1. Introduction
ervices sector is the fastest growing sector in India and is projected to have high growth in future. A major contributor among huge service sector is the insurance sector which plays an important role in enhancing financial intermediation, creating liquidity and mobilizing savings in the country. The Indian life insurance industry remained a monopoly of Life Insurance Corporation of India (LIC) till it was liberalized in 1999. At present, there are 24 life insurance companies operating in India with LIC being the only public sector life insurer and the balance being private players.
Presently, there are 36 crore life insurance policies in India making it the biggest player in the world for life insurance. India's insurable population is anticipated to touch 75 crore in 2020. India was ranked 10th among 147 countries in the life insurance business in financial year 2013 with a share of 2.03 percent. The life insurance industry in India is projected to increase at a compound annual growth rate (CAGR) of 12-15 per cent in the next five years. The industry has the potential to top the US$ 1 trillion mark over the next seven years (IBEF, 2014). According to Insurance Regulatory & Development Authority (IRDA), insurance services sector grew by 8.6 percent and the total premium for the life insurance sector was Rs. 2.87 lakh crore (IRDA Annual Report 2012 -13).
With most life insurance companies offering similar policies, product differentiation is tough in increasingly competitive market. As a result, Insurance companies in India are now moving from a productcentered approach to a customer-centered strategy. The focus is on enhancing customer satisfaction through improved service quality which leads to improved customer retention, loyalty and profitability. In order to survive and thrive in the competitive insurance industry, life insurers are actively engaged in developing new strategies for customer satisfaction through proper improvement of service quality.
With increased awareness level, the consumers demand higher standard of services and insurance sector is getting more and more competitive. Customers are becoming increasingly aware of the options on offer in relation to the rising standards of service (Kris hnaveni et al, 2004). They demand better quality service. Delivering quality service is considered an essential strategy for success and survival in today's competitive environment (Dawkins and Reich held, 1990; Parasuraman et al., 1985; Reich held and Sasser 1990; Zeithaml et al., 1990). More specifically, the cost of retaining existing customers by enhancing the products and services that are perceived as being important is significantly lower than the cost of winning new customers (Krishnan et al, 1999). Hence, to remain competitive, Life insurance companies need to focus on service quality.
Studies have shown that it costs six times more to attract new customers than to retain the existing ones (Rosenberg & Czepiel, 1983). It has also been suggested that service quality has a direct effect on organizations' profits as it is positively associated with customer retention and customer loyalty (Baker & Crompton, 2000;Zeithaml & Bitner, 2000).
Customer dissatisfaction has been found to have a greater psychological impact and a greater longevity compared to good experiences. As per estimates, two out of three times an unhappy customer will speak about a bad experience than relate to a good experience. Hence, there is a multiplier effect of poor service hurting not just the bottom line of an insurance company but bringing additional costs of losing potential customers in addition to existing ones.
The purpose of the present study is to measure customer's perception towards service quality of life insurance companies. The framework developed by Tsoukatos and Rand, (2006), Durvasula et al. (2004) and Mittal et al. (2013) has been used to find out customer's perception towards service quality dimensions of Life Insurance providers.
2. II.
3. Literature Review a) Service Quality
Extensive research has been undertaken on different aspects of service quality providing a sound conceptual foundation. Authors (Parasuraman et al., 1988;1991;Carman, 1990) agree that service quality is an abstract and elusive concept, difficult to define and measure. Empirically, various service quality models and instruments have been developed for measuring service quality. According to Gronroos (1982), there are two dimensions of customer's perceptions of any service, namely technical quality (what is provided) and functional quality (how the service is provided). Sasser et al. (1978) suggested three different attributes (levels of material, facilities, and personnel) all dealing with the process of service delivery. Subsequently, Gronroos (1990) identified six specific dimensions viz., professionalism and skills, reliability and trustworthiness, attitudes and behavior, accessibility and flexibility, recovery, and reputation and credibility, on which service quality could be measured. Lehtinen and Lehtinen (1982) discussed three dimensions viz., physical quality, involving physical aspects; corporate quality, involving a service firm's image and reputation; and interactive quality, involving interactions between service personnel and customers. Perceived service quality has been defined as a global judgment or attitude relating to the superiority of a service (Zeithaml and Bitner, 2000).
