barriers (21 statements), satisfaction of women entrepreneurs (8 statements), entrepreneurial orientation (20 statements), and entrepreneurial motivation (12 statements).

Results

After determining the mean and standard deviation in the population, the sample size was worked out at 274. However, the figure was rounded to 300 for the purpose of easy calculation. Hence, out of 1,556 women entrepreneurs 300 respondents have been selected for data collection. The selection of entrepreneurs was done on basis of chit method. Before analyzing the data it has been duly purified and validated with the help of exploratory and confirmatory factor analysis, the result of which are discussed next.

Data purification-exploratory factor analysis

Data collected has been subjected to factor analysis to bring out the important factors affecting women entrepreneurs. The data has been summarized by application of Principal Component Analysis along with Varimax rotation. The reduction of data began from anti-image correlation matrix and statements with values less than 0.50 on the diagonal axis were deleted first. Then communalities were checked and variables with extracted communalities less than 0.5 were deleted. In the last stage, rotated component matrix were inspected and variables with values less than 0.5 and double loading were deleted. The statements with factor loading less than 0.5 and Eigen value less than 1.0 were ignored for the subsequent analysis. Data purification was performed on all six constructs. Ten statements of social factors were reduced to four after three rounds of factor analysis, which coverged under one factor explaining about 56% of the total variation (Table 1). Application of factor analysis on psychological factors reduced 12 statements to 4 under one factor with acceptable factor loading values and communalities. About 65% variance is being explained by this construct (Table 1). Financial factor was reduced to four items under one factor, explaining 64% of the total variation. All items have average to good factor loadings and communalities are also above 0.5. The problem construct resulted into six items under one factor with good factor loading values and 72% of the total variation is being explained by this construct. The satisfaction construct was reduced to four items after factor analysis, which explained 67% of the variation. Twenty items of entrepreneurial orientation construct were reduced to eight under one factor explaining 78% of the total variance. All items have good factor loadings and communalities (Table 1). Twelve items of entrepreneurial motivation were reduced to nine, which converged under two factors namely, push and pull factors with average to good factor loadings and communalities. About 61% variance is being explained by this construct.

Table 1. Summary of exploratory factor analysis

Factors	M	SD	FL	Comm	Eigen value	V.E (%)	KMO Value	Alpha value
Social Factor	3.70				2.22	55.5	0.668	0.751
Family welfare	3.74	0.912	0.816	0.667
Family motivation	3.54	0.813	0.809	0.657
Moral support of family	3.53	0.747	0.759	0.556
Society takes my work in good spirit	3.99	0.936	0.549	0.577
Psychological Factors	3.94				2.58	64.457	0.546	0.832
Urge for learning	3.96	0.596	0.504	0.521
Not afraid of failures and business risk	3.75	0.983	0.781	0.611
Do not get discouraged easily	3.95	0.852	0.835	0.697
High self–esteem	4.10	0.661	0.528	0.535
Financial Factors	2.57				2.56	64	0.762	0.743
Government support	3.28	1.213	0.737	0.525
Support of financial agencies/difficulty in procuring loan	2.12	0.910	0.625	0.516
Financial motivation to start the unit	2.19	0.857	0.515	0.502
Awareness about loan scheme	2.70	1.189	0.724	0.544
Work-Related Problems	3.00				4.332	72.2	0.511	0.791
Availability of raw material	3.15	0.872	0.572	0.512
Demand for the product	3.26	0.765	0.781	0.813
Infrastructure	3.10	0.732	0.835	0.815
Warehousing facilities	3.15	0.714	0.894	0.848
Power supply	2.33	0.899	0.677	0.661
Skilled workforce	2.69	0.931	0.672	0.633
Satisfaction	4.04				2.685	67.13	0.609	0.741
Business satisfaction	4.23	0.690	0.773	0.615
Satisfaction with business earning	4.01	0.744	0.79	0.638
Satisfied with family support	4.14	0.590	0.665	0.543
Satisfied with workers	3.79	0.632	0.968	0.939
Entrepreneurial Orientation	3.72				5.66	78.2	0.611	0.873
Want to achieve status in the society	3.82	0.730	0.789	0.642
Want to control the events around me	3.45	0.936	0.782	0.631
Financial independence	4.57	0.528	0.863	0.803
Self-confidence	3.95	0.601	0.763	0.599
Determination	3.97	0.781	0.513	0.575
Independence in decision making	3.80	0.706	0.613	0.513
Innovative instincts	2.10	1.05	0.523	0.73
Profitability of business	4.12	0.632	0.836	0.762
Pull Factors	4.07				3.25	30.61	0.683	0.771
Feel proud to be entrepreneur	4.45	0.670	0.674	0.565
It gives me satisfaction	3.92	0.590	0.79	0.625
Freedom to adopt my own approach	4.09	0.623	0.521	0.523
Confident about my skills and knowledge	3.83	0.533	0.747	0.585
Push Factors	3.31				2.71	30.11	0.525	0.732
Desire for good life	3.92	0.797	0.713	0.509
Financial help from government.	1.98	1.19	0.756	0.581
Motivated due to schemes for assistance	2.16	0.868	0.503	0.503
Attracted by high profit margins	4.18	0.689	0.814	0.663
Need for greater income	4.30	0.836	0.774	0.601

