Relationship between Social bonds and Customer value in commercial Banks in Kenya

International Journal of Business and Management Invention
ISSN (Online): 2319 – 8028, ISSN (Print): 2319 – 801X
www.ijbmi.org || Volume 6 Issue 1 || January. 2017 || PP—28-35
www.ijbmi.org 28 | Page
Relationship between Social bonds and Customer value in
commercial Banks in Kenya
Dr. Tom O. Mboya
School of Business, Technical University of Mombasa, Kenya
ABSTRACT: This study sought to empirically examine the influence of social bonds on customer value in
commercial banks in Kenya. The social bonds examined in this study are communication with customers (CC),
creation of friendship (CF) and social support (SS). The study sample consists of 384 respondents with a
response rate of 78.1 per cent. Data was analyzed by employing correlation and multiple regression analysis.
The findings revealed that social bonds are positively associated with customer value in commercial banks in
Kenya. The generalization of the findings is limited as the study focused only on a single industry in Kenya.
Based on the findings, companies employing social bonds strategies should focus on improving the usefulness of
the social bonds to customers by creating opportunities to strengthen social relationships. This study
successfully extends the relationship marketing strategies in the context of customer value by incorporating
communication with customers, creation of friendship and social support constructs. This extended relationship
marketing model is developed to achieve the greater understanding of customer acceptance of social bonding
strategy in Kenya’s commercial banks. In conclusion, the model in this study presents a considerable
improvement in explanatory power.
Keywords: Banking, Customer value, Relational marketing, Social bonds
I. INTRODUCTION
The concept of customer value emerged as the defining business issue of the 1990s, and has continued
to receive extensive research interest today. The Marketing Science Institute (2006–2008) has included the
definition of „customer value‟ in its list of research priorities for 2010–2015. These developments reflect the
great interest that has been generated by the phenomenon of value creation among marketing researchers in both
academia and industry. Despite this wide interest, the concept of value has often not been clearly defined in
studies of the subject; indeed, according to Khalifa (2004), the concept has become one of the most overused
and misused concepts in the social sciences in general and in the management literature in particular. Given the
complexity and lack of consensus in this area, the purpose of this study is to provide new insight to the area of
customer value, in terms of how social bonds influence it. The marketplace today is very dynamic, vibrant and
competitive. The customers are smarter, more informed, and have an access to many channels and choices
which they take little time to exercise (Bhardwaj, 2007). Matching the growing complexity of the business
environment has led to an ever-more diversified and demanding customer base. (Barnes et al., 2004). Increased
customer value is frequently argued to be the single most important driver of organizations‟ long-term
performance. Eisingerich & Bell (2007) find empirical support that customer value emerges as the dominant,
significant, direct determinant of repurchase intention. The intensity with which a bank invests in social bonds
directly affects its customer value and market share. Commercial banks in Kenya compete amongst themselves
with amazing technology and investment in marketing to outdo each other (Kuria, 2007).
II. LITERATURE REVIEW
2.1 The concept of customer value
Organizations are increasingly recognizing that customer value is a key factor in strategic marketing
(Coltman, 2007, Mylonakis, 2009). Buttle (2009) has observed that the creation of customer value must be the
reason for the firm‟s existence and certainly for its success. As these words indicate, the creation of customer
value has become a strategic orientation in building and sustaining a competitive advantage (Wang et al., 2004).
A value proposition is an explicit promise made by a company to its customers that it will deliver a particular
bundle of value creating benefits (Buttle, 2009). According to Anderson, Narus and Rossum (2006) a value
proposition has measures that differ from the traditional measure of value (internally oriented view), seeing the
business instead from a customer perspective (externally oriented view). When investigating the value
proposition, there should be some visible signs on customers‟ perceived value terms (Bititci, Martinez, Albores
and Parung, 2004). Customer perceived value is the trade-off between perceived benefits and perceived sacrifice
(Kar, 2006). A successfully tested and proven value proposition is essential to a successfully differentiated
business (Fifield, 2007). Kar (2006), states that „the ability of a company to provide a superior value for its
customers is regarded as one of the most successful competitive strategies‟.