There are three types of customer expectations predicted service, desired service, and adequate service which presents a comparison between customer evaluation of service quality and customer satisfaction (Valerie A. Zeithaml, Lonard L. Berry, and A. . It has been found that investments in service quality, customer satisfaction and customer relationships result in increased profitability and market share (Rust and Zahorik, 1993). High-quality service and customer satisfaction often lead to more repeat purchases and market share improvements (Buzzell and Gale, 1997). Service quality is one of the effective means in building a competitive position in the service industry (Lewis, 1991). Customer satisfaction leads to customer loyalty and this leads to profitability (Hallowell, 1996).
The most widely used service quality measurement tools include SERVQUAL (Parasuraman et al.,1988;Boulding et al., 1993) and SERVPERF (Cronin and Taylor, 1992). The SERVQUAL model suggests that service quality can be measured by identifying the gaps between customers' expectation and perceptions of the performance of the service using 22 items and five-dimensions: reliability, assurance, tangible, empathy, and responsiveness. In the SERVPERF scale, service quality is measured through performance on score based on the same 22 items and five dimensional structure of SERVQUAL. The SERVQUAL have been used to measure service quality in the insurance industry (Stafford et al., 1998 Experts have claimed that the number of service dimensions is dependent on the particular service being offered. According to Babakus and Boller (1992), the domain of service quality may be factorially complex in some industries and very simple and uni-dimensional in others. The SERVQUAL scale has been presented in different dimensions in various studies -singledimensional (Babakus et al., 1993;Lam, 1997) (Headley and Miller, 1993), seven-dimensional (Sasser et al., 1978;Freeman and Dart, 1993), nine-dimensional (Carman, 1990), and nineteen-dimensional (Robinson and Pidd, 1998) construct.
Also, several scales have been replicated, adapted and developed to measure services by taking SERVQUAL as a base, viz., SERVPERF Taylor, 1992, 1994) for hotels, clubs and travel agencies; DINESERV (Stevens et al., 1995) for food and beverage establishments; LODGSERV (Knutson et al., 1990) for hotels; SERVPERVAL (Petrick, 2002) for airlines; SITEQUAL (Yoo and Donthu, 2001) for Internet shopping; E-S-QUAL (Parasuraman et al., 2005) for electronic services; SELEB (Toncar et al., 2006) for educational services; HISTOQUAL (Frochot and Hughes, 2000) for historic houses; LibQUAL (Cook et al., 2001) for library ; and ECOSERV (Khan, 2003) for ecotourism.
4. b) Service Quality in Life Insurance
Life insurance is a high credence service (Lynch and Mackay, 1985), very abstract, complex and focused on future benefits that are difficult to prove (financial protection etc.). Life insurance products provide very little signs to signal quality. It has been suggested that consumers usually rely on extrinsic signs like brand 20
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Volume VII Issue IX Version I Year ( ) image to ascertain and perceive service quality (Gronroos, 1982). Customer satisfaction in insurance is both difficult to measure and ascertain. The future benefits of the "product" purchased are difficult to foresee and take a long time to "prove" its effects (Crosby and Stephens, 1987). An extended period of time may be required in this industry for a fully informed evaluation (Devlin, 2001).
As the premium amount typically invested in an insurance policy is high, customers seek long-term relationships with their insurance companies and respective agents in order to reduce risks and uncertainties (Berry, 1995). Research have indicated that the key parameters, e.g. past experience, personal needs, external communication, word of mouth, and active clients significantly influence service quality of the insurance sector (Barkur et al., 2007).
In Indian context, measuring service quality on six dimensions, namely assurance, competence, personalized financial planning, corporate image, tangibles and technology dimensions, it was found that the priority areas of service were assurance followed by competence and personalized financial planning (Siddique & Sharma, 2010). Perceived service quality of life insurance services is a multi-dimensional secondorder construct consisting of the primary dimensions of Service Delivery, Sales Agent Quality, Tangibles, Value and Core Service (Mittal et al. 2013). Cultural factors were found to have significant influence on the expectation on service quality in Indian Insurance market (Meharajan and Vanniarajan, 2011). Three factors namely, proficiency; physical and ethical excellence; and functionality were found to have significant impact on the overall service quality of Life Insurance Corporation of India in a study based on seven-factor construct (Sandhu and Bala, 2011).
Strong relationship is found between satisfaction level and the service quality dimensions (Gayathri et al., 2005). Perceived service quality and customer satisfaction are dependent on information technology (Choudhuri, 2014). SERVQUAL construct cannot be applied to Indian Life Insurance sector and further research is needed to understand and improve life insurance service quality within Indian context (Bala et al., 2011). Demographic variables are related to eight service service quality factors namely, employee competence, creditability, timeliness and promptness, convenience, accessibility, communication, customer orientation and responsiveness (Bishnoi and Bishnoi, 2013). Product innovation, increased interaction level between agents and customers and technological upgradation affect the service quality perceptions of Life Insurance policyholders in Northern India (Chawla and Singh, 2008).
An insurance policy is almost always sold by an agent who, in most cases, is the customer's only contact (Richard and Allaway, 1993; Clow and Vorhies, 1993; Crosby and Cowles, 1986). Customers are, therefore, likely to place a high value on their agent's integrity and advice (Zeithaml et al., 1993). Service quality depends to a large extent on the information gathering and processing activities of agents (Eckardt and Doppner, 2010). The quality of the agent's service and strength of his relationship with the customer play a major role in customer purchasing the life insurance product. Putting the customer first, and, exhibiting trust and integrity have found to be essential in selling insurance (Slattery, 1989). According to Sherden (1987), high quality service (defined as exceeding "customers' expectations") is rare in the life insurance industry but increasingly demanded by customers.
In Insurance Industry, high retention rates are closely related to the economic performance of companies (Diacon and O Brien, 2002). The insurance industry considers that understanding consumer behaviour after the initial purchase will help insurers to maintain longer customer-insurer relationship (Harrison, 2003). Toran (1993) points out that quality should be at the core of what the insurance industry does. Customer surveys by Prudential have identified that customer want more responsive agents with better contact, personalized communications from the insurer, accurate transactions, and quickly solved problems (Pointek, 1992). A different study by the National Association of Life Underwriters highlighted other important factors like financial stability of the company, insurer's reputation, integrity of agent and the quality of information and guidance from the agent (King, 1992). Clearly, understanding consumers' expectations of life insurance agent's service is crucial as expectations serve as standards or reference points against which service performance is assessed (Walker and Baker, 2000). In a study conducted in Germany, the duration of counseling interviews is found to be the single most important factor that has a positive effect both on the information quality and on the total service quality provided (Eckardt and Doppener, 2010). Consumers tend to rate service quality higher if they are aware of their right to complain to the regulator (Wells and Stafford, 1995). Technology has also become an important factor in how the agent operates in the field including other functions such as distribution, claim costs and administration (Anonymous, 2004). Communication, ICT, customer's knowledge and prior experience influence the service quality in insurance industry (Saad et al., 2014).
Research has shown that the quality of service and the achievement of customer satisfaction and loyalty are fundamental for the survival of insurers. The quality of after sales services, in particular, can lead to very positive results through customer loyalty, positive word-of-mouth, repetitive sales and cross-selling (Taylor, 2001). However, many insurers appear unwilling to take the necessary actions to improve their image. This creates problems for them as the market is
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Volume VII Issue IX Version I Year ( ) extremely competitive and continuously becomes more so (Taylor, 2001).
Previous studies, notably those of Wells and Stafford (1995), the Quality Insurance Congress (QIC) and the Risk and Insurance Management Society (RIMS) (Friedman, 2001a(Friedman, , 2001b)), and the Chartered Property Casualty Underwriters (CPCU) longitudinal studies (Cooper and Frank, 2001), have confirmed widespread customer dissatisfaction in the insurance industry, stemming from poor service design and delivery. Ignorance of customers' insurance needs (the inability to match customers perceptions with expectations), and inferior quality of services largely account for this. The American Customer Satisfaction Index shows that, between 1994 and 2009, the average customer satisfaction had gone down by 2.5% for life insurance. However, post 2010 till 2014 there have been continuous improvement in the index as Insurers are now realizing the importance of service quality and its impact on customer satisfaction (www.theacsi.org, 2014).
