An exploration study on influential factors on customer relationship management

Article history: Received January 15, 2013 Accepted August 12, 2013 Available online August 14 2013 These days, we see an increasing competition among different business owners and they strive for customer retention. Customer relationship management plays an important role to increase business performance and attracting new customers in an attempt to have more market shares. One primary concern is to find more about important factors influencing customers being satisfied with different services they may receive. This paper performs an empirical investigation to find important factors influencing customer relationship management (CRM) in information and communication firm is city of Tehran, Iran. The survey uses factor analysis to find important factors using a questionnaire consist of 27 variables. Cronbach alpha is calculated as 0.861, which validates the questionnaire. The survey detects six factors influencing CRM learning performance attributes, enterprise resource planning, perception attributes, structure oriented, perception image and organizational resources. © 2014 Growing Science Ltd. All rights reserved.


Introduction
During the past few years, there have been tremendous efforts on investigating different factors on customer relationship management (CRM) since many business models have substantially invested in CRM (Mandják & Szántó, 2010;Mandják & Szántó, 2010;Nguyen & Mutum, 2012;Chikweche & Fletcher, 2013).Smith and Chang (2010), for instance, investigated whether an investment CRM generates the anticipated benefits.They reported that firms that pay more attention to a customercentric approach could benefit significantly from the implementation of CRM systems.There were no differences in the degree or focus of applying attributable to industry differences.Lambert (2009) stated that CRM can be stated as a strategic, process-oriented, cross-functional, value-creating for buyer and seller, and facilitate for accessing superior financial performance.However, we require for a more holistic view of cross-functional as it is associated with CRM.Lambert (2009) described a macro level cross-functional view of CRM and provided a structure for managing business-to-business relationships to co-create value and increase shareholder value.Lambert reported in a framework that managers could implement a cross-functional, cross-firm, CRM process in business-to-business relationships.Lin et al. (2010) investigated the impacts of different dimensions of CRM on innovation capabilities.They identified five dimensions of CRM including information sharing, customer involvement, long-term partnership, joint problem-solving, and technology-based CRM and five aspects of innovation capability including product, process, administrative, marketing, and service innovations.Sophonthummapharn (2009) developed a comprehensive research framework for understanding the adoption of techno-relationship innovations.The author also examined the factors influencing the adoption of electronic customer relationship management (eCRM) applications.They reported the top five influential factors including compatibility, industry pressure, customer pressure, subjective norm, and attitude.Jamali et al. (2013) aimed to develop a valid and reliable technique for assessing and measuring the management relationship level with customers in e-libraries.Besides, the research tried to detect how successful Yazd University Libraries met students' expectations, as a guideline to take steps towards improving its services.Kim et al. (2012) considered a means to create competitive advantage for a company, as well as influence organizational performance.Ata and Toker (2012) investigated the effect of customer relationship management adoption in business-to-business markets.Wang and Feng (2012) investigated CRM capabilities in terms of measurement, antecedents and consequences.Ranjan and Bhatnagar (2011) investigated the role of knowledge management and analytical CRM in business based on data mining based framework.

The proposed method
The proposed model of this paper uses factor analysis (Azad & Hassanabadi, 2013;Azad & Mohammadi, 2013) to determine important factors influencing data security in information and communication organization in city of Tehran, Iran.The study designs a questionnaire consists of 27 questions and in Likert scale.The sample size is calculated as follows, where N is the population size, and N=1100, the number of sample size is calculated as n=203.Cronbach alpha has been calculated as 0.891 in preliminary stage and final 0.856 in final stage, which are statistically acceptable.In addition, Kaiser-Meyer-Olkin Measure of Sampling Adequacy is calculated as 0.74, which is within an acceptable limit and validates the results.Since factor analysis is sensitive on skewness of factors, we have decided to delete seven questions.Fig. 1 demonstrates Scree plot on questions of the survey.Table 1 demonstrates 27 items of the designed questionnaire.After performing preliminary analysis, we have extracted communalities with major factors and Table 2 summarizes the results of our survey.In addition, we have extracted important factors using principal component analysis and Table 3 to Table 5 demonstrate the survey before and after rotation happen.As we can observe from the results of Table 5, we may extract six important factors influencing on data security.Next, we explain all these factors in details.

The results
The implementation of factor analysis helps us group different factors and find better insight on market development.

Measurement attributes
The first factor is associated with the measurement attributes and it includes four factors including improving quality of product and services, traditional methods to get customers' feedbacks, explicit observations for measuring quality and resolving existing conflicts.Table 6 summarizes the results of our findings.

Table 6
The summary of different components associated with measurement attributes As we can observe from the results of Table 6, improving quality of product and services is the most important factor followed by traditional methods to get customers' feedbacks, explicit observations for measuring quality and resolving existing conflicts.

The second factor: Integrated system
The second factor is associated with integrated system, which consists of four factors summarized in Table 7 as follows, According to the results of Table 7, managing firm in forms of network is the most important factor followed by recognizing opportunities and threats, leadership style and information technology.

The third factor: Perception components
Integrated system is the third factor and it includes three items summarized in Table 8 as follows, According to the results of Table 8, subjective norms as well as moral agency are the most influential factors followed by interpersonal influence.

The fourth factor: Structure oriented
Structure oriented is the fourth factor and it includes four items summarized in Table 9 as follows, According to the results of Table 9, cultural features are the most important issues followed by advertisement on products and services.

The fifth factor: Perception from product and services
The fifth factor is associated with perception from product and services, which consists of three factors summarized in Table 10 as follows, According to the results of Table 10, past experience from the firm is the most important factor followed by Value received from products and services and Perception of customer from products.

The sixth factor: Organizational resources
The last factor is associated with organizational resources, which consists of three sub-components summarized in Table 11 as follows, According to the results of Table 11, strategy for providing product and services is number one priority followed by personal characteristics of products.

Conclusion
This paper has presented an empirical investigation to detect important factors influencing customer relationship management in information and communication technology firm in city of Tehran, Iran.Using a questionnaire in Likert scale, the survey used factor analysis to detect influencing factors in CRM.The survey has detected six factors influencing CRM learning performance attributes, enterprise resource planning, perception attributes, structure oriented, perception image and organizational resources.
The first factor is associated with the measurement attributes and it includes four factors including improving quality of product and services, traditional methods to get customers' feedbacks, explicit observations for measuring quality and resolving existing conflicts.In our survey, improving quality of product and services is the most important factor followed by traditional methods to get customers' feedbacks, explicit observations for measuring quality and resolving existing conflicts.The second factor is associated with integrated system, which consists of four factors where managing firm in forms of network is the most important factor followed by recognizing opportunities and threats, leadership style and information technology.Integrated system is the third factor and it includes three items where subjective norms as well as moral agency are the most influential factors followed by interpersonal influence.Structure oriented is the fourth factor and it includes four items where cultural features are the most important issues followed by advertisement on products and services.The fifth factor is associated with perception from product and services, which consists of three factors where past experience from the firm is the most important factor followed by Value received from products and services and Perception of customer from products.The last factor is associated with organizational resources, which consists of three sub-components where strategy for providing product and services is number one priority followed by personal characteristics of products.
of normal distribution and finally  is the error term.Since we have 96

Table 2
The summary of communalities

Table 3
The results of total variance explained

Table 4
The results of principal component analysis before rotation

Table 7
The summary of different components associated with integrated system

Table 8
The summary of different components associated with perception components

Table 9
The summary of different components associated with structure oriented

Table 10
The summary of different components associated with organizational learning

Table 11
The summary of different components associated with feasibility study