PHYSICAL EVIDENCE AND CUSTOMER PATRONAGE OF BANKS IN THE SOUTH-SOUTH ZONE OF NIGERIA

The purpose of this study was to assess empirically the impact of physical evidence on customer patronage of quoted Banks in south-south zone of Nigeria. The study population was 14 quoted Banks which are functionally registered and listed with the Nigerian Stock Exchange (NSE); and our unit for data generation was the top level managers of the selected banks and customers of the chosen banks which were randomly selected. Forty two (42) managers were randomly selected from the banks which constituted our respondents for the study. The Spearman’s Rank Correlation(SPRC) statistical tool was used in testing the postulated hypotheses. The result of the analysis showed that there is a positive and statistically significant correlation between the empirical referents of physical evidence and customer patronage. The study specifically revealed that efficient design of work place ambience, physical architecture and signs significantly impacts on sales volume, profit margin, and customer retention. Furthermore, the study concludes that organization size and information technology capability of banks significantly moderates the relationship between physical evidence and customer patronage. The study therefore recommends that the panacea to poor customer patronage is predicated on the bank’s ability to efficiently adopt physical evidence strategies while taking cognizance of organization’s size and information technology capabilities since they significantly impact on customer patronage

PHYSICAL EVIDENCE AND CUSTOMER PATRONAGE OF BANKS IN THE SOUTH-SOUTH ZONE OF NIGERIA BY ADIELE, KENNETH CHIMA G2010/MSC/MKT/FT/491 AUGUST, 2013 PHYSICAL EVIDENCE AND CUSTOMER PATRONAGE OF BANKS IN THE SOUTH-SOUTH ZONE OF NIGERIA BY ADIELE, KENNETH CHIMA G2010/MSC/MKT/FT/491 A THESIS SUBMITTED TO THE DEPARTMENT OF MARKETING FACULTY OF MANAGEMENT SCIENCES, UNIVERSITY OF PORT HARCOURT, PORT HARCOURT IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF MASTER OF SCIENCE (MSC) DEGREE IN MARKETING AUGUST, 2013 ABSTRACT The purpose of this study was to assess empirically the impact of physical evidence on customer patronage of quoted Banks in south-south zone of Nigeria The study population was 14 quoted Banks which are functionally registered and listed with the Nigerian Stock Exchange (NSE); and our unit for data generation was the top level managers of the selected banks and customers of the chosen banks which were randomly selected Forty two (42) managers were randomly selected from the banks which constituted our respondents for the study The Spearman’s Rank Correlation(SPRC) statistical tool was used in testing the postulated hypotheses The result of the analysis showed that there is a positive and statistically significant correlation between the empirical referents of physical evidence and customer patronage The study specifically revealed that efficient design of work place ambience, physical architecture and signs significantly impacts on sales volume, profit margin, and customer retention Furthermore, the study concludes that organization size and information technology capability of banks significantly moderates the relationship between physical evidence and customer patronage The study therefore recommends that the panacea to poor customer patronage is predicated on the bank’s ability to efficiently adopt physical evidence strategies while taking cognizance of organization’s size and information technology capabilities since they significantly impact on customer patronage DECLARATION I hereby declare that this work is the outcome of my own research and that it has not been previously submitted to this university or any other institution, either in part or in full of which I am aware, for the award of any other certificate or degree …………………………………… Adiele C Kenneth G2010/MSC/MKT/FT/491 CERTIFICATION This is to certify, this thesis, conducted by Adiele, Kenneth Chima (G2010/MSC/MKT/FT/491) of the Department of Marketing, Faculty of Management Sciences, University of Port Harcourt, is accepted in Partial fulfillment of the requirements for the award of Master of Science (MSC) Degree in Marketing with specialization in Services Marketing ………………………… Dr A C Ezirim Research Supervisor ………………………… Date ………………………… Dr I F Asiegbu Acting Head of Department, Marketing ………………………… Date ………………………… Prof S E Kalu Dean, Faculty of Mgt Sci., Uniport ………………………… Date DEDICATION This MSC thesis is dedicated to my dear wife, Mrs Nne Kenneth