Construct reliability and validity–confirmatory factor analysis

The factors that emerged after exploratory factor analysis were validated through confirmatory factor analysis with the help of AMOS soft ware (16th version). The variables that emerged after exploratory factor analysis (EFA) were used as manifest variables of the respective latent construct. The results revealed that all variables are highly related with their respective latent constructs (Table 2). The variables with standardized regression weight less than 0.5 were deleted (Hair, Anderson, Tatham, & William, 2006). The goodness of fit statistics also gave excellent results as the value of GFI, AGFI, CFI, in all constructs is above nine and value of RMR is less than 0.05 and RMSEA is less 0.08 (Table 3).

Table 2. Results of confirmatory factor analysis

Latent construct	Manifest variables	SRW	CR	R^2
Social factors	S2	0.601	7.705***	0.362
	S4	0.747	7.750***	0.558
	S5	0.696	Reference	0.484
	S9	0.523	2.120**	0.253
Psychological factors	P3	0.502	2.347**	0.153
	P5	0.512	Reference	0.252
	P6	0.920	2.988**	0.846
	P7	0.523	3.970**	0.352
Financial factors	f2	0.598	Reference	0.358
	f7	0.561	4.404***	0.213
	f9	0.546	3.779***	0.119
	f15	0.734	4.495***	0.285
Problems	PR1	0.624	5.158***	0.189
	PR2	0.569	2.118*	0.115
	PR3	0.750	Reference	0.530
	PR4	0.990	6.978***	0.980
	PR8	0.547	2.366*	0.120
	PR10	0.558	1.966*	0.192
Pull factors	PL1	0.521	Reference	0.271
	PL3	0.733	5.982***	0.537
	PL4	0.599	6.150***	0.359
	PL5	0.345	4.370***	0.119
Push factors	PL7	0.524	2.134*	0.252
	PL8	0.556	2.531*	0.542
	PL10	0.736	5.231***	0.846
	PL11	0.528	1.99*	0.183
	PL12	0.581	2.13*	0.213
Satisfaction	S1	0.635	Reference	0.403
	S2	0.700	4.474***	0.491
	S3	0.594	4.726***	0.163
	S4	0.501	1.992*	0.128
Entrepreneurial orientation	EO1	0.300	2.812**	0.190
	EO3	0.361	Reference	0.135
	EO4	0.218	2.758**	0.175
	EO8	0.267	2.713**	0.253
	EO11	0.303	2.927**	0.192
	EO12	0.503	3.573***	0.171
	EO18	0.418	3.407***	0.148
	EO19	–0.383	3.076**	0.130

***Sig.<0.001, **Sig.<0.01, *Sig.<0.05.

Table 3. Summary of goodness of fit

Constructs	Chi square	DF	Chi square/DF	P level	RMR	GFI	AGFI	CFI	RMSEA	Bentler-Bonnet Coefficient
Social	3.762	2	1.881	0.152	0.017	0.994	0.969	0.990	0.054	0.980
Psychological	1.024	2	0.512	0.599	0.008	0.998	0.992	1.000	0.000	0.989
Financial	3.127	2	1.563	0.209	0.024	0.967	0.932	0.986	0.046	0.965
Problems	29.626	9	3.292	0.001	0.044	0.970	0.930	0.930	0.081	0.905
Pull	2.436	2	1.218	0.296	0.008	0.996	0.980	0.997	0.027	0.984
Push	4.923	3	1.641	0.177	0.023	0.993	0.967	0.965	0.046	0.925
Satisfaction	1.364	2	0.682	0.506	0.047	0.998	0.989	1.000	0.000	0.986
Entrepreneurial orientation	27.902	18	1.550	0.064	0.025	0.977	0.954	0.919	0.043	0.901

Convergent validity assesses the degree to which two measures of the same concept are correlated. High correlations indicate that the scale is measuring its intended concept (Hair, Rolph, Tatham, & Black, 2005). A scale with Bentler-Bonnet coefficient values of 0.90 or above implies strong convergent validity (Bentler & Bonnet, 1980). The Bentler-Bonnet coefficient for all dimensions of scales used in this study are above 0.90 (Table 3), indicating strong convergent validity.