It is therefore not surprising that measurement of service quality has generated, and continues to generate, a lot of interest in the industry (Wells and Stafford, 1995). Several metrics have been used to gauge service quality. In the United States, for example, the industry and state regulators have used "complaint ratios" in this respect (www.dfs.ny.gov, 2014). The "Quality Score Card", developed by QIC and RIMS, has also been used. However, both the complaints ratios and the quality scorecards have been found to be deficient in measuring service quality and need for a more robust metric is strongly felt.
Although service quality structure is found rich in empirical studies on different service sectors, service quality modeling in life insurance services is not adequately investigated. Further, for service quality modeling, a set of dimensions is required, but there seems to be no universal dimension; it needs to be modified as per the service in consideration. Thus, the dimensions issue of service quality requires reexamination in context of life insurance services.
7. III.
8. Objectives of the Study
The objective of the study was to find out the factors that affect the service quality of Life Insurance providers. It also studied the effect of demographic factors on customer perception and service delivery. In order to achieve these objectives, the following hypotheses have been formulated: H o1 -There is no relationship between the age of respondents and perception of service quality of Life Insurance providers H o2 -There is no relationship between the gender of respondents and perception of service quality of Life Insurance providers H o3 -There is no relationship between the education level of respondents and perception of service quality of Life Insurance providers H o4 -There is no relationship between the income of respondents and perception of service quality of Life Insurance providers IV.
9. Research Methodology a) Data Collection Method
The main instrument used for data collection in this research was the questionnaire. The responses have been collected through online survey using google docs and email. Prior to the final survey, the questionnaire was pre tested using a sample of respondents similar in nature to the final sample. The goal of pilot survey was to ensure readability and logical arrangements of questions. The questionnaire was sent to 25 respondents having a life insurance policy through email.
The responses of pilot study were thoroughly analyzed. The questionnaire was reviewed in light of comments and shortcomings and then it was revised accordingly. The final questionnaire was uploaded on Google docs and the link was sent to 200 customers and 139 usable responses were received, thereby making a response rate of 69.5%.
The perception of the respondents towards the service delivery quality was gauged using a questionnaire containing close-ended questions, which were designed to ascertain perception of the respondents using a five point Likert scale with following options: Highly Agree, Agree, Neutral, Disagree and Highly Disagree.
10. c) Research and Statistical Tools Employed
The research and statistical tools employed in this study are factor analysis and correlation. SPSS 16 was used to perform statistical analysis. The reliability of the data was carried out by using Cronbach's Alpha Value. The factor analysis was used to examine the underlying or latent dimensions within variables of overall customer perception (Hair et al, 1998). Both Bartlett's test of spherecity and measure of sampling adequacy (MSA) were also carried out to ensure that the requirements of factor analysis were met.
V.
11. Data Analysis and Interpretation
The analysis of this data was divided into following section:
12. a) Demographic profile of Respondents
The respondent profile as displayed in table 2 indicates the current scenario of life insurance sector and its user's profile. Most of the respondents (75.5%) were males and post-graduate (89.2%). Majority of respondents are in the age group of 25-35 years (42.4%) and between 35-50 years (43.2%). Most of the respondents have income above 5 laksh (5-10 lakhs at 25.2% and above 10 lakhs at 30.9%). The profile of respondents indicates they are young, urban, educated and have high income which is a right demographic composition from life insurance provider's context.
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Volume VII Issue IX Version I Year ( ) The study highlighted that majority of respondents hold a policy by Life Insurance Company (49.6%) followed by ICICI Pru (10.8%). This is in line with market share position of major insurers in India with LIC leading at 72.7% share followed by ICICI Pru at 4.7% market share. The lowest number of respondents had a policy from Kotak Mahindra (1.4%) followed by Aegon Religare (2.2%). 4 shows the result of reliability analysis-Cronbach's Alpha Value. This test measured the consistency between the survey scales. The Cronbach's Alpha score of 1.0 indicate 100 percent reliability. Cronbach's Alpha scores were all greater than the Nunnaly's (1978) generally accepted score of 0.7. In this study, the score was 0.871 for the service quality provided by the life insurance companies. Overall, the set of data meets the fundamental requirements of factor analysis satisfactorily (Hair et al, 1998). In analyzing the data given, the 20 response items were subjected to a factor analysis using the principal component method. Using the criteria of an Eigen value greater than one, four clear factors emerged accounting for 73.71% of the total variance. As in common practice, a Varimax rotation with Kaiser Normalization was performed to achieve a simpler and theoretically more meaningful factor solution. The Cronbach's alphas score for all the factors was 0.871 (Table 4).
