This article uses an adapted version of the United States Agency for International Development (USAID) (2005:5) funnel model, shown in Figure 1, to demarcate internal and external factors that might affect the growth of informal enterprises in Diepsloot, Johannesburg. The funnel model includes contextual factors related to the institutional, social capital, social exclusion and human capital theories.

Internal factors refer to business operational factors that are typically controllable and that arise, among others, from the education, knowledge and skills of managers and employees. External factors emanate from outside the enterprise as a result of the activities of other agents in the markets like suppliers, customers, competitors and government institutions (Hassan 2013:12). External factors are usually uncontrollable, but manageable, and are explained by the institutional, social capital and social exclusion theories.

The institutional theory discusses the impact of government and non-government institutions, responsible for formulating and implementing rules, on informal enterprise growth (OECD 2003:3). Government institutions attempt to reduce enterprise risks, stemming from economic uncertainty, by creating an adequate regulatory framework (Hassan 2013:7; North 1993:2).

Irregularities between laws and regulations for formal institutions and the norms, values and codes of conduct in informal institutions explain why some entrepreneurs opt to operate in the informal sector (Welter, Smallbone & Pobol 2015:1). Informal enterprises are usually small, not registered as an enterprise, do not pay taxes, use traditional and labour-intensive technologies and do not keep accounting records. Entry into the informal economy is usually easier than into the formal economy (De Beer, Fu & Wunsch-Vincent 2013:3; Grabrucker, Grimm & Roubaus 2018:49), but being informal excludes them from many business opportunities. Weak legal and institutional frameworks characterise informality, and the laws and policies are not effectively implemented (ILO n.d.:2).

The social capital theory analyses factors that affect the growth of an informal enterprise, through the lens of social networks. Social capital depends on a person’s contacts, people’s support and resource availability (De Graaf & Flaph 1988:462). Kondowe (2013:16) states that a requirement for enterprise growth is secure social networks. Most owners or managers of informal enterprises do not have socialising opportunities, skills and knowledge to collaborate and build networks for entrepreneurial growth.

Value chain linkages are also a driver for small and micro enterprise (MSE) growth. These linkages have direct effects on the growth of enterprises through the formation of vertical linkages between buyers and their suppliers, and horizontal linkages between the small enterprises themselves (USAID 2005:5, 11). Value chains are also necessary for the transfer of technology, skills or finance, which affect the growth of enterprises directly (USAID 2005:5).

The social exclusion theory provides deeper insights into informal enterprise marginalisation experiences and explains the effect of interlinking and mutually reinforcing problems in an area, like unemployment, discrimination, poor skills, low incomes and crime, that lead to other exclusions. According to Rann in Mathieson et al. (2008:12), an interplay of harsh, difficult and unjust economic conditions and social environments and insensitive government policies leads to social exclusion. Rogerson (2003:20) and Hassan (2013:4) confirm that the mere fact that an enterprise is informal and located in a specific area excludes them from the economic landscape, access to markets, market activities and resources that could grow the enterprise.

The human capital theory explains the human subject in an enterprise who takes charge of the economic activities such as production, consumption, and transactions. Human capital endowments correlate with enterprise growth as they contribute to production through knowledge, skills, attitudes, capabilities, aptitudes and other acquired traits (Kondowe 2013:15). Human capital consists of different types of skills like personal, technical, entrepreneurial, business operations and management skills that can be acquired through education and training (Kondowe 2013:15; Smith & Perks 2006:16). In most cases, informal enterprise owners, managers, supervisors, and employees do not possess the education and skills required to perform critical tasks. The lack of these characteristics constrains the growth of informal enterprises (Fadahunsi 2012:106).

The focus of the empirical literature is to highlight the results of previous studies on the variables used in the data analysis.

De Soto (1989:1) and Fatoki (2014:31) state that the stringent regulations and regulatory framework in which informal enterprises operate are restrictive and act as growth constraints to informal enterprises. Lack of legitimate and serviced trading spaces for unregistered informal enterprises pose a significant growth constraint for informal street traders in the cities of Johannesburg and Cape Town as well as informal enterprises in Diepsloot (Berry 2009:41; Sandeep 2014:202; Ukukhula 2003:9). Cupido (2002:1) agrees that informal enterprises often struggle to secure an area from which to operate. Hadebe (2010:3) indicates that most informal enterprises operate from home stands because they do not own property, trading space or stands. Ukukhula (2003:3) states that in the City of Cape Town, anyone wanting to access an area of operation must have a South African identity document and have a registered enterprise. This requirement excludes most informal enterprises from operating in business viable trading areas and makes them inaccessible to customers, potential partners and suppliers (Berry 2009:10). Informal enterprise owners are therefore deterred from making growth-enabling investments because of stringent regulations (USAID 2005:8).

A study by Kondowe (2013:2), among youth informal enterprise owners and four financial service providers in Cape Town, found that it is difficult for informal enterprises to secure capital from the private sector because of their informality. A study by Hassan (2013:9) also shows that micro, small and medium enterprises (MSMEs) in South Africa’s townships are less likely to have bank accounts and their credit penetration is generally low. The lack of property rights and assets that can serve as collateral by informal enterprise owners makes the sourcing of capital difficult (Sparks & Barnett 2010:6). Without collateral, lenders charge much higher interest rates, making it difficult for informal enterprises to obtain the necessary capital for either initiating new enterprises or expanding existing ones (Kondowe 2013:21). Informal enterprise owners have to find alternative ways to source capital to start or invest in their enterprises such as loans from informal sources, including family or informal moneylenders. Informal moneylenders, commonly referred to as ‘loans sharks’, charge inflated interest rates causing the informal enterprises’ owners to struggle to repay their debt (Willemse 2011:8). Hassan further points out that the difficulty in accessing finance increases the vulnerability and economic exclusion of township entrepreneurs. Informal enterprises are therefore caught in a vicious cycle of informality.

Evidence by Chebelyon-Dalizu et al. (2010:10) among spaza shop owners showed that most of these shops have a weak value chain and that the lack of a consistent distribution system, and getting the goods from suppliers to their shops, serves as a significant growth constraint. In some cases, suppliers deliver products to informal shops directly but other suppliers hire independent logistics service providers to deliver goods to these shops. Most informal shop owners have to use public transport like taxis and buses to buy their stock from suppliers (Chebelyon-Dalizu et al. 2010:14). A weak value chain increases transaction costs, making products more expensive, hindering competitive pricing and resulting in a shrinking customer base (USAID 2005:5).

Evidence from Woodward et al. (2011:67) and Hassan (2013:10) indicates that competition from shopping malls and supermarkets in townships also limits the informal enterprises’ market size, income and growth prospects as the shopping malls attract most customers. Woodward et al. (2011:83), however, state that in South Africa informal enterprises and shopping malls can coexist as informal enterprises serve as convenience stores and are in closer proximity to their customers. They further sell smaller quantities, making it a cheaper option for the poor, and operate at convenient hours.

Informal enterprise owners are also intimidated and affected by foreign-owned enterprises. Foreign-owned enterprises usually have more social capital in the form of business network connections and have high-turnover and low-margin strategies that translate into lower prices and more clients (Abdi 2011:700; Hassan 2013:16, 19). Evidence shows that a lack of inter-enterprise cooperation among informal enterprises as a result of competitiveness and jealousy jeopardises their ability to increase or maintain their income and profit levels (Bear, Tladi, Pedro & Bradnum 2005:17; Willemse 2011:9). Bear et al. (2005:17) also emphasise the importance of informal enterprise owners to organise themselves. Chebelyon-Dalizu et al. (2010:12) also stress that approaching suppliers collaboratively can benefit informal enterprises as they can collectively bargain for discounted prices, enabling them to provide their customers with more competitive prices.

The high crime level is one of the most significant growth constraints for informal enterprises (The South African Presidency 2008:7). The location of informal enterprises determines the level of crime. Informal enterprises located in townships face the highest rates of crime and lack protection against crime. Mengistae (2014:202) identified crime in Diepsloot as one of the most considerable growth constraints to informal enterprises. The cash-based nature of informal enterprises makes them appealing targets to criminals (Chebelyon-Dalizu et al. 2010:12–14). Chebelyon-Dalizu et al. (2010:13) found that most of the informal enterprises in their study have reported break-ins.

According to Willemse (2011:9), the lack of infrastructure can have a negative impact on the informal enterprises’ ability to operate their business and therefore their growth. This infrastructure includes access to roads and efficient and affordable public transport. Access to banks and post offices is essential for their financial needs. Infrastructure provided by municipalities like water, electricity and sewerage facilities are also important services that impact on the informal enterprises (Willemse 2011:9). Erastus, Stephen and Abdullai (2014:30) found that the lack of access to public services and delays in the delivery of public services impede the enterprises’ growth.

Informal enterprises are further excluded from the use of technology as they are unable to accumulate the necessary capital to invest in technology (De Beer et al. 2013:4). Their production is usually labour intensive using low-skilled workers and simple technology.

Smith and Perks (2006:1) determined that most informal enterprise owners have limited entrepreneurial skills, mainly resulting from a lack of formal education and training. Many studies (Chebelyon-Dalizu et al. 2010:13; Kondowe 2013:1; Rwigema & Karungu 1999:15) have established that a lack of education and training hinder the competence and competitiveness of informal enterprises, limiting their ability to grow. According to Morris and Pitt (1995:85), informal enterprises in townships generate more sales, create more jobs for family and non-family members, have higher growth prospects, and are more sophisticated when the owner, manager and employees have higher formal education levels and skills. The educational level of informal enterprise owners alone also increases their profits (Fourie 2018b:140). Woodward et al. (2011:73) found that business training is more likely to increase the income and sales of micro enterprises due to the variety of business challenges that their enterprises face. Often, informal enterprise owners have good ideas but they do not have the necessary skills to execute the ideas (Barron 2000:1; Brink 1997:364). Kondowe (2013:1) also found that it is unlikely for someone without relevant enterprise management skills to grow an informal enterprise. For informal enterprise owners and managers to increase productivity and income, their enterprise management skills have to be improved (ILO 2009:36).

Female informal enterprise owners face more constraints than men as they are more likely to experience ‘sociocultural constraints’ such as crime and gender stereotyping and discrimination (Co & Mitchell 2006:350). Berry’s (2009:1) study on challenges and coping strategies for informal female-owned street enterprises found that the lack of trading space is a major growth constraint. These challenges may make it difficult for them to develop professional relationships and enterprise networks with other shop owners or suppliers (Department of Trade and Industry 2008:10). Therefore, their male counterparts usually have higher income, sales and profits and are more successful in their enterprise ventures (Hassan 2013:13).

The enterprise and manager characteristics include the gender, age, level of education and other qualifications or training of the owner or manager (person in charge of the enterprise), whose idea it was to start the enterprise, the age of the enterprise, the reason for starting the business, whether the business sells a good or service or both and the location of the business.

According to Chandra and Rajaratnam (2001:17), the age of informal enterprises is an indicator of the experience of the informal enterprise’s owners in the informal economy and the enterprise’s chance of survival. There are mixed findings on whether skilled entrepreneurs run informal enterprises. Advocates of the legalist theory state that entrepreneurs resort to informal enterprises because of the high cost associated with the formal economy (Chen 2012:1). Another view is that informal enterprises are characterised as small, inefficient and usually run by poorly educated entrepreneurs (Rothenberga et al. 2015:5–6).

The theoretical conceptual framework and empirical results on informal enterprises in South Africa provide evidence that informal enterprises are affected by internal and external constraints. The growth constraining factors identified in the literature are used to analyse the factors that constrain the growth of informal enterprises in Diepsloot, Johannesburg. The growth of an enterprise can be measured by an increase in its turnover, revenue, employee numbers, sales or the movement of the enterprise into bigger trading premises (Bridge, O’Neill & Cromie 1998; Hadebe 2010:15). This article analyses the growth constraints of informal enterprises in Diepsloot in terms of the increase in the number of employees employed by the informal enterprises. This article will provide policymakers with the knowledge to design specific policies for creating an appropriate environment within which these enterprises can grow and sustain themselves.

This section discusses the demographic information, research population, sampling strategy, research instrument, design, data collection procedures and data analysis.

Diepsloot in Johannesburg was chosen for this study because it has a fast-growing population and hosts many informal enterprises (ed. Mahajan 2014:150) that are concentrated and conveniently located on the periphery of the township. A previous study in Diepsloot by Hassan (2013:15) indicated that the informal enterprises cluster in areas with a more extensive customer base and a variety of enterprises. Diepsloot harbours a rising class of ‘active entrepreneurs’ who run profitable, growth-oriented enterprises. Many of these informal enterprises are employment-generating businesses, and have managed to expand (Hassan 2013:17). These characteristics make Diepsloot an ideal township for a follow-up study to investigate growth constraints.

Primary data were collected from 300 of an estimated 1110 South African owned informal enterprises in Diepsloot. A sample size of around 286 enterprises was required to ensure validity of the study and to get results that represent the target population as precisely as possible (Creative Research Systems 2016:1). A convenience sampling technique was used to collect information from these enterprises. The reason for using this non-parametric sampling technique is that informal enterprises in Diepsloot are located close to each other, making it easier for field workers to collect data from many different types of informal enterprises.

A quantitative research approach was followed, and primary data were collected from the persons in charge of the informal enterprises (hereafter referred to as managers), using a structured questionnaire with closed and open-ended questions. A pilot study was conducted to test the questionnaire and to observe the location of the informal enterprises. Owners of four informal enterprises were interviewed in the pilot study and the questionnaires of these informal enterprises were included in the final data set as the questionnaire did not need any changes. During the pilot study, the need to appoint multilingual fieldworkers who could translate the questionnaire to participants with linguistic barriers was identified.

Information was gathered about the owners or persons in charge of the informal enterprises, enterprise characteristics, their perceived internal and external growth constraints and their recommendations regarding assistance required to grow their enterprises.

Data were collected in August 2017. The fieldworkers were trained before conducting the interviews to improve the validity and accuracy of the information gathered. All fieldworkers could translate the questionnaire. All ethical principles were adhered to throughout the study, and ethical clearance was obtained for the research.

The data collected were captured in Excel and analysed in Excel and STATA. The data analysis techniques include a descriptive analysis, matrix analysis and logistic regression models to provide for the different facets of information collected. In the first stage, a descriptive analysis is conducted to determine the profile of the ownership and management of informal enterprises. Frequencies, means, medians and correlations were used in the descriptive analysis. Secondly, a matrix analysis assesses the extent to which the owner or manager perceives some predetermined constraints as minor, moderate or major constraints for their enterprise’s growth. In the third stage, three logit regressions were applied. In all three logit models, the enterprise and manager characteristics were used as generic variables. In the first model, only the enterprise and manager characteristics were used. In the second model, only the internal constraints were added to the first model and in the third model, only the external constraints as perceived by the owner or manager were added to the initial model.

The growth of informal enterprises was measured according to the growth in the number of employees. The managers were asked whether the number of employees in the enterprise increased substantially, increased, stayed constant, decreased or decreased substantially. A binary logit model with a dummy variable was used with the value 1 if the number of employees increased and a value of 0 if the number of employees did not increase (decreased or stayed constant).

The logit model seems to be an appropriate model to use as Hassan (2013:20) adopted a similar model to examine constraints that hinder the growth of SMEs in Zanzibar. Adam (2014:133) applied a logit model to investigate constraints and growth potentials of MSEs in Mekelle City, Ethiopia, and Alemayehu and Gecho (2016) adopted it in their study to analyse the determinants of MSE growth in several regions in Ethiopia.

The equation for the logistic regression model is: Zi=β0+β1X1+β2X2+β3X3+β4X4+…+βnXn+Ui

The dependent variable Zi is the probability of the informal enterprise growing in terms of the number of employees. The independent variables include the enterprise and manager characteristics and perceived internal and external constraints.

X is an explanatory variable that determines the informal enterprise growth or lack thereof. β0 is the constant term and β1–βn are coefficients to be estimated. Table 1 lists the variables used, the measurement units and variable types.

All ethical principles were adhered to throughout the study, and ethical clearance was obtained for the research on 24 July 2017. The ethical clearance number is FEFSREC2017080801.

As seen in Table 2, almost two-thirds (61.3%) of the 349 enterprise owners are male. Half (49.9%) of the enterprise owners are youths according to the broad definition of a youth (15–34 years). The dominating ethnical group is African (97.3%) with 2.6% Indian participants.

The managers of the informal enterprises are, in most cases (77%), the owners themselves with only 23% non-owner managers. The youngest manager is 17, whilst the oldest manager is 75, with an average age of 36. The majority (77.3%) of the managers have not completed their secondary education, and 6% have no school education. The mean education level of the managers is Grade 9, and the median is Grade 10, implying that the grades are ‘skewed to the left’. Only 18.3% and 14% of managers have other qualifications and training.

Only 22% enterprises had more employees than when they started. Approximately 6.6% had fewer employees, and the majority (71.3%) of enterprises’ number of employees did not change. Table 3 shows the total number of employees in the informal enterprises at start-up and their current number of employees. The permanent full-time employees decreased slightly from 88% to 83%. Additionally, the number of casuals, permanent and temporary part-time and temporary full-time employees has increased slightly. Informal enterprises in Diepsloot, therefore, provide secure employment opportunities to people as they predominantly employ permanent full-time employees.

The age of the informal enterprise, measured from the month and year it started operating until the month-end of August 2017, shows a minimum enterprise age of 1 month and a maximum of 44 years. The result implies that this enterprise was in that area before the establishment of Diepsloot in 1995. The mean and median age of the informal enterprises is 83 months and 63 months (Table 2). According to Fourie (2018b:132), there is a positive relationship between the age of a firm and profits, and a negative relationship between age and employment growth.

The majority (70.3%) of informal enterprise owners in Diepsloot sourced their start-up capital from their savings, 18.3% obtained it from family and friends, 2.7% from micro-lenders, 0.7% from bank loans and 8% from other sources, as indicated in Table 4.

The majority (85.7%) of the respondents indicated that the decision to start the enterprise was that of the owners of the enterprise while only 6.3% of the enterprises were started based on the ideas of the managers.

Approximately 6% were initiated by partners, and in 2% of the cases, the idea came from family members and friends. More than half (56%) of the enterprises were started because the owner had no alternative employment while 13% were started to subsidise and supplement the owners’ low household income. Only 7% of the enterprises were started because of the innovativeness of the owners, 6% were retrenched or dismissed from their previous jobs, 5% inherited their enterprises, 5% started the enterprise because they prefer self-employment, and 8% started because of other reasons.

Of all the informal enterprises, 65.7% sell products, 20.3% sell services, and 14% sell both products and services. Informal enterprises that sold both products and services were for example hair salons (that sell beauty products and braid hair), car enterprises (that wash cars, fix cars and sell car parts) and internet or phone cafes (that provide internet services, sell airtime and other products).

Most informal enterprises (44%) operate on the street sidewalk, requiring no rental payments, with many people passing by on their way home from work. Another 20.3% operate from their homes, also not requiring paying rent to operate. Those operating on stands at taxi ranks near commuters, or on stands in commercial business areas where the rent had to be paid, made up 32.3% and 3.4% respectively.

To identify the factors that constrain the growth of the informal enterprises, the manager had to indicate, on a list of factors provided to them, which factors constrain the growth of their enterprises. They had to rate the extent to which each factor constrained the enterprise’s growth. A rating of 1 indicates a ‘minor constraint’, 2 a ‘moderate constraint’, and 3 a ‘major constraint’.

Table 5 displays the matrix of enterprise growth constraints as perceived by informal enterprise managers.

More than 70% of managers see a lack of proper infrastructure, price of raw materials, merchandise and inputs, and competition as growth constraints. More than 50% ranked these as a significant constraint. More than half of the enterprises ranked cost of transportation, lack of savings, limited access to government funding, lack of land ownership, lack of business management skills and inadequate educational attainment level of managers, lack of electricity, inadequate roads and streetlights, competition from formal shopping malls in the area, and competition from foreign traders as growth constraints.

The relationship between the enterprise’s growth and the enterprise and manager characteristics, internal and external constraints, and the significance thereof, are analysed with logistic regression models. The dependent variable is enterprise growth, measured by whether or not there was an increase in the number of employees since the enterprises started. The internal and external constraints used are those perceived by the managers as a constraint to the growth of the enterprise. The first model (Model I) only controls for the enterprise and manager characteristics. The second model (Model II) controls for the enterprise and manager characteristics and the internal growth constraints, and the third model (Model III) controls for the enterprise and manager characteristics and the external growth constraints.

The results from the logistic regression controlling only for enterprise and manager characteristics showed that informal enterprises with male managers were more likely to grow than those with female managers. The odds ratio of 1.846471 is statistically significant at a 10% level. Controlling for enterprise and manager characteristics and internal growth constraints (Model II), enterprises with male managers had a 97% higher likelihood to have an enterprise that grows than those with females at a 10% statistical significance level.

Controlling for enterprise and manager characteristics and external constraints (Model III), the odds ratio for gender (1.655567) showed similar results but was not statistically significant. The age of the managers does not seem to predict whether an informal enterprise is more or less likely to grow.

Higher levels of education and additional qualifications of managers seem to increase the likelihood of enterprise growth, but the results are not statistically significant. However, informal enterprises whose managers have additional training are between three (in Model I and Model II) and five times (in Model III) more likely to grow than enterprises with managers without training. Additional training is statistically significant at a 1% level in Model I and Model III, and a 5% level in Model II. Chebelyon-Dalizu et al. (2010:13) agree that a lack of enterprise management skills is one of the significant challenges that many informal enterprise owners face.

When the idea of starting the enterprise came from someone else besides the owner, the chances of an enterprise growing is higher. When the start-up was initiated by an idea from a partner, the enterprise was 3.4, 3.7 and 3.3 times more likely to grow in Model I, Model II and Model III. This finding is statistically significant at a 5% level in the first two models, and 10% level in Model III.

Every additional month that the enterprise operates seems to increase the enterprise’s likelihood of growth. These results are statistically significant at a 1% level in all models.

The different reasons for starting the enterprise revealed no statistically significant results except in the case where the enterprise was started to have freedom of self-employment. Enterprises with this motive are more likely not to grow, and the result is statistically significant at a 5% level in all models. Enterprises that were started because of market opportunities (entrepreneurial motive) were the only motive that makes an enterprise more likely to grow in all three models with odds ratios of 2.16, 1.93 and 1.84, although the results are not statistically significant. In cases where the motive for starting the enterprise was unemployment, low income, retrenchment, freedom of self-employment and entrepreneurship, the likelihood of no enterprise growth is higher.

Enterprises that sell services and both products and services are more likely to grow than those who only sell products. Enterprises selling services have the highest likelihood of growth with odds ratios of 2.23, and 2.07 in Model I and Model II and 2.86 in Model III. These results are statistically significant in all models. The location from which the enterprise operates did not yield any significant results. These results confirm the results of a national study by Fourie (2018b:133) over the period 2009–2013 that the location from which the informal enterprises operate was not found to be statistically significant in the propensity to increase employment.

Enterprises that perceived a lack of their own money, inadequate education of managers, lack of entrepreneurial skills of employees, credit purchases by customers and customers defaulting on credit as a constraint are less likely to experience enterprise growth. Enterprises that perceived inadequate education of employees, lack of business skills and entrepreneurial skills of the manager, employee cost and theft by employees as a constraint are more likely to experience enterprise growth, but the predictions are weak.

Enterprises who perceived a lack of agency funding, commercial bank funding, land ownership, proper roads and street lights, and sewerage facilities as growth constraints are more likely not to grow with odds ratios of 052859, 0.3707268, 0.4009038, 0.761923 and 0.2718777. The result for lack of agency funding is statistically significant at a 1% level, and the results for lack of sewerage facilities are statistically significant at a 5% level.

Enterprises who perceived high transport costs and a lack of non-financial support from the government as a constraint to their enterprises’ growth are also more likely not to grow with odds ratios of 0.4891801 and 0.7697399, but these results are not statistically significant. Although the results for high transport costs as a growth constraint were not statistically significant, Hassan (2013:18) points out that it is indeed a constraint faced by informal enterprises in Diepsloot that should be addressed. Chebelyon-Dalizu et al. (2010:14) confirm that high transportation costs are a growth constraint for informal enterprises.

The managers of enterprises that are more likely to grow perceived a lack of micro-lender funding, proper infrastructure, water and electricity supply, competition from foreign traders and shopping malls, price of raw materials, cost of employee strikes, cost of technology, theft by criminals and lack of security as growth constraints. The odds ratio of the lack of micro-lender funding as a growth constraint is 18.73403 and statistically significant at a 1% level. The odds where managers perceive a lack of proper infrastructure, water and electricity supply as constraints to the growth of their enterprises are 2.067268, 1.601059 and 1.2939698 but are not statistically significant.

Managers who perceive competition from foreign traders and shopping malls as constraints are also 1.80 and 4.69 times more likely to have an enterprise that grows than those who do not perceive it as a constraint. The results for the competition from shopping malls are statistically significant at a 1% level.

The odds for the price of raw materials, the cost of technology, criminal theft, lack of security and cost of employee strikes are 1.37089, 1.73238, 1.017329, 2.50097 and 1.452921. None of these results are statistically significant.

The Hosmer-Lemeshow goodness of fit test results of 12.76, 8.70 and 3.12 with probabilities of 0.1203, 0.3678 and 0.9265 imply good model fit and confirm that the models used are correctly specified and fit the data adequately. Collin’s multicollinearity test results (1.12, 1.5 and 1.96) show no multicollinearity concerns.