Discriminant validity describes the degree to which the operationalization is not similar to other operationalization. Fornell and Larcker (1981) highlighted the importance of evaluating the discriminant validity of the constructs used. A successful evaluation of discriminant validity shows that a test of a concept is not highly correlated with other tests designed to measure theoretically different concepts. This assessment was done in two ways. First, the diagonal elements of the correlation matrix shown in Table 4 show the square root of the average variance extracted. Each diagonal element of the matrix should be greater than all other entries in the corresponding row and column of which the diagonal element is a part if discriminant validity is sufficient. The results meet this requirement. Second, for good discriminant validity no item should load more highly on another construct than on the construct to which it is supposed to belong (Fey, Yakushev, Park, & Bjorkman, 2009) Since the data fulfill both of these requirements, it is proved that the discriminant validity of the constructs used in is more than adequate.

Table 4. Discriminant validity and correlation analysis

	Social	Psychological	Financial	Problems	Satisfaction	Entrepreneurial orientation	Pull	Push
Social	546
Psychological	0.038	0.692
Financial	0.024	0.049	0.552
Problems	0.008	0.044	0.049	0.625
Satisfaction	0.103	0.076	0.109	0.131	549
Entrepreneurial	0.236	0.300	0.008	0.076	0.136	0.762
Pull	0.158	0.162	0.059	0.041	0.230	0.473	0.594
Push	0.000	0.001	0.132	0.101	0.033	0.195	0.047	0.535

* The values on diagonal axis represent Average Variance Extracted and values below diagonal axis are squared correlations

Reliability

To check the internal consistency in the data collected, the reliability tests viz. Cronbach's Alpha and split half value have been worked out. The split-half reliability of the data has been examined by dividing the respondents into two equal halves. The data collected from women entrepreneurs has proved reliable as mean values of both groups (Group I=3.37; Group II=3.46) are above average and there is no significant difference in the mean values of two groups (F=0.941, p>0.05). Further, Cronbach's alpha value has also proven reliable as value of Cronbach's alpha for all the constructs is above 0.7 (Table 1).

Measurement and analysis

The application of CFA resulted into validation of the constructs used in the study. The explanation of each construct is next.

Social factors (F1)

This factor comprised of four statements and the mean score came to 3.70 (Table 1). About 75% of women entrepreneurs opined that they work for the welfare of the society (M=3.99) as well as they (70%) feel that their business enhanced welfare of their family (M=3.74) also. About 63% of women reflect that they get motivation (M=3.54) and moral support (M=3.53) from their family to start the business. The intensive study of the factor reveals that women entrepreneurs get moral support and encouragement from their family, which acts as a motivator for them to start their business.

Psychological factors (F2)

The average score for satisfying factor has arrived at 3.94 (Table 1). The analysis revealed that 90% of respondents have high self-esteem (M=4.10), as they (78%) have urge for learning (M=3.96). In addition, 75% of women entrepreneurs revealed that they do not get discouraged easily (M=3.80) because they are not afraid of failures (M=3.75). An in-depth study of the factor reveals that women entrepreneurs are satisfied and they are not afraid of failures.

Financial factors (F3)

The factorial mean for financial factors has arrived at 2.57 (Table 1), which is below average on the scale used. About 62% of respondents revealed that there was a lack of financial facilities from government agencies (M=2.12) as the assistance provided by the government and other agencies (53%) is largely on paper (r=0.50, Sig.<0.01). Further, 68% of respondents hardly feel motivated due to financial support provided by government (M=2.19). They are also aware about the various loan schemes from time to time (M=2.70). The overall analysis of this factor revealed a lack of support from government and non-government agencies to financially support the women entrepreneurs.