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Volume VII Issue IX Version I Year ( ) It is clear from the factor loadings as highlighted in Table 6 that clear four factors have emerged representing 73.71% of total variance. These four factors represent different elements of services quality that form the underlying factors from the original 20 scale response items. Referring to the Table 6 above, first factor represents elements of the service quality directly related to responsiveness and assurance; it is therefore labeled "Responsiveness and Assurance Factors". These elements are timely service, agent's recommendation, timely claim, sympathy, courteous behavior of employees, individual attention to customers, wide range service and availability of adequate information. Second factor is directly related to convenience provided to customers, it is therefore labeled as "Convenience Factors". These elements are agent's communication skills, agent's trust, premium rates, convenient location and convenient working hours. Third factor is directly related to tangibility of services and therefore named as "Tangible Factor". These elements are modern office, attractive office, employee's dress and understanding of customer needs. Fourth factor represent empathy, therefore it is named as "Empathy Factor". These elements are best interest of customers, availability of employee assistance and trustworthiness of employees.
15. e) Correlation
To measure the impact of demographic factors on customer perception of service quality of life insurers, correlation technique was used. Table 7 shows the correlation between age and the 20 items of service quality. Since in case of majority of attributes of service quality the significance level is lower than .05, we reject the null hypothesis (Ho1) that there is no relationship between the age of respondents and perception of service quality of Life Insurance providers. In other words, the age has significant relationship which determines the service quality perception. Similar findings were there in the study of Bishnoi and Bishnoi (2013). Table 8 shows the correlation between gender and service quality. Since in case of majority of attributes of service quality the significant level is greater than .05, we accept the null hypothesis (Ho2) that there is no relationship between the gender of respondents and perception of service quality of Life Insurance providers. In other words, gender does not affect the service quality perception and both male and female customers share similar perception towards service quality of life insurers. 9 shows the correlation between educational qualification and service quality. Since in case of majority of attributes of service quality the significant level is greater than .05, we accept the null hypothesis (Ho3) that there is no relationship between the education level of respondents and perception of service quality of Life Insurance providers. In other words, education does not affect the service quality perception. If we look at the demographic profile we find that majority of the respondents are post-graduates (89.2%) and have the knowledge about the different life insurance products. Similar response may be there in other metro cities of India. Table 10 shows the correlation between income levels and service quality. Since in case of majority of attributes of service quality the significant level is greater than .05, we accept the null hypothesis (Ho3) that there is no relationship between the income level of respondents and perception of service quality of Life Insurance providers. In other words, income does not affect the service quality perception. If we look at the demographic profile we find that majority of the respondents (56.1%) have high income (above 5 lakh).
16. Discussion
The research examined the impact of different demographic characteristics on customer perception of service quality of life insurance providers.
The factor analysis has brought four clear factors related with the service quality of life insurers. These factors are Responsiveness and Assurance Factors, Convenience Factors, Tangible Factors and Empathy Factors. These factors represent 73.71% of total variance. The life insurers may take note of these factors which significantly determines the customer's perception of service quality. They may take care of these factors and ensure proper availability of tangible factors which will positively enhance the customer perception of service quality.
The test of correlation between demographic characteristics and service quality parameters have found out that the age of respondents significantly determine the customer perception of service quality of life insurance companies. Therefore, the life insurance providers may keep in mind the age factor while designing their product offerings and promotions. The other demographic characteristics such as gender, education and annual income does not have significant impact on customer perception towards service quality of life insurance providers.