Adiele, and my children, Gabriella and Davida Kenneth Adiele ACKNOWLEDGEMENTS My sincere gratitude goes to the Almighty God, the greatest splendor of my life who never disappointed me, and also gave me the substance, wisdom, strength, kept me alive and saw me through the successful completion of this programme I will ever remember the immeasurable contributions of my supervisor Dr Aloy C Ezirim who provided insight, encouragement and friendship through-out this work This work is a product of his dedication and thoughtful contributions Moreso, I will not fail to acknowledge the immense contributions of Professor Sylva E Kalu the Dean, Faculty of Management Sciences University of Port Harcourt, for his academic mentorship and encouragement towards the successful completion of this programme Also my sincere appreciation goes to Dr Ikechukwu F Asiegbu, the current Head of Department Marketing, Faculty of Management Sciences, Uniport for his inestimable academic contributions and mentorship in the course of my Postgraduate programme in Uniport Furthermore, my appreciation goes to the formal co-ordinator of Postgraduate programme in Marketing Department, Dr John Amue for his mammoth contributions during the conceptualization stage of this thesis and also the current co-ordinator of the PG programme, Dr Ogbuji Chinedu for his tremendous academic impact in my life My candid appreciation goes to all my lecturers who taught and helped me to increase knowledge during my programme in Uniport They include Dr G A Okwandu, late Dr D W Maclayton, Dr Henry N Ozuru, Dr Anderson C Eketu, Dr Ikenna Chukwu, Mr Iruka Chijindu, Dr Awaa, Dr Igwe R Sunny, Mr Abiye Horsefall to mention but a few On the other hand, I must not fail to acknowledge my amiable and ever encouraging Head of Department of Marketing, RSUST Dr Bright C Opara for his scholarly input, encouragement and support to me during these years of my programme in Uniport Also my thanks go to Dr G A Okwandu and Dr N Gladson Nwokah for their academic research impact in my life More so, I appreciate Mr J U D Didia, Mr P M Nadube and Mrs Stella C Nwulu for their moral and academic support to me throughout the period of my programme in Uniport With gratitude, I acknowledge the moral and academic encouragement and comfort I received from my wife Mrs Nne Kenneth Adiele, for her understanding and for creating a peaceful home to ensure my academic pursuit Moreso, I sincerely appreciate my children Gabriella Aruchi Kenneth Adiele and Davida Chiburuoma Kenneth Adiele and my siblings for all their effort and encouragement towards me Finally, I must not fail to appreciate the moral, financial and spiritual support of my parents, Mr and Mrs Samuel C Adiele, for seeing me through from the cradle stage to the position I am today Similarly, my sincere appreciation goes to my spiritual head, pastor and friend, Rev Asuquo Akpan Ekpo, the provincial Presbyter of Church of God Mission Rumuepirikom Headquarter for all his spiritual and moral encouragement Furthermore, my immeasurable thanks goes to Mr Lawrence Nwibanaka the Admin Officer, Marketing Department, Uniport for his encouragements and supports God will definitely bless him for me Also my appreciation goes to my competent computer operators Miss Azubuike Chidera, Miss Ihua Patience and my data analyst Mr Winny, for their assiduous effort in ensuring that this thesis is successfully completed TABLE OF CONTENTS Cover page i Title page ii Abstract iii Declaration iv Certification v Dedication vi Acknowledgement vii Table of contents x CHAPTER ONE INTRODUCTION 1.1 Background to the study 1.2 Statement of the Problem 1.3 Objectives of the Study 10 1.4 Research Questions 11 1.5 Research Hypotheses 12 1.6 Significance of the study 13 1.7 Scope of the Study 16 1.8 Limitation of the study 18 1.9 Study Area 20 References 23 10 APPENDIX HYPOTHESIS THREE COMPUTING SPEARMAN’S RANK CORRELATION COEFFICIENT BETWEEN AMBIENT CONDITION (x) AND CUSTOMER RETENTION (y) IN BANKS IN SOUTH-SOUTH ZONE OF NIGERIA Nonparametric Correlations Correlat ions Spearman's rho Ambience Condition Correlation Coefficient Sig (2-tailed) N Perceived Service Quality Correlation Coefficient Sig (2-tailed) N Ambience Condition 1.000 42 570** 000 42 Perceived Service Quality 570** 000 42 1.000 42 ** Correlation is significant at the 0.01 level (2-tailed) Source: SPSS ver 15 Output window APPENDIX 10 HYPOTHESIS FOUR COMPUTING SPEARMAN’S RANK CORRELATION COEFFICIENT BETWEEN PHYSICAL ARCHITECTURE (x) AND SALES VOLUME (y) IN BANKS IN SOUTH-SOUTH ZONE OF NIGERIA