Relationship between Social bonds and Customer value in commercial Banks in Kenya
2.2 Customer value from the customer perspective
Customer value can be seen as a more personal and holistic view of quality. It is therefore an
assessment of both the positive and negative consequences of using a product or a service (Fifield, 2007). While
the reasons for firms wishing to form relationships with their customers are very convincing, it is sometimes less
clear why customers may want to reciprocate in relationship development (Khalifa and Shen., 2008). Benefits
customers may receive from a relationship: confidence benefits, social benefits and special treatment benefits,
which include customers receiving social support and customized communication.
2.3 The Role of social bonds on creating customer value
Ndubuisi (2004) argued that, more and more firms are capitalizing on strong firm-customer
relationship through bonding to gain invaluable information on how best to serve customers. Due to its
importance, researchers have been attracted to the issue of bonding in relational marketing (Vierra and Ennew,
2004). Defining bonding Callanghan et al, (1995) said it is the dimension of a relationship that result in two
parties (customer and supplier or buyer and seller) acting in a unified manner towards a desired goal. A bond
has a number of dimensions which Hollebeek, (2011) based on some cardinal relations that includes social
interaction, closeness and friendship.
Conceptual framework
III. HYPOTHESES DEVELOPMENT
3.1 Relationship between communication with customers and customer value
Communication with customers refer to the provision of direct information to keep customers aware of
new products and services, policy changes in exchange for customer loyalty, includes; bank communicate
extensively to customers, provision of timely and trustworthy information, information on new services,
accuracy of information, ease of understanding information, tailor made communication and bank fulfils
promises (Bhardwaj, 2007). Researchers have argued that one motivation for customers to engage in relational
exchanges is when they are provided with relevant and accurate information to help them make decision.
Communication enhances customer relationships by creating a feeling of importance (Hsieh, Chiu, & Chiang,
2005). Therefore it is proposed that commercial banks with good communication tactics display a greater
potential to create customer value relative to competitors; thus the following null hypothesis was postulated:
Н01: There is no significant relationship between communication with customers and customer value of
commercial banks
3.2 Relationship between creation of friendship and customer value
Friendship is ties that bind customers to service provider. The customers willingly remain in the
relationship if they deem the friendship to be beneficial (Hsieh et al., 2005). Hence, creation of friendship tactics
such as; employees are courteous, employees show respect to customers, mutually beneficial relationship, bank
is my first choice among others, recommend bank to friends, relationship goes beyond business and satisfaction
with relationship (Chiu et al., 2005), show the willingness to create customer value. The study proposed that
organizations with good and effective loan repayment terms have the potential to perform well and create
customer value. Thus the relevant null hypothesis was stated as:
Н02: There is no significant relationship between creation of friendship and customer value
3.3 Relationship between social support and customer value
Hsieh et al. (2005) define social support as the value adding strategy that is designed to help loyal
customers when in distress. Social support such as; strong bond with my bank, confidence in my bank, concern
for bank success, bank offer social support when needed, social meetings for interaction, bank employees are
socially responsible and I encourage people to do business with my bank can influence how customers perceive
their bank, hence derive customer value. The study proposes that banks with good social support programs can

control the adverse effects of customer switching to other banks and create customer value. Thus the relevant
null hypothesis is stated as:
H03: There is no significant relationship between social support and customer value
IV. METHODOLOGY
The study employed explanatory survey research design as it sought to describe and establish the
associations among the key study variables, namely; communication with customers, creation of friendship,
social support and customer value. The study was cross-sectional (that is, snapshot or one-shot) as the research
respondents were interviewed just once. Cross-sectional studies have been found to be robust for effects of
relationships studies (Coltman, 2007).
4.1 Sampling and data collection
In this study, 31 survey items for four constructs in the questionnaire actually come from the prior
empirical studies, and are modified to fit the context of social bonds and customer value (Luarn and Lin, 2005).
The target population of this study was individual commercial bank customers. The survey instruments for this
study were distributed at commercial banks outlets located in Nairobi city in Kenya. The survey instrument was
given to every fifth customer who enters the bank. A total of 384 copies of the survey instrument were
distributed in the commercial banks. Out of these 384 samples, 84 samples were rejected due to partial response
and/or missing data, thus giving a total response rate of 78.1 per cent.
4.2Data analysis
Multiple linear regressions were employed to test the hypotheses. This analysis is used to test the effects that
arise due to a causal relationship. The analysis equation can be written as follows:
Y = a + b1 (X1) + b2 (X2) + b3 (X3) + e
Where:
Y = Customer Value
a = y-intercept (constant)
b1‟ b2 and b3 = coefficient regression of each independent variables
X1 = Communication with customers
X2 = Creation of friendship
X3 = Social support
e=Error Term
V. FINDINGS
5.1 Profile of respondents
The demographic profile of the surveyed respondents is presented in Table 1, which includes gender
and period the respondent has been customer of the bank. The total sample for the survey comprised of 300
respondents. The gender distribution of the survey respondents is 64.7 per cent males and 35.3 per cent females.
The results also indicated that most of the respondents have been customer of bank for between 6-10 years,
which is 53.3 per cent.
Table 1: Demographic profile for respondents
Source: Research Data (2016)
5.2 Scale reliability and factor analysis
The reliability of the questionnaire was tested using Cronbach alpha measurements. The reliability
coefficients of each variable are as follows: CC (0.787); CF (0.952); SS (0.935) and Customer value (CV)
(0.954). The reliability coefficients of all the variables are above 0.70, which concurs with the suggestion made
by Nunnally (1978). Table 2 show the reliability coefficients of the items

Construct validity measures the degree to which a scale measures what it intends to measure (Garver
and Mentzer, 1999) and it is assessed by factor analysis in this study. In order to assess the construct validity, 21
items for independent variables are examined by principal components extraction with varimax rotation. The
Kaiser-Meyer-Olkin (KMO) has a measure of 0.748, which is above the threshold of 0.5 (Field, 2005). The
Bartlett‟s test is significant in this study with Chi square 6078.408 and (p-value < 0.05). Therefore, the KMO
value of 0.748 and significance of Bartlett‟s statistic confirm the appropriateness of the factor analysis for the
data set. Tables 3 show the factor loading for each item. The eigen value for each factor is greater than 1.0
(5.243, 5.608, 5.905), which implies that each factor can explain more variance than a single variable. The
cumulative percentage of variance explained by the three factors is 82.586 per cent. Based on above results, the
construct validity is established. Results also show that 10 items for the dependent variable i.e. customer value
are sorted and clustered into one component. The results of principal component analysis indicate that, there is
one factor whose Eigen values exceed 1.0. For customer value, the factor has Eigen value of 1.429. The factor
identified for the dependent variable; customer value explains 86.021% of the total variance. The Kaiser-Meyer-
Olkin (KMO) has a measure of 0.770, which is above the threshold of 0.5 (Field, 2005). The Bartlett‟s test is
significant for customer value with Chi-Square= 5845.198 (p-value< 0.05). Therefore, the KMO value of 0.770
and significance of Bartlett‟s statistic confirm the appropriateness of the factor analysis for customer value. Any
item that failed to meet the criteria of having a factor loading value of greater than 0.5 and loads on one and only
one factor was dropped from the study (Liao et al., 2007).
Table 2: Results of reliability tests of the variable measures
Variable Measure Cronbach alpha coefficient
Communication with customers Bank communicate extensively to customers
Provision of timely and trustworthy information
Information on new services
Accuracy of information
Ease of understanding information
Tailor made communication
Bank fulfils promises 0.940
Creation of friendship Employees are courteous
Employees show respect to customers
Mutually beneficial relationship
Bank is my first choice among others
Recommend bank to friends
Relationship goes beyond business
Satisfaction with relationship 0.953
Social support Strong bond with my bank
Confidence in my bank
Concern for bank success
Bank offer social support when needed
Social meetings for interaction
Bank employees are socially responsible
I encourage people to do business with my bank 0.968
Customer value Proximity to branch and ATM network
Speed and efficiency of response to complaints
Speed and efficiency in dealing with customer
Friendly and courteous staff and short queues
Provision of a range of quality products
Employees are truthful and honest
Bank fulfill its obligations
Provision of personalized telephone banking
Concerns to managing my savings
Personalized services 0.954

Table 3: Factor analysis results of the study variables
Variable Scale item Factor loading Eigen values Percentage
of variance
Communication Bank communicate extensively .909 5.243 74.905
Information is timely and trustworthy .891
Information on new services 902
Accuracy of information .929