Work-related problems (F4)

Mean value of this factor is 3.00 (Table 1). About 78% of women entrepreneurs viewed that shortage of power supply affected their business (M=2.33). About 48% of women entrepreneurs opined that lack of skilled labor is a major problem in running business (M=2.69) followed by lack of infrastructure facilities (M=3.10) and availability of raw material, storing, and warehousing facility (M=3.15). In addition to all these problems, they (43%) admitted that there is lack of demand for their products in the market (M=3.26), which further adds to their sufferings. The analysis of this factor indicates that the shortage of electricity, skilled labor, scarcity of raw material, lack of infrastructural facilities, shortage of storage and warehousing facilities, and the low demand for their products in the market is an area of concern for women entrepreneurs.

Satisfaction (F5)

The mean value of this factor has arrived at 4.04 (Table 1). About 80% of women entrepreneurs were satisfied with their workers (M=3.79) and their earnings from business (M=4.01) that helps to enhance their satisfaction from business (M=4.23). They (48%) are very satisfied with support provided by their family for running the business (M=4.14). Thorough analysis of the factor shows that women entrepreneurs are satisfied with their business earnings and family support.

Entrepreneurial orientation (F6)

Mean value of this factor is 3.72 (Table 1). They are (58%) highly confident of their skills (M=3.95) and decision-making capability (M=3.80). Innovative ideas help them to run business efficiently, moreover they are also able to have control over the events surrounding the business (M=3.45). They feel that being an entrepreneur also gives them high status in the society. Further profitability attracted them toward the business as it gives them financial interdependence (M=4.12). The whole analysis of the factor reflect that women entrepreneurs are oriented toward their business success through their self-confidence, financial interdependence, and determination, which are significant attributes for deciding their success in business.

Pull factors (F7)

It comprised of four statements related to the factors that pulls them to become an entrepreneur. Mean value of this factor is 4.07 (Table 1). About 68% of women entrepreneurs felt proud to be an entrepreneur (M=4.45) as it gives them satisfaction (M=3.92, r=0.396, Sig.<0.01). About 43% of women reflected that they are confident about their skills and knowledge (M=3.83) that's why they adopt their own approach to do the business without any intervention from others (M=4.09). The whole analysis confirms that being psychologically oriented towards the business makes them more confident and they use their own concepts for running the business.

Push factors (F8)

Factorial mean for push factor has arrived at 3.31 (Table 1). About 70% of respondents were least satisfied with the financial help from government to start the business (M=1.98). Besides financial help they get the least motivation from the various government schemes for assistance (M=2.16). In spite of these difficulties respondents revealed that they are attracted towards the business due to the high profit margin (M=4.18) and the desire for a good life (M=3.92). The whole aspect of push factors disclose the irresponsible attitude of government to provide financial and motivational assistance to women entrepreneurs; but in-spite of that, women's entering into the field of entrepreneurship is due to the desire for good life.

Structural equation modeling (SEM)

Structural Equitation Modeling (SEM) is a multivariate technique that seeks to explain the relationship among multiple variables. In the present study, factors affecting entrepreneurial orientation of women in rural India have been assessed by using the structural equation modeling (SEM) through AMOS 15. The results are discussed next.

Thirteen paths were created in the SEM for evaluating the cause and effect relationship between different factors viz., social, psychological, financial, push, pull factors, problems, and entrepreneurial orientation and satisfaction (Fig. 1). The relationships between social factors, psychological factors, financial factors, problems factor, satisfaction, Entrepreneurial orientation, pull factors, and push factors were analyzed using the summated scales (Hair et al., 2006; Jones & Taylor, 2007). All the variables were examined for outliers and other departures from non-normality. No significant outliers were detected. The obtained sample size appeared adequate to test a simultaneous structural model (Hair et al., 2005). Tables 2 and 3 present the standardized values of estimation and the goodness of fit indices. All indices indicated the robustness of the overall model, with the GFI, CFI, NFI, and AGFI well exceeding 0.9, and the RAMSEA is close to 0.05. However, the statistically significant chi-square (p-value) was expected, due to its sensitivity to large sample size (Bagozi, Yi, & Phillips 1991). Out of 13 paths, eight relationships are significant while five relationships are insignificant in this structural equation (Table 5).

Fig. 1. Factors affecting entrepreneurial orientation and satisfaction. Key: ER 1=errors of entrepreneurial orientation, ER 2=error of entrepreneurial satisfaction. Sig:<0.05, **Sig.<0.01, ***Sig. <0.001.

Table 5. Impact of independent variables on entrepreneurial satisfaction and orientation

Dependent variable	Independent variable	SRW	CR (Sig.)	R^2
Entrepreneurial orientation	Social	0.189	3.911***	0.300
	Psychological	0.254	5.254***
	Finance	0.011	0.234
	Problems	0.057	1.176
	Push	0.178	3.675***
	Pull	0.405	8.375***
Entrepreneurial satisfaction	Social	0.076	1.345	0.080
	Psychological	0.056	0.962
	Finance	0.104	1.99*
	Problems	0.135	2.431**
	Push	–0.044	–0.773
	Pull	0.197	3.198*

Significance: *sig.<0.05, **sig.<0.01, ***sig.<0.001.

Exploration of the factor-wise impact of all factors on women entrepreneurial orientation revealed that all factors are accounting for 30% variation. (R ²=.300, Table 5). Pull factor is the most significant predictor (SRW=0.405, Sig. <0.001) of entrepreneurial orientation followed by psychological factors, social factors, and push factors (Table 5). Though both pull and push factors are affecting entrepreneurial orientation but the quantum of influence exerted by pull factors (SRW=0.405, Sig.<0.001) is more than that of push factors (SRW=0.178, Sig.<0.001), hence the first hypothesis stands rejected.

Further exploration of push and pull factors on satisfaction of women entrepreneurs also yielded significant relationship with pull factors (SRW=0.197, Sig.<0.001) and insignificant relationship with push factors. Hence the second hypothesis that push and pull factors affect entrepreneurial satisfaction is partially accepted.

Finance and related matters deem to exercise influence on orientation as well as satisfaction but the results are not completely in-line as the relationship entrepreneurial orientation is insignificant (SRW=0.011, Sig.>0.05) but they do affect the satisfaction level of the women entrepreneurs (SRW=0.104, Sig.<0.05). So the third hypothesis that financial factors significantly affect orientation and satisfaction level of the women entrepreneurs is partially accepted.

On the other hand the fourth hypothesis that women entrepreneurs have difficulty in procuring loan stands accepted (t=0.761, Sig.>0.05) as mean score from this item is also below average (M=2.12).

Psychological characteristics are thought to drive an individual to become an entrepreneur, which stands accepted in this study also (SRW=0.254, Sig.<0.001) and women entrepreneurs are also willing to take risks (t=0.832, Sig.>0.05). Thus the fifth and sixth hypotheses stand to be accepted.

Social support from the family, relatives, and friends affects the orientation (SRW=0.189, Sig.<0.001) but its effect on the satisfaction of the women entrepreneurs is insignificant (SRW=0.076, Sig.>0.05), thus the seventh hypothesis that social support affects orientation and satisfaction of women entrepreneurs is partially accepted.

The work and work related problems can cause frustration among the entrepreneurs and this study revealed that these don't affect the orientation but certainly affect the satisfaction level of the women entrepreneurs (Table 5). Hence, hypothesis 8 that women entrepreneurs have worker-related problems is also rejected (t=2.363, Sig.<0.01) as they are moderately satisfied from this aspect of the business (M=3.00).

Strong entrepreneurial orientations motivate an entrepreneur to take projects to gain high returns and personal satisfaction, which has also been proved through this research (SRW=0.576, Sig.<0.001). So the ninth hypothesis that higher the level of entrepreneurial orientation higher is the satisfaction level of women entrepreneurs stands accepted.

Conclusion and discussion

In the recent era, the Indian women entrepreneurs are eager to do the business. Women have been taking interest in income generating activities through entrepreneurship. This study examines different factors affecting women entrepreneurial orientation and satisfaction. The results revealed that women who are oriented towards their business have a high level of satisfaction (Covin & Slevin, 1989). The intensity of different factors (positive and negative) like social, psychological, financial, problem, pull, push are the deciding element for orientation and satisfaction of women entrepreneurs. The study further analyzed that the pull factors motivate women entrepreneurs to enter into business field and affect the orientation towards business (not in-line with earlier studies by Humbert & Drew, 1998) and thus they reflect higher satisfaction as compared to women entrepreneurs who are motivated through push factors (Mukherjee, 2006). The study results illustrate the importance of financial factors with regard to satisfaction level of women entrepreneurs. Financial help from the government as well as from their families to support the business affect their satisfaction level but it does not hold good in the case of their orientation because satisfaction is more reflected in the financial gains of the business, which can occur only when they have initial access to it, whether through financial institutions or through their families.

The study further proves the relationship between psychological factors and women entrepreneurs orientation as it plays an important role in the orientation of women entrepreneurs because need to achieve power and affiliation are all reflected through psychological characteristics (McClelland, 1996). If the women entrepreneurs are ready to learn the new techniques only then they can implement new innovations as they are seldom afraid of failure. Psychologically they have the courage to face the failures and remain in the business, which is reflected in this study.