Correlat ions Spearman's rho Spatial Layout Customer Satisfaction Correlation Coefficient Sig (2-tailed) N Correlation Coefficient Sig (2-tailed) N ** Correlation is significant at the 0.01 level (2-tailed) Source: SPSS ver 15 Output window 300 Customer Spatial Layout Satisfaction 1.000 684** 000 42 42 684** 1.000 000 42 42 APPENDIX 11 HYPOTHESIS FIVE COMPUTING SPEARMAN’S RANK CORRELATION COEFFICIENT BETWEEN PHYSICAL ARCHITECTURE (x) AND PROFIT MARGIN (y) IN BANKS IN SOUTH-SOUTH ZONE OF NIGERIA Correlat ions Spearman's rho Spatial Layout Customer Loyalty Correlation Coefficient Sig (2-tailed) N Correlation Coefficient Sig (2-tailed) N Spatial Layout 1.000 42 681** 000 42 Customer Loyalty 681** 000 42 1.000 42 ** Correlation is significant at the 0.01 level (2-tailed) Source: SPSS ver 15 Output window APPENDIX 12 HYPOTHESIS SIX COMPUTING SPEARMAN’S RANK CORRELATION COEFFICIENT BETWEEN PHYSICAL ARCHITECTURE (x) AND CUSTOMER RETENTION (y) IN BANKS IN SOUTH-SOUTH ZONE OF NIGERIA Correlat ions Spearman's rho Spatial Layout Correlation Coefficient Sig (2-tailed) N Perceived Service Quality Correlation Coefficient Sig (2-tailed) N ** Correlation is significant at the 0.01 level (2-tailed) Source: SPSS ver 15 Output window 301 Perceived Service Spatial Layout Quality 1.000 709** 000 42 42 709** 1.000 000 42 42 APPENDIX 13 HYPOTHESIS SEVEN COMPUTING SPEARMAN’S RANK CORRELATION COEFFICIENT BETWEEN SIGNS (x) AND SALES VOLUME (y) IN BANKS IN SOUTH-SOUTH ZONE OF NIGERIA Correlat ions Spearman's rho Signs Customer Satisfaction Customer Signs Satisfaction 1.000 736** 000 42 42 736** 1.000 000 42 42 Correlation Coefficient Sig (2-tailed) N Correlation Coefficient Sig (2-tailed) N ** Correlation is significant at the 0.01 level (2-tailed) Source: SPSS ver 15 Output window APPENDIX 14 HYPOTHESIS EIGHT COMPUTING SPEARMAN’S RANK CORRELATION COEFFICIENT BETWEEN SIGNS (x) AND PROFIT MARGIN (y) IN BANKS IN SOUTH-SOUTH ZONE OF NIGERIA Correlat ions Spearman's rho Signs Customer Loyalty Correlation Coefficient Sig (2-tailed) N Correlation Coefficient Sig (2-tailed) N ** Correlation is significant at the 0.01 level (2-tailed) Source: SPSS ver 15 Output window 302 Signs 1.000 42 614** 000 42 Customer Loyalty 614** 000 42 1.000 42 APPENDIX 15 HYPOTHESIS NINE COMPUTING SPEARMAN’S RANK CORRELATION COEFFICIENT BETWEEN SIGNS (x) AND CUSTOMER RETENTION (y) IN BANKS IN SOUTH-SOUTH ZONE OF NIGERIA Correlat ions Spearman's rho Signs Correlation Coefficient Sig (2-tailed) N Perceived Service Quality Correlation Coefficient Sig (2-tailed) N ** Correlation is significant at the 0.01 level (2-tailed) Source: SPSS ver 15 Output window 303 Perceived Service Signs Quality 1.000 658** 000 42 42 658** 1.000 000 42 42 APPENDIX 16 HYPOTHESIS TEN COMPUTING PARTIAL CORRELATION COEFFICIENT OF THE RELATIONSHIP BETWEEN PHYSICAL EVIDENCE AND CUSTOMER PATRONAGE IN BANKS IN SOUTH-SOUTH ZONE OF NIGERIA CONTROLLING FOR ORGANIZATION SIZE MODERATING THE RELATIONSHIP Correlat ions Control Variables -none-a Physical Evidence Customer Patronage Organization Size Organization Size Physical Evidence Customer Patronage Correlation Significance (2-tailed) df Correlation Significance (2-tailed) df Correlation Significance (2-tailed) df Correlation Significance (2-tailed) df Correlation Significance (2-tailed) df a Cells contain zero-order (Pearson) correlations Source: SPSS ver 15 Output window 304 Physical Evidence 1.000 888 000 40 593 000 40 1.000 824 000 39 Customer Patronage 888 000 40 1.000 617 000 40 824 000 39 1.000 Organization Size 593 000 40 617 000 40 1.000 APPENDIX 17 HYPOTHESIS ELEVEN COMPUTING PARTIAL CORRELATION COEFFICIENT OF THE RELATIONSHIP BETWEEN PHYSICAL EVIDENCE AND CUSTOMER PATRONAGE IN BANKS IN SOUTH-SOUTH ZONE OF NIGERIA CONTROLLING FOR INFORMATION TECHNOLOGY CAPABILITY MODERATING THE RELATIONSHIP Correlat ions Control Variables -none-a Physical Evidence Customer Patronage Information Tech Information Tech Physical Evidence Customer Patronage Correlation Significance (2-tailed) df Correlation Significance (2-tailed) df Correlation Significance (2-tailed) df Correlation Significance (2-tailed) df Correlation Significance (2-tailed) df a Cells contain zero-order (Pearson) correlations Source: SPSS ver 15 Output window 305 Physical Evidence 1.000 888 000 40 500 001 40 1.000 851 000 39 Customer Patronage 888 000 40 1.000 492 001 40 851 000 39 1.000 Information Tech 500 001 40 492 001 40 1.000 APPENDIX 18 COMPUTING MULTIPLE REGRESSION ANALYSIS WITH SALES VOLUME (Y) AGAINST AMBIENT CONDITION (X1), PHYSICAL ARCHITECTURE (X2), AND SIