Factors Affecting Consumers' Switching Intentions

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For example, considering cell phone industry when a ... waiting time and evaluation effort have a significant impact on consumer switching. Wen-Hua et al.,.
European Journal of Social Sciences – Volume 19, Number 1 (2011)

Factors Affecting Consumers’ Switching Intentions Ayesha Saeed Department of Management Sciences Army Public College of Management Sciences Rawalpindi E-mail: [email protected] Nazia Hussain Department of Management Sciences Army Public College of Management Sciences Rawalpindi Adnan Riaz Lecturer, Department of Business Administration Allama Iqbal Open University Islamabad E-mail: [email protected] Abstract This research study has gone through a detailed analysis of a questionnaire survey by involving all types of people particularly the youth. 171 responses were collected randomly to know the switching intentions of different mobile users with the help of four predictors i.e. outcome quality, perceived commitment, price and anger incident. Findings of the study showed that beside all other factors have influence on the customers’ switching intentions but the overriding factor remained the cost effectiveness. The critical role of this factor is because of the different socio / economic conditions of a developing country like Pakistan where buying power of a common man is low as compared to rich and developed countries. But generally it holds true as concluded by other research studies.

Keywords: Outcome Quality, Perceived Commitment, Price, Anger Incident, Consumers’ Switching Intention

1. Introduction The growing competition in the global market is showing that it is becoming increasingly important for companies to retain their existing customers. Gaining knowledge about customers' switching behavior is substantively important which can only be examined by analyzing the role of various factors affecting switching processes. This study is based on the responses from youngsters who are also termed as innovators in marketing literature being dynamic and risk taker. Youngsters always need change in their life and normally observed adaptive about new and advance offerings. They like creativity and innovation and move from one product to another. Therefore, preferring new and innovative product is the core attribute associated with youngsters (Stanton et al., (1994). For example, considering cell phone industry when a company offers better packages, it entices youngsters to switch to new cellular service provider irrespective of what advantages existing company is offering. Switching process results in relationship dissolution. Duck (1982) described relationship dissolution as the permanent dismember ship. It is the process of the breaking up of relationships by the voluntary activity of at least one side (Duck and Rollie, 2010). Research in relationship marketing has for some reasons argued that creating and developing relationships contributes to the success of firms. Some authors are of the view that 54

European Journal of Social Sciences – Volume 19, Number 1 (2011) firms can even use relationship marketing as a competitive advantage. Analyzing the dissolution process is very important in industrial markets for the long-term survival. Moreover, firms must also frequently consider terminating inefficient relationships because of their implicit costs (Anto´n et al., 2007). When firms lose a customer they are not only losing future earnings but also incurring the cost of finding new customers. Over time loyal customers become less price-sensitive therefore, Losing loyal customer means giving up high margins. Considering the technological advancements and its easy access to every individual, customers are becoming intolerant and they can dissolve the relationship as soon as any problem arises. This is the reason; customer retention is the core concern of each organization. These notions laid to the foundation of this study as to determine the most important factors impinging upon customer-vender relationship dissolution. In this research study, the author tried to specify the best results about switching behavior of innovators who constitute the 3% of the whole population (Stanton et al., 1994).

2. Literature Review Contemporary marketing literature reveals keen focus of experts and researchers on customer retention. Because retaining customers is a core factor for the economic success of a firm in long-run (Wan-Ling and Hwang, 2006). To retain customers, organizations have to satisfy them particularly in service industry (Oyeniyi and Abiodun, 2010). If customers are satisfied with the service, this not only enhances repurchase intentions (Hellier et al., 2003) but also addresses the switching intentions (Fernandes and Santos, 2007). Switching and switching intentions are considered as the most important variable in service sector. Organizations are now deeply concerned with the factors which impetus switching intentions. Understanding switching intentions may help in retaining customers and to avoid the adverse effects that may result due to switching (Wan-Ling and Hwang, 2006). Service switching is defined as the act of replacing and exchanging the current service provider with another that is available to the consumer in the market (Bansal, 1997). Normally customer satisfaction is found to be the most common factor impinging upon switching intentions (Fernandes and Santos, 2007). As concluded by Wen-Hua et al., (2010) if customers are satisfied with call quality, mobile device and customer complement then it negatively affect switching intentions. But contemporary researches show different attempts of the researchers in explaining the causes of switching and switching intentions. For example, the study of West (2000) showed that network externalities, switching costs and psychological involvement are the key factors to predict switching while according to Gupta et al., (2004) price-search intentions, channel-risk perceptions, waiting time and evaluation effort have a significant impact on consumer switching. Wen-Hua et al., (2010) claimed switching costs and attractiveness of alternatives as dominant factor affecting switching intention. Jones et al., (2003) made a different attempt by evaluating the impact of instructional manual understanding on switching intentions. Customers who understand instructions given at handbook usually face less difficulty in using the service, have higher levels of satisfaction and recommend it to others therefore have less switching intentions. A different approach was applied by Bansal (1997) by incorporating some measures of theory of planned behavior (TPB) in predicting switching intentions. TPB posits a link between attitudes and behavior. In combination, perceived behavioral control, subjective norm and attitude toward the behavior lead towards “behavioral intention” (Ajzen, 1991). Bansal (1997) measured the impact of one's attitude towards switching behavior, perceived behavior control (termed as perceived switching costs) and satisfaction with the service provider, with intentions to switch. Results confirmed the significant influence of all three variables on switching intentions. Keeping in view the different culture, social norms and perceptions of people in Pakistan. This research focused on price, perceived commitment, outcome quality and anger incident as predicting variable to explain switching intentions. Previously, Anto´n et al., (2007) analyzed the predicting 55

European Journal of Social Sciences – Volume 19, Number 1 (2011) qualities of these variables in Spanish environment and found that poor quality, a perception of low organization commitment or interest, perceived unfair price and an anger incident can help in explaining the consumers’ intention to switch.

3. Hypothesis H1: Poor quality is positively associated with switching intentions. H2: Perceived commitment is negatively associated with switching intentions. H3: Outcome quality is negatively associated with switching intentions. H4: Anger incident is positively associated with switching intentions.

4. Methodology 4.1. Research Model

4.2. Participants/Subjects Students of the main universities within twin cities (Islamabad and Rawalpindi) of Pakistan were treated as the population of this study. However, an attempt was made to collect responses from mobile users with GSM connections only. Since it was a random survey therefore; students of four main institutes like APCOMS, Foundation University, Fatima Jinnah and SZABIST were approached for data collection. 4.3. Procedure It was decided to collect at least 50 questionnaires from each of the institute for equal representation therefore, a total of 100 questionnaires were floated in each institute using non-probability convenience (accidental / haphazard) sampling method. Initially self-administered approach was used to float the questionnaires. Surveys were completed anonymously and returned back. 4.4. Measures The instrument used in this research was adopted from the study of Antón et al., (2007). Questionnaire was slightly modified in view of respondents’ approach. It had two sections, one for demographical information and the other to measure customer switching intention. The responses for questions made use of different methods like tick-boxes, circling answers and inserting their own comments and suggestions. Demographic section was based on tick-boxes and consisting of seven questions on age, gender, highest level of education, occupation, income level, tenure of employment and type of 56

European Journal of Social Sciences – Volume 19, Number 1 (2011) network. To measure switching intention, it had two sub-sections to determine score of independent variables. The respondents were asked to encircle the appropriate number against each statement, that best suit their feelings. to measure customer switching intention, total of 3 items were given, for satisfaction of customers only 1 item was given and outcome quality it was two, for interaction quality 3 items were given physical environment quality 2 items were selected, perceived commitment five items were selected, price 2 items and anger incident 1 item was given and all the items were measured based on five point likert scale ranging from 1. strongly disagree, through to 5. strongly agree, developed by Renis Likert to enable respondents to answer questions according to the intensity of their attitude. the questionnaire also contained brief background information about the purpose of the study and measures for confidentiality. Initially, 11 questionnaires were distributed as pilot testing, to check the authenticity of the instrument and then it was distributed to target group of respondents.

5. Data Analysis Data was analyzed to determine how the units covered in the research project respond to the items under investigation. Descriptive statistics, Pearson product moment correlation and multiple regression methods were used in view of hypothesis and research objectives. Figures obtained from SPSS-15.0 statistical part were interpreted to come at conclusion and implications. Table 1:

Demographic Characteristics of Respondents:

Measures

Age

Gender

Highest Level of Education

Occupation

Income Level

Tenure of Employment

Network

Items 20 or below 21-25 26-30 31-35 36-40 41 or Above Male Female Bachelors Masters MS/M.Phil P.H.D Student Self-Employed Employed Others… Below 10,000 11,000-20,000 21,000-30,000 31,000-40,000 41,000-50,000 Above 50,000 Less than 1 year 1-5 yrs. 6-10 yrs. 10 or above Mobilink Ufone Telenor Warid Zong Other

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Percentage 26% 45% 16% 6% 5% 2% 54% 46% 53% 33% 9% 5% 72% 10% 16% 2% 23% 26% 21% 12% 10% 8% 19% 47% 33% 1% 12% 28% 28% 14% 18% Nil

European Journal of Social Sciences – Volume 19, Number 1 (2011) First section of the questionnaire was helped to explain the demography of respondents with the help of six (6) items. Majority of youngsters were male which is about 54% while 46% were females. Pakistan is male dominated society therefore this was an encouraging aspect. Most of the respondents had completed education up to bachelor’s level (53%). We also come to know that 28% youngsters were using Ufone and Telenor respectively whereas subscribers of the other networks were lower than this figure. The outcome of survey further showed that 72% of youngsters were students and only 16% were employed. Figure 1: Aggregate Mean Values

Anger incident

2.71

Price

3.26

Perceived commitment

3.25

Outcome quality

3.19

Switching intention

2.8 1

2

3

4

5

Descriptive statistics were used to describe the main features of collected data quantitatively. Since all the items were measured using five point likert scale starting from “strongly disagree” to “strongly agree” therefore mean values greater than 3.00 for three independent variables (outcome quality perceived commitment price ) are showing positive trend. Switching intention has 2.80 mean value while analysis shows 2.71 mean value for anger incident. Standard deviation varies from 0.89 to 1.31 for different variables showing that most observations cluster around the mean for all variables. Correlation analysis determines the association as well as the extent of association between variables. Results of correlation analysis show that fair tariff/price is found significantly correlated with switching intentions. Correlation value between switching intentions is found as -0.51. Second highest correlation value is calculated as -0.43 between perceived commitment and switching intentions explaining slightly low relationship as compared to fair tariff and switching intentions but still significantly high. Correlation value between outcome quality and switching intentions remained as -0.42 which is also found significantly related. Table 2:

Correlation Matrix Mean

Switching intention Outcome quality Perceived commitment Price Anger incident

2.80 3.19 3.25 3.26 2.71

Std. Deviation 0.89 1.09 1.11 1.26 1.31

Switching intention 1 -0.42 -0.43 -0.51 -0.3

Outcome quality

Perceived commitment

1 0.86 0.79 0.31

1 0.79 0.32

Price

1 0.43

Anger incident

1

**Correlation is significant at the 0.01 level (2-tailed).

Regression analysis was applied to know the interdependence of two variables. Results show that total 27 % of the variation in switching intentions is explained by the three predicting variables of this study. The values of coefficient of determinations are found as -0.2949, -0.0619 and -0.0065 for fair tariff, perceived commitment and outcome quality respectively. While the t values for all the independent variables remained more than 2.4 showing significant influence and predicting qualities of the coefficients. 58

European Journal of Social Sciences – Volume 19, Number 1 (2011) Table 3:

Regression Table

Dependent Variable Switching Intentions

Independent Variables Outcome quality Perceived commitment Price Anger incident

Adjusted R Square 0.256

β 0.007 -0.077 -0.416 -0.099

t Stat 0.05 -0.547 -3.476 -1.346

P-value 0.9544 0.5836 0.0006 0.1789

In nutshell, regression analysis shows that 27% of the switching intentions among customers depend upon fair tariff, perceived commitment, and anger incident and outcome quality while rest of 77% dependence is unexplained or explained by other variables which are not taken in this study.

6. Discussions The factors which affect the switching intentions include tariff rates, perceived commitment, outcome quality and anger incident. The main focus of this research study was price/tariff rates which showed relatively strong influence in determining switching intentions. This explanation is consistent with analogous findings that if the tariff rates are fair then less number of customer switch-over to the other networks. Descriptive analysis show that respondents do not have any intentions to change their network if they do then very few of them are intending to switch to another network. Furthermore, they showed positive attitude towards quality of services offered by the companies. Their respective network providers strive to maintain a frequent and constant relationship with them and they rarely had any experience that angered them. More importantly, they are of the view that the price they pay for the service is fair and it is a good value for money. Correlation analysis highlighted the fact that increasing tariff rates may increase the customer’s switching intention. If customers feel that prices they pay for the telecom services are not good value for money then ultimately it establishes the feeling to renew their mobile network. Secondly, the correlation results between perceived commitment and switching intention validate the notion that if company is not committed to them as a customer then customers start searching for other mobile service providers. Outcome quality also affects the customer switching intentions to some extent. It is basically the actual quality of the service provided by the networks. If the outcome quality is below the mark then the switching intention will be high. This shows that outcome quality has inverse relationship with the switching intentions. Results further show that anger incident has smaller impact on switching behavior. If the customer has a bad experience with the representative of network then it may result in future switching intentions of customers. Regression analysis expressed that fair prices, prompt customer services, commitment with customers and anger free services control the switching intentions and customers stay with their existing service providers for long time. Results elucidate that customers are more concerned about the money they pay for the service. They want the good quality of service in return. Minor fluctuations in the price of packages may lead to switching towards other cost effective network. So the companies should try to make the tariff rates fair.

7. Conclusion and Implications of the Study It is an established fact that from the last decade cell phone has taken a deep root in every walk of our life particularly in the lower middle and poor class of society. Even expensive mobile sets have become a status symbol for the people. A common observation in telecom industry is that subscribers keep on changing their mobile sets due to various reasons. Usually people change their mobile sets to 59

European Journal of Social Sciences – Volume 19, Number 1 (2011) experience change. Continuous technological advancements add new features in mobile sets which also tend youngsters to go for new mobile sets. However, this research study was mainly focused on investigating the factors that cause individuals to change their cellular connections particularly among youngsters which constitute the major portion of the population. In Pakistan, various companies have invested in telecom sector in anticipation of long-term growth. They are duly concerned about retaining old customers in addition to look for new. This study validate and confirm the significant role of fair prices, prompt customer services, commitment with customers and anger free services in order to control switching intentions. Although we cant ignore the role of outcome quality and anger incident but the core factors identified by the customers remained as fair price and perceptual commitment of mobile service provider with customer. In view of the findings of this study, the organizations can take following factors into account; 1. It is the prime duty of support staff to address the problems of their subscribers effectively and efficiently. Especially, their attitude play important role in retaining customers. 2. Tariff rates should be fair enough to retain existing customers and attract new. 3. Mobile service providers should be fully committed to their customers by maintaining a frequent and constant. 4. Critical incidences may provide a set back to the relationship between customer and a company. An utmost effort should be made to avoid such incidences in short-term as well as in long-term.

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