Home based business (HBB) is a small business that operates from the business owner's home office [1] Home businesses are having a very small number of employees, usually all immediate family of the business owner, in which case it is also a family business. Home businesses generally lack shop frontage, customer parking and street advertising signs Helmrich, [2]. The use of the home as a business pace can provide a considerable advantage in establishing a business. For instance, it can reduce initial capital requirement, but arguably forming a home based business rely heavily upon clients accepting the home as the space in which business will take place, and more broadly, accepting home based business as serious business entities Evangelista & Savona, [3]. Home-based businesses can be any business, either product or service, production and/or sales [4]. According to Clark and Douglas, In spite of being ‘microscopic’ in size, HBB were operating with sales across the full spectrum of geographic regions from local, to regional, national and international. HBB is defined as “any business or self-employed person that uses a residential property as a base from which they run their operation – consciously doing so instead of running a separate workspace/ shop/office”[5].      

Home Business in Oman

Home business is a new tributary to the national economy, as they reduce unemployment rates and job opportunities, which have recently increased with the outbreak of the new Coronavirus, which has led to many losing their jobs and jobs, due to the months-long closure in most countries of the world. The Sultanate of Oman is witnessing widespread activity in the emergence of a large number of home businesses, which benefit from the remarkable development in advertising and electronic marketing methods spread on the Internet and social media. Home  business   began   to    receive    government support through long-term loans, with the aim of continuing on the ground and developing them, to become registered and legal companies with greater income for the Omani family. Although the idea of home business is old in Oman, it has started to gain greater popularity during the past few years, as it requires much less capital than the common commercial project, and work on it starts from the house with tools available or easily obtainable. The number of home businesses increased by 6.16% in 2019, and reached 1687 businesses registered with the Omani Ministry of Trade and Industry, which confirms the interest of Omanis and residents in them. With the outbreak of the new Coronavirus, and the imposition of curfews during the first six months of 2020, the Omani government recorded 7,706 new home activities, a very large increase compared with the past few years combined, as many people took advantage of staying at home to revive their skills, and started small projects. They promote it on the Internet and social media, with delivery service, and at competitive prices [6].

Growth of Homebased business in Oman

The number of home business activities that were registered with the Ministry of Commerce and Industry until the end of June 2020 reached 7706 activities. The Secretary of the Commercial Register at the Ministry of Commerce and Industry confirmed, "The Ministry encourages the owners of home business, small, medium and large enterprises to promote their products, and use modern methods and technologies in marketing to them inside and outside the Sultanate, with a commitment to be within the framework of home business licenses." [7]. The number of commercial registrations done in the Ministry of Commerce, Industry and Investment Promotion reached 1,860 during the second quarter of the year 2020. The statistics department of the ministry said that of these registrations, there were 972 for an individual business, 296 for limited liability companies, 216 for individually owned companies, 101 for investment companies, and 99 for household businesses, 96 for non-profit organization companies, 44 for joint liability companies, 23 for street vendors, 12 for limited companies, and one for a branch of an international company [8]. Ministry of Social Development (MoSD) targets 1,000 such families to train them in marketing and modern selling skills. The initiative will last three years in all governorates of the Sultanate. This year, it will target ten governorates. It will conduct around 11 workshops targeting about 330 people every year. Specialists will explain modern marketing methods, service development, product packaging and marketing via social media. They are seeking cooperation from colleges and universities, the Ministry of Health and Oman Chamber of Commerce and Industry [9]. Home business owners can obtain a home business license electronically via self-service RO for three years. It is worth noting that the number of home business licenses amounted to 7,795 licenses as of April 30, 2020.[10]. The Authority for Small and Medium Enterprises Development (Riyada) has issued a detailed statement on incentives offered to owners of small and medium enterprises (SMEs) with a view to writing off SME license fees. The incentives target SMEs operating in the sectors of manufacturing industries, agriculture and fisheries, services, logistics and related activities, according to the Ministry of Commerce and Industry and Investment Promotion. The incentives are a one-activity, one-time offer to Riyada card holders. It is a condition (for exemption from government license fees for the target activities) that the licenses be issued for the first time. It is not allowed to sell, transfer or modify the commercial register of the beneficiary SME for a period of three years, except in the case of transfer of the commercial register to a full-time citizen who is a holder of the Riyada card or in the case of cancellation of the establishment by the cancellation of its commercial register. According to this statement, the beneficiaries are exempt from payment of all government license fees. Holders of Riyada cards are also exempt from payment of labour license fees for new activities specified in the sectors covered by the incentives, towards a total of 5 workers and for a single activity. The exemption does not cover payment of fees for renewal of existing government licenses across all sectors [11].

Objectives of the Study

• To study the correlation of factors that affect home business owners

• To identify the major factors that influence the homebased business units   

• To categorize the component factors of home based business units based on factor analysis

Literature Review

Zainab [12] identified factors influencing home- based business performance like owner’s gender, sources of finance, information and communication technology, age of business, managerial know- how, customer, and innovation plays a significant role in the performance of small business in which home based is one. Home- based business has great potential of becoming large businesses if properly managed and will contribute towards economic development. Most businesses actually start off as small enterprises and often from a modest home-base, so the decision to grow and potentially move from a home-base has personal implications for the owner-operator in relation to aspects such as additional risk, both of a financial and emotional nature. Home-based businesses are a large component of the business sector in Australia and they are the biggest single sector, accounting for 58% of all businesses [13]. Women make up 33% of small business operators in Australia [13], and they do tend to operate at the small end of the small business spectrum.  Non-financial measures of success used by business owners, such as autonomy, job satisfaction or the ability to balance work and family responsibilities [14-17] are subjective and personally defined and are more difficult to quantify. Assibey et al. [18], sought to find out the nature of finance on micro, small enterprises in Ghana, like a loan, grant, or self -raised finance mattered for their productivity edge and growth, suggest that where the nature of enterprise’s start-up capital was loaned or debt finance, compared to grant finance, the enterprise is associated with a higher productivity edge. Kalleberg & Leilch, examined that women were no more likely to fail in business and were just as successful as men, suggesting that the process underlining small business is similar irrespective of the gender. The procedures involve in operating a small business is almost the same and can manage effectively by both genders depending on their enthusiasm to move the business forward. Fairlie and Robb [19], suggested that female-owned businesses are less successful than male-owned businesses because they use less start-up capital, have less prior work experience in a similar business, and less prior work experience in a family business. Daskalakis, Jarvis and Schizas, [20] while identifying financing practices and preferences for micro and small firms in the United Kingdom, outline the major sources of funding business that is equity financing, debt financing and grant financing and further suggest that regarding equity financing, firms depend heavily on their own funds and would not raise new equity from sources outside. Åstebro and Bernhardt, [21], opined that a bank loan is likely to improve the survival chances of a new small business for many reasons. Start-up company survival based on owner human capital, loan sources, and wealth as well as industry and company characteristics showed indeed that having a bank loan is a significant positive predictor of the survival of start-up companies. Therefore, the source of funding business is a major determinant of business performance. Small business that depend on bank loan tend to have high performance that those with grant from government agencies and other donor agencies.

It is a descriptive study as it covers the factors affecting home business owners registered and non-registered. The study area selected for this research is Nizwa. The period of study is from February 2021 to April 2021. According to National Center for Statistics and Information, the total number of small scale units registered in Ad Dhakliyah is 600 from January 2020 to November 2020. This is taken as the sampling frame. The sample size is determined as 234 units. These samples are collected from registered and unregistered home business owners from Nizwa. The sampling method adopted is simple random sampling method, where in the home owners are contacted on a random basis. Data is collected from primary and secondary sources. Primary data is collected with the help of a structured questionnaire shared online using Google form in both English and Arabic. Pilot study is conducted among 15 respondents to identify the validity of the questions. 

Analysis of the Study

The sample size determined is 234 and the data is collected from 129 responses. Thus the response rate is 55%. The demographic profile showed that 66% are females and 34% are males. The reason for more number of females entering into homebased business is due to lack of employment opportunities for women. Also most of the women feel free to work from home. It is clear that 60% of the home business owners are in the age category of 20 years –<30 years and 26% of them are in the age group between 30 to 40 years. 8% of the homebased business owners are less than 20 years. This shows that there is a drive among the young female population to enter into homebased business and be independent. This is also a good sign of being independent and earning by themselves for their livelihood. The marital status revealed that 36% of the home business owners are single and 64% of them are married. This also shows the growing demand of housewives entering into homebased business units as they can be independent and can manage the business from home itself.  41% of the home business owners have family members of 6 and more. 25% of the owners are having a family of 5-6 members. It is observed that the number of large families are the owners of home business units in contrast to the number of small families. The educational status of the home business owners is analysed and is found that 36% of them are educated upto high school level. 27% of them are diploma holders and 10% are Bachelor holders. This shows that home business owners who started their own business are educated.

Table 1 shows the descriptive analysis of the factors that influence homebased business units in Nizwa. The mean value is high for the factor ‘Manage business effectively’ (4.29) and the standard deviation is 1.03. The next factor is “Liking and Interest for home business” with a mean of 4.25 and a standard deviation of 1.20. The low average is for the variable ‘cover cost’ (2.85) with a standard deviation of 1.40. The mean is highest for the attribute ‘manage business effectively and the standard deviation is also low. This shows that the data points tend to be close to the mean.

Table 1: Descriptive Statistics

SPSS output shows the abridged version of R-matrix. The top half of Table 2 contains the Pearson correlation coefficient between all pairs of questions whereas the bottom half contains the one-tailed significance of these coefficients. By scanning the significance values it is clear that majority of the values are less than 0.05and no values are greater than 0.9. Hence, there is no need to eliminate any variable. There is no problem of multicollinearity. Thus all the questions correlate fairly well and none of the correlation coefficients are particularly large. The determinant of correlation matrix value is 0.035 which is greater than 0.00001 also supports the correlation matrix that can be used for factor analysis. Therefore, there is no need to consider elimination at this stage.

Table 2: Correlation Matrix

a. Determinant = .035

The Kaiser-Meyer-Olkin measure the sampling adequacy The KMO statistic varies between 0 and 1. A value close to 1 indicates that patterns of correlations are relatively compact and so factor analysis should yield istinct and reliable factors. Kaiser recommends accepting values greater than 0.5 as acceptable. Values between 0.5 and 0.7 are mediocre, values between 0.7 and 0.8 are good, values between 0.8 and 0.9 are great and values above   0.9   are   superb. For   this data the value is 0.775, which falls in the range of good. Therefore, factor analysis is appropriate for these data. Bartlett’s measure tests the null hypothesis that the original correlation is an identity matrix.  For factor analysis to work some relationships between variables and if the R-matric were an identity matrix then all correlation coefficients would be zero. For the test to be significant, the significance value should be less than 0.05. For these data, Bartlett’s test is highly significant (p<0.001) and therefore factor analysis is appropriate Table 3.

Table 3: KMO and Bartlett's Test

Principal component analysis works on the initial assumption that all     variance    is     common.   Therefore, before extraction the communalities are all 1. The communalities in the column labeled extraction reflect the common variance in the data structure. 72.6% of the variance is associated with factor (manage business effectively) is common or shared variance. Similarly, 70.8% of the variance    is   associated    with   the    factor (started as hobby) is common or shared variance. Another way to look at these  communalities  is   in   terms   of   the proportion of variance explained by the underlying factors. After extraction some of the factors are discarded. The amount of variance in each variable that can be explained by the retained factors is represented by the communalities after extraction Table 4.

Table 4: Communalities

Extraction Method: Principal Component Analysis

The Table 5 total variance lists the eigenvalues associated with each linear component or factor before extraction and after extraction. The eigenvalues associated with each factor represent the variance explained by that particular linear component and SPSS also displays the eigenvalues in terms of the percentage of variance explained. The factor “Liking and interest for home business explains 30.09% of total variance. The first three attributes explain relatively large amounts of variance, especially factor 1, whereas subsequent factors explain only small amounts of variance. SPSS then extracts all factors with eigenvalues greater than 1, which leaves with three factors. The eigenvalues associated with these factors are again displayed and the percentage of variance explained in the Extractions Sum of Squared Loadings. The values in the column are the same as the values before extraction. The values for the discarded factors are ignored and the table is blank after the third attribute.  Similarly factor 2 ‘Cannot afford a place for business’ accounts for 14.22% of variance. The third variable “cover cost” contributes 13.91% of total variance. Thus 58.29% of the cumulative variance is contributed by the first three variables and remaining factors contribute 41.71% of cumulative variance. In the final part of the table labelled Rotation Sums of Squared Loadings the Eigenvalues after rotation are displayed. Rotation has the effect of optimizing the factor structure and the relative importance of these factors are equalized. Before rotation the first two factors of home business accounted for considerably more variance (34% and 14.56%) than the remaining factors. However, after extraction the first factor ‘liking and interest’ accounts for only 30.09% of variance and the second factor (14.22%) as compared to other factors.

Table 5: Total Variance Explained

Extraction Method: Principal Component Analysis

A scree plot visualizes the Eigenvalues in the diagram. The first 3 components have Eigenvalues over 1 and they are considered as “strong factors”. From the Scree plot (Figure 1) it is clear that after the third factor the curve begins to tail off. Therefore, it is probably justifiable to three factors. This output shows the rotated component matrix (also called as the rotated factor matrix) which is a matrix of factor loadings for each variable onto each factor. This matrix contains the same information as the component matrix except that it is calculated after rotation. Factor loadings less than 0.3 are not loaded as it is suppressed. The variables are listed in the size of their factor loadings. Before rotation, most variables loaded highly on to the first factor. After rotation the factor structure has clarified. There are eleven factors and the variables are loaded equally. The suppression of factors less than 0.3 and ordering variables by loading size also makes interpretation easier.

Figure 1: Scree Plot for Component Extraction

The component matrix is shown in Table 6 before rotation. This matrix contains the loadings of each factor into each factor. All the loadings less than 0.3 are suppressed in the output so there are blank spaces for many of the loadings. The above table shows the loadings (extracted values of each item under 3 variables) of 11variables on the 3 factors extracted. The higher the absolute value of the loading, the more the factor contributes to variable. Three variables are extracted wherein the 11 items are divided into 3 variables according to the most important items with similar response in component 1, component 2 and component 3. At this stage SPSS has extracted three factors. By Kaiser’s criterion, three factors are extracted and is proved accurate. The criteria is accurate when communalities after extraction is greater than 0.7. The second ground for accuracy is when the average communalities is greater than 0.6. The average of the communalities is found by adding the communalities divided by the number of factors (0.58). Thus on one ground Kaiser’s rule is accurate. A model that is a good fit will have less than 50% of the non-redundant residuals with absolute values that are greater than .05 which is true for our example. Since there are cross loadings, the factors loadings are redistributed by rotation. A cross loading is when an item loads at .32 or higher on two or more factors.

Table 6: Component Matrix^a

Extraction Method: Principal Component Analysis. a. 3 components extracted

Table 7 shows the rotated component matrix or the rotate factor matrix in factor analysis. This is a matrix of the factor loadings for each variable onto each factor after rotation. The idea of rotation is the reduce the number of factor on which the variables under investigation have high loadings. The six variables that load highly on factor 1 (Hobby, manage effectively, liking and interest, convenient, good demand, and cover the cost) relate to personal preference and interest to start home based business. Therefore, it is labeled as Liking and Interest. The three variables that load heavily on factor 2 (No need of a commercial place, low risk, accommodate family needs) relate to the easiness of business and is labelled as Nature of Business. The two variables that loads heavily on factor 3 are related to variables (cannot afford a place for business and flexibility) and are labelled as Affordability and Flexibility. Thus this analysis reveals that the initial questionnaire is composed of three sub-scales: Liking and Interest, Nature of Business, Affordability and Flexibility.

Table 7: Rotated Component Matrix^a

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.^a a. Rotation converged in 5 iterations

The component transformation matrix simply displays the component correlation matrix prior to and after rotation. The original factor or component loadings are transformed to the rotated loadings by post-multiplying the matrix of original loadings by the transformation matrix. The values in the transformation matrix are functions of the angle of rotation of the factors or components (Table 8).

Table 8: Component Transformation Matrix

Extraction Method: Principal Component Analysis.  Rotation Method: Varimax with Kaiser Normalization

Findings of the Study

• The correlation value R is highest for the factors convenient (0.617) and managing business effectively (0.564)

• The factor “Liking and interest for home business explains 30.09% of total variance. The factor 2 ‘Cannot afford a place for business’ accounts for 14.22% of variance. The third variable “cover cost” contributes 13.91% of total variance. Thus 58.29% of the cumulative variance is contributed by the first three variables

• After the rotation through rotated component matrix, factor analysis derived three components or variables that influence home based business units. The three sub-scales are labeled as ‘Liking and Interest, Nature of Business, Affordability and Flexibility’ 

Recommendations

• The government agencies like Chamber of commerce, Riyadha and Al Rafd should be more responsible in supporting home business owners

• A training program can be organized by the Ministry of Commerce to cooperate with the Sanad office in order to educate home business owners about the importance of registering

• Young Omani women people should be motivated to take advantage of business opportunities in home businesses as there is a tremendous growth in this business for the last 3 years. Therefore, government bodies should integrate with the Universities and facilitate exhibitions and fairs for these units

• There is a need of awareness program organized by the government bodies like Oman Chamber of Comerce, Sanad Office, Riyadha and Al Rafd  

This paper explores the attributes that influences homebased business units in Nizwa, using factor analysis. In all aspects, the data was appropriate to conduct factor analysis and hence sound conclusions could be drawn from this analysis. A principal component analysis has been carried out with varimax rotation. This resulted into three correlated factors, constituting the reasons for starting home based business units.  i.e. Liking and Interest, Nature of Business, Affordability and Flexibility’. The measurements of the three components loaded on different factors, which could indicate the different factors that influence home based business owners. This can be important for the government and young entrepreneurs, as it gives opportunities to develop the economy. 

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