Information is easy to understand .805
Tailor made communication .708
Bank fulfils promises .892
Friendship Bank employees are courteous .965 5.608 80.118
Bank employees show respect .875
Mutually beneficial relationship .942
Bank is my first choice .916
Recommend bank to friends .798
Relationship goes beyond business .897
Satisfaction with relationship with bank .862
Social support Strong bond with my bank .901 5.905 84.362
Confidence in my bank .935
Concern for bank success .930
Bank offer social support when needed .954
Social meetings for interaction .924
Bank employees are socially responsible .980
I encourage people to do business with bank .794
Customer Value Close proximity to branch and ATM networks .568 1.429 86.021
Speed and efficiency of response to customer
complaints
.879
Speed and efficiency in dealing with customer requests .925
Friendly, courteous branch staff and short queues .886
Provision of a range of quality products .696
Bank employees are truthful and honest .931
Bank fulfills its obligations .853
Provision of personalized telephone banking .874
Bank show concern to savings management .897
Bank is willing to offer personalized services .819
5.3 Correlation Analysis
Since a single construct in the questionnaire was measured by multiple items, the average score of the
multi-items for a construct was computed and used in further analysis such as correlation analysis and multiple
regression analysis (Wang and Benbasat, 2007). Pearson correlation analysis was conducted to examine the
relationship between the variables (Wong and Hiew, 2005; Jahangir and Begum, 2008). As cited in Wong and
Hiew (2005) the correlation coefficient value (r) range from 0.10 to 0.29 is considered weak, from 0.30 to 0.49
is considered medium and from 0.50 to 1.0 is considered strong. However, according to Field (2005), correlation
coefficient should not go beyond 0.8 to avoid multicollinearity. Since the highest correlation coefficient is 0.779
which is less than 0.8, there is no multicollinearity problem in this study. All the associated pairs of variables
were significant at level 0.05. All the hypothesized relationships developed were found to be statistically
significant at level p < 0.05. Based on Table 4, the correlation between CC and CF was the strongest (r-value =
0.779, p < 0.05. This is followed by the relationship between CC and SS (r-value = 0.775, p < 0.05). The
weakest relationship was between CC and CV (r-value = 0.635, p < 0.05).

Table 4: Pearson Correlation Coefficients of Study variables
5.4 Multiple regression analysis
Multiple regression analysis is applied to analyze the relationship between a single dependent variable
and several independent variables (Hair et al., 2005). Multiple regression analysis was therefore selected as it is
viewed as an appropriate method for this study. The summary of results analysis is shown in Table 5. In order to
test for multicollinearity among the predictor variables, variance-inflation factor (VIF) and tolerance were
applied. The multicollinearity statistics showed that the tolerance indicator for Communication with customers
(CC), Creation of friendship (CF) and Social support (SS) are all greater than 0.1 and their VIF values are less
than 10. The result indicates that no multicollinearity problem has occurred (Ott and Longnecker, 2001). The F-
statistics produced (F = 169.070) was significant at 5 per cent level (Sig. F < 0.05), thus confirming the fitness
for the model. Therefore, there is a statistically significant relationship between the social bonds and customer
value. The coefficient of determination R2
was 72.9 per cent. Thus, social bonds can significantly account for
72.9 per cent in customer value. The results also shows that CF (p < 0.05), and SS (p < 0.05) significantly affect
customer value, while CC (p> 0.05) was found to be insignificant. Based on Table 5, it indicated that the most
important social bonds that affect customer value is SS (β= 0.403, p-value< 0.05), followed by CF (β= 0.225, p-
value< 0.05). Communication however, were found not to be significantly associated with customer value CC
(β= 0.135, p-value> 0.05). Hence, Ho2 and Ho3 are rejected since none of the βi‟s ≠ 0 and their p-values is less
than α. On the other hand the study fail to reject Ho1 since the p-value of communication is greater than 0.05.
Table 5: Multiple regression results on relationship between social bonds and customer value
VI. DISCUSSIONS
The relationship between creation of friendship with customers and customer value was found to be
positive and significant (β= 0.225, p< 0.05). The hypothesis was therefore rejected. The coefficient 0.225
implies that creation of friendship significantly explain the variability in customer value. This result supports
prior researches that looked at relational marketing strategies in the context of customer value (Lin and Wang,
2005; Khalifa and Shen, 2008; Ahmad and Buttle, 2001).
Likewise the relationship between social support and customer value was found to be positive and
significant (β= 0.403, p< 0.05). The hypothesis was therefore rejected. The coefficient 0.403 implies that
extension of loan repayment terms explain the variability in customer value. This result supports prior studies
(Hsieh et al., 2005; Chiu et al., 2005).
The study however found positive and insignificant relationship between communication and customer
value (β= 0.135, p> 0.05). The study therefore failed to reject the hypothesis. Communication was found to have
an insignificant effect on customer value. This result contradicted the prior studies (Ndubisi, 2006). It is
surprising that communication was found to be not significant in influencing customer value in the study, even
though communication has been validated as major behavioral belief to influence customer value. It can be
inferred that since majority of the bank customers in the study are retail customers, it is possible that
communication with the bank is less important to them and thus has no impact on value to them.
VII. IMPLICATIONS
Besides, the implications for practices, this study provides several implications for scholars too. This
study successfully extends the social bonding strategies in the context of customer value by incorporating
communication, creation of friendship and social support constructs. This extended marketing model is

developed to achieve the greater understanding of customer acceptance of social bonding strategies in Kenya‟s
commercial banks. In conclusion, the model in this study presents a considerable improvement in explanatory
power. According to Kuria (2007), it costs tens of billions to build and develop customer value and relationships
in Kenya‟s commercial banks. Since lots of efforts and money have been invested in social bonding strategies, it
is essential to ensure that customers positively perceive such strategies. By using the derived results in the
analysis, commercial banks may focus on the improvement of constructs or attributes that have positive impact
on customer value (Wong and Hiew, 2005). Since social support is the most critical factor among all
independent variables, commercial banks should initiate critical social support systems which customers will
find valuable.
7.1 Limitations of the Study and Suggestions for further research
Despite the fact that this study produced important results, it also faced certain limitations which in turn
offer opportunities for further research. This research was conducted in Kenya and whether the results from this
research would be consistent with other countries‟ commercial bank customers would need to be verified
through further research. Future studies can focus on conducting a multi-country comparison to test the
influence of social bonds on customer value. Future research should also follow the longitudinal approach to
predict customer value over time since the model in this study is cross-sectional, which measures customer
value only at a single point in time (snapshot approach) (Luarn and Lin, 2005).
Finally, the fact that the study was based on a single industry setting that is the banking sector in
Kenya, limits the generalization of the results. Although the industry specificity of a study enhances its internal
validity, care should be taken when generalizing these findings to other industries. It should be kept in mind that
the results in a services context may not necessarily translate into a product oriented context. This industry-level
difference needs to be considered when evaluating the results and further research is required. Samples from
different industries could explain more clearly the relationship between social bonds and customer value.
REFERENCES
[1]. Ahmad, R. and Buttle, F. (2001). Retaining Business Customers through adaptation and bonding: a case study of HDOX. Journal
of business and industrial marketing, Vol. 16
[2]. Anderson, J., Narus, J., & Rossum, W. (2006). Customer Value Propositions in Business Markets. Harvard Business Review.
(pp 90-99)
[3]. Barnes, Bredley R., Fox, Martin T., & Morris, D.S. (2004). Exploring the Linkage between Internal Marketing, Relationship
Marketing and Service Quality: a Case Study of a Consulting Organization. Journal of Total Quality Management, Vol. 15(pp 593-
601).
[4]. Bhardwaj, Deepali. (2007). Relationship Marketing in Context to the IT Industry. The Journal of Business Perspective, Vol. 11
[5]. Bititci, U., Martinez, V., Albores, P., & Parung, J. (2004). Creating and Managing Value in Collaborative Networks.
InternationalJournal of Physical Distribution and Logistics Management.http://dx.doi.org/10.1108/09600030410533574
[6]. Buttle, F. (2009). “Relationship Marketing”, in Buttle, F. (Eds), Relationship Marketing Theory and Practice. Paul Chapman,
London
[7]. Callanghan, M., Mcphail, J., Yau O.H.M. (1995). Dimensions of Relationship Marketing Orientation. Proceedings of the Seventh
Biannual world Marketing Congress, Melbourne.