The study has been carried out in the metropolitan area of Delhi NCR. The findings can be generalized for other metropolitan areas as the demographic profile of major metropolitan cities shows similar trends. The managers of life insurance service providers can use these findings to further improve their product offering and marketing strategies incorporating these findings. This will help them to enhance their brand image as well as customer loyalty and retention resulting in increased sales of their products. The managers of life insurance industry may utilize the findings of this study to minimize the service quality gap caused by the difference between the customer's actual expectation and the management's estimation of customer's expectations. Similar research can be carried out by the life insurance providers for rural and semi-urban areas so that the reach of these companies can be expanded into the majority of Indian population.
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| Global Journal of Management and Business Research | Item My Life Insurer has best interest of customers at heart My Life Insurer's employees are available for assistance My Life Insurer provides services in timely manner My Life Insurer's employees are trustworthy | Code SQ1 SQ2 SQ3 SQ4 |
| My Life Insurer's agents recommend policy as per customer needs | SQ5 | |
| My Life Insurer's agents have good communication skills | SQ6 | |
| My Life Insurer's agents are trustworthy | SQ7 |
| Demographic | Characteristics Freq. | % | |||
| Factors | |||||
| Age | 25-35 years | 59 | 42.4 | ||
| 35-50 years | 60 | 43.2 | |||
| 50 above | years | & | 20 | 14.4 | |
| Gender | Male | 105 | 75.5 | ||
| Female | 34 | 24.5 | |||
| Educational | Graduate | 15 | 10.8 | ||
| Qualification | Post Graduate | 124 | 89.2 | ||
| Annual Income | Upto 2 lakhs | 20 | 14.4 | ||
| 2-5 lakhs | 41 | 29.5 | |||
| 5-10 lakhs | 35 | 25.2 | |||
| Above 10 lakhs | 43 | 30.9 | |||
| Total | 139 | 100 | |||
| b) Respondent's Share of Life Insurers | |||||
| Insurer | Frequency | Percent |
| LIC | 69 | 49.6 |
| ICICI Pru | 15 | 10.8 |
| HDFC Life | 6 | 4.3 |
| Birla Sun life | 4 | 2.9 |
| SBI Life | 10 | 7.2 |
| Reliance Life | 4 | 2.9 |
| Tata AIA | 4 | 2.9 |
| Max Life | 4 | 2.9 |
| Bajaj Allianz | 8 | 5.8 |
| Kotak Mahindra | 2 | 1.4 |
| Aegon Religare | 3 | 2.2 |
| Others | 10 | 7.2 |
| Total | 139 | 100.0 |
| c) Reliability and Validity | ||
| Table |
| d) Factor Analysis | |
| Cronbach's Alpha | N of Items |
| .871 | 20 |
| Kaiser-Meyer-Olkin Measure of Sampling Adequacy. | .830 | |||
| Bartlett's Test of Sphericity | ||||
| Approx. Chi-Square | 2556.710 | |||
| df | 190 | |||
| Sig. | .000 | |||
| Table 6 : Rotated Component Matrix | ||||
| Rotated Component Matrix a | ||||
| Component | ||||
| 1 | 2 | 3 | 4 | |
| SQ1 | .212 | -.002 | .389 | .755 |
| SQ2 | .261 | .103 | .015 | .737 |
| SQ3 | .534 | .455 | .263 | .094 |
| SQ4 | .571 | .201 | -.006 | .648 |
| SQ5 | .643 | .165 | .569 | .025 |
| SQ6 | .346 | .652 | .379 | -.181 |
| SQ7 | .321 | .726 | .226 | .007 |
| SQ8 | .201 | .353 | .811 | -.103 |
| SQ9 | .055 | .112 | .836 | .286 |
| SQ10 | .125 | .391 | .723 | .341 |
| SQ11 | .299 | .776 | .180 | .205 |
| SQ12 | .136 | .881 | .113 | .176 |
| SQ13 | .219 | .806 | .283 | .123 |
| SQ14 | .691 | .280 | .075 | .426 |
| SQ15 | .720 | .263 | .266 | .212 |
| SQ16 | .711 | .083 | .279 | .327 |
| SQ17 | .492 | .296 | .445 | .316 |
| SQ18 | .509 | .304 | .619 | -.032 |
| SQ19 | .703 | .391 | .125 | .273 |
| SQ20 | .771 | .337 | .021 | .201 |
| SQ1 | SQ2 | SQ3 | SQ4 | SQ5 | |
| PC | -0.160 | -0.072 | -0.264 | 0.157 | -0.370 |
| Sig. | 0.060 | 0.399 | 0.002 | 0.065 | 0.000 |
| SQ6 | SQ7 | SQ8 | SQ9 | SQ10 | |
| PC | -0.329 | -0.223 | -0.421 | -0.271 | -0.195 |
| Sig. | 0.000 | 0.008 | 0.000 | 0.001 | 0.021 |
| SQ11 | SQ12 | SQ13 | SQ14 | SQ15 | |
| PC | -0.125 | -0.130 | -0.265 | 0.020 | -0.258 |
| Sig. | 0.143 | 0.128 | 0.002 | 0.819 | 0.002 |
| SQ16 | SQ17 | SQ18 | SQ19 | SQ20 | |
| PC | 0.069 | -0.282 | -0.333 | -0.127 | -0.018 |
| Sig. | 0.422 | 0.001 | 0.000 | 0.135 | 0.833 |
| PC = Pearson Correlation | |||||
| Sig. = Significance (2-tailed) | |||||
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| Global Journal of Management and Business Research | PC Sig. PC Sig. PC Sig. | SQ1 -0.124 0.145 SQ6 0.129 0.131 SQ11 -0.063 0.465 SQ16 | SQ2 0.046 0.593 SQ7 0.079 0.357 SQ12 -0.101 0.236 SQ17 | SQ3 0.112 0.189 SQ8 0.084 0.327 SQ13 -0.081 0.341 SQ18 | SQ4 -0.163 0.055 SQ9 -0.106 0.214 SQ14 -0.219 0.010 SQ19 | SQ5 0.077 0.367 SQ10 -0.025 0.774 SQ15 -0.194 0.022 SQ20 |
| PC | -0.005 | -0.164 | 0.194 | -0.059 | 0.096 | |
| Sig. | 0.949 | 0.053 | 0.022 | 0.487 | 0.260 | |
| PC = Pearson Correlation | ||||||
| Sig. = Significance (2-tailed) | ||||||
| SQ1 | SQ2 | SQ3 | SQ4 | SQ5 | |
| PC | 0.123 | 0.054 | 0.122 | 0.289 | -0.019 |
| Sig. | 0.148 | 0.527 | 0.153 | 0.001 | 0.825 |
| SQ6 | SQ7 | SQ8 | SQ9 | SQ10 | |
| PC | 0.036 | 0.172 | 0.119 | 0.228 | 0.306 |
| Sig. | 0.671 | 0.043 | 0.164 | 0.007 | 0.000 |
| SQ11 | SQ12 | SQ13 | SQ14 | SQ15 | |
| PC | 0.062 | 0.073 | -0.006 | 0.086 | 0.109 |
| Sig. | 0.466 | 0.391 | 0.942 | 0.311 | 0.202 |
| SQ16 | SQ17 | SQ18 | SQ19 | SQ20 | |
| PC | 0.209 | 0.042 | 0.300 | 0.153 | 0.069 |
| Sig. | 0.014 | 0.623 | 0.000 | 0.073 | 0.421 |
| PC = Pearson Correlation | |||||
| Sig. = Significance (2-tailed) | |||||
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| PC Sig. PC Sig. PC Sig. | SQ1 0.057 0.504 SQ6 0.097 0.255 SQ11 .176* 0.038 SQ16 | SQ2 -0.062 0.467 SQ7 0.116 0.172 SQ12 .321** 0.000 SQ17 | SQ3 -0.093 0.279 SQ8 -0.133 0.117 SQ13 0.045 0.601 SQ18 | SQ4 .259** 0.002 SQ9 -.182* 0.032 SQ14 .307** 0.000 SQ19 | SQ5 -0.068 0.425 SQ10 0.011 0.896 SQ15 .181* 0.033 SQ20 | Global Journal of Management and Business Research |
| PC | 0.108 | 0.099 | -0.098 | 0.128 | .230** | |
| Sig. | 0.205 | 0.248 | 0.252 | 0.132 | 0.006 | |
| PC = Pearson Correlation | ||||||
| Sig. = Significance (2-tailed) | ||||||