Further, the study revealed that social support affect orientation of women entrepreneurs that is consistent with the results of the study by Nair and Pandey (2006). Helpful spouse is a source of motivation for women entrepreneurs as positive moral support encourages them to face the world more boldly. Further, this phenomenon is strengthened if family and society also motivates and support them. Previous literature (Ganesan et al., 2002; Nigam & Sharma, 1997) hints at work and worker-related problems but does not hold true here. The study reflects that they are also willing to take a business risk, which reflects their level of orientation for their business. It is concluded that women entrepreneurs have come of the age and they know how to tackle the work-related problems. It further reflects their confidence in running the business.

Suggestions

The results of the study show that women entrepreneurs faced a number of problems. The following suggestions are recommended to overcome the problems:

1. Banks and financial institutions must come forward to support and motivate them to start the units.

2. Financial help should be provided to women entrepreneurs by government as well as non-government financial agencies as it removes their difficulty in procuring loans.

3. Sources of power supply should be raised for women entrepreneurs. The power supply should be regular in general for the ventures being run by women entrepreneurs in particular. The government should provide power at low rate and other facilities related with electricity to those units, which are started and operated by women entrepreneurs.

4. Besides fiscal effort the entrepreneurship development agencies should create awareness among them regarding various loan schemes launched by the government from time to time.

5. Respondents have also communicated several financial problems in relation to non-availability of finance and their release in time. The attention of government and non-government organizations need to be drawn in rectifying these problems by making liberal assistance and gearing up the various facilities for enhancement of the status of enterprises.

6. Women entrepreneurs should be provided with special training and development programs for developing their innovative instincts.

Strategic implications for fostering women entrepreneurship in rural India

India has traditionally been a society with low participation of women in the economy. But the fact remains that women represent nearly 50% of the total population, and it is crucial to encourage women's role in the economy at every level. After independence of our country, the Government of India found that on the one hand industrial development was confined to a few developed cities and on the other it was concentrated in the hands of a few top business houses. At present, women's entrepreneurial role is limited in the large-scale industries and technology-based businesses. But even in small-scale industries, the women's participation is very low. Thus the government decided to promote entrepreneurial activities among women through various incentives in industrially backward and rural areas. The following are the key strategies that can help to foster women entrepreneurship in rural India:

1. Spreading awareness about government policies and regulations regarding business and industry among women entrepreneurs in rural India.

2. Government should review the existing regulatory framework like the working of Entrepreneurship Development Institute of India (EDII) and make necessary modifications to incorporate steps for spreading women entrepreneurship in rural areas.

3. The administrative hurdles should be reduced especially for women entrepreneurs in rural areas.

4. There should be a provision for easy and subsidized financing for women's entrepreneurial projects from state level institutions like Small Industries Service Institute (SISI) and state finance corporations (SFC).

5. Government agencies should facilitate the entry of women entrepreneurs from rural areas into the projects where high growth is expected by providing them necessary assistance.

6. In order to encourage women entrepreneurship in rural areas, the J & K government should provide incentives under industrial policy.

7. The government should provide the right type of infrastructural facilities and other financial incentives in rural areas through cooperative banks, commercial banks, and regional rural banks operated under NABARD leading to the emergence of entrepreneurial class in rural areas that can lead to entrepreneurial growth in India.

8. Special incentives, tax rebates, duty cuts, interest subsidy, and subsidized land and machinery can be provided to encourage women in emerging sectors.

9. Special recognitions and awards can be instituted for women participating in such targeted industries.

10. Higher education incentives for women from rural regions and advanced training programs for development of management skills among women, and setting up of polytechnics and industrial institutes for women are the key thrust areas to strengthen the women's entrepreneurial talent through education and training. Counseling in entrepreneurship through women oriented NGOs, cheap micro-financing, and bank support for new business projects launched by women entrepreneurs, and privileged infrastructural support such as priority in land allotment and administrative approvals can also promote the cause of women entrepreneurs substantially.

Limitations of the study

All possible efforts were made to maintain objectivity, reliability, and validity of the study, yet certain limitations could not be ignored and are required to be kept in mind whenever its findings are considered for implementation. These are:

1. The sample was restricted only to the entrepreneurs of Jammu.

2. The study has measured the factors affecting entrepreneurial orientation on the basis of the perception of the women entrepreneurs that might have been guided by their likes and dislikes.

Conflict of interest and funding

The authors have not received any funding or benefits from industry or elsewhere to conduct this study.