[8]. Chiu, H.-C., Hsieh, Y.-C., Li, Y.-C. and Lee, M. (2005). Relationship Marketing and Consumer Switching Behavior. Journal of
Business Research Vol. 58 (pp 1681-9)
[9]. Coltman, T. (2007). Can Superior CRM Capabilities Improve Performance in Banking? Journal of Financial Services Marketing,
Vol. 12
[10]. Eisingerich, Andreas B., and Bell, Simon J. (2007). Maintaining customer relationships in high credence services. Journal of
Services Marketing, Vol. 21
[11]. Field, A. (2005), Discovering Statistics Using SPSS, 2nd ed., Sage, London.
[12]. Fifield, P. (2007). Marketing Strategy Masterclass: Making Marketing Strategy Happen. Butterworth-Heinemann.
[13]. Garver, M.S. and Mentzer, J.T. (1999), Logistics research method: employing structural equation modeling to test for construct
validity. Journal of Business Logistics, Vol. 20 (pp 33–57).
[14]. Hair, J., Black, W., Babin, B., Anderson, R. and Tatham, R. (2005), Multivariate Data Analysis, 6th ed., Prentice-Hall, Englewood
Cliffs, NY.
[15]. Hsieh Y, Chiu H, HsuY (2005), Supplier market orientation and accommodation of the customer in different relationship phases.
Journal of Management, Vol. 37 (pp 380– 393.)
[16]. Hollebeek, L. (2011), Exploring customer brand engagement: definition and themes. Journal of Strategic Marketing, Vol. 19,
(pp 555-573)
[17]. Jahangir, N. and Begum, N. (2008), The role of perceived usefulness, perceived ease of use, security and privacy, and customer
attitude to engender customer adaptation in the context of electronic banking. African Journal of Business Management, Vol. 2 (pp.
32-40)
[18]. Kar, M. (2006). A Classification of Relationship Marketing Strategies in Business to Consumer Markets. Manchester Business
School.
[19]. Khalifa, A.S. (2004). Customer Value: A Review of Recent Literature and an Integrative Configuration, Management Decision.
Journal of Management, Vol. 42 (pp 645–666)
[20]. Khalifa, M. and Shen, N.K. (2008). Explaining the adoption of transactional B2C mobile commerce. Journal of Enterprise
Information Management, Vol. 21(pp. 110-24.)
[21]. Kuria, Thuo J. (2007). Customer Relationship Management and Competitiveness of Commercial Banks in Kenya, Nairobi
University Press

[22]. Lin, H.H. and Wang, Y.S. (2005), Predicting consumer intention to use mobile commerce in Taiwan. Proceedings of the
International Conferences on Mobile Business, Sydney, Australia.
[23]. Liao, C.H., Tsou, C.W. and Huang, M.F. (2007). Factors influencing the usage of 3G mobile services in Taiwan. Online
Information Review, Vol. 31 (pp. 759-74).
[24]. Luarn, P. and Lin, H.H. (2005), Toward an understanding of the behavioral intention to use mobile banking. Journal of Human
Behaviour, Vol. 21(pp. 873-91).
[25]. Mylonakis, John. (2009). Customer Relationship Management Functions: A Survey of Greek Bank Customer Satisfaction
Perceptions. The Icfai University Journal of Bank Management, Vol. 8
[26]. Ndubisi N. O. (2004). Factorial and discriminant analyses of the underpinnings of Relationship Marketing and customer
satisfaction. International journal of Bank Marketing, Vol. 23
[27]. Ndubisi, O. N. (2006). Relationship marketing and customer loyalty. Journal of Marketing intelligence & planning, Vol. 25(pp
98-106).
[28]. Nunnally, J. (1978), Psychometric Theory. McGraw-Hill, New York, NY.
[29]. Ott, R.L. and Longnecker, M. (2001). An Introduction to Statistical Methods and Data Analysis. Thomson Learning Inc., Boston,
MA.
[30]. Vieira, A. L. and Ennew, C. (2004). The Nature of Marketing Relationship Perspectives from Providers and Clients in the Hotel
Sector. Journal of marketing
[31]. Wang, W.Q. and Benbasat, I. (2007). Recommendation agents for electronic commerce: effect of explanation facilities on trusting
beliefs, Journal of Management Information System, Vol. 23 (pp. 217-46).
[32]. Wong, C.C. and Hiew, P.L. (2005). Diffusion of mobile entertainment in Malaysia: drivers and Barriers. Journal of Enformatika,
Vol. 5(pp. 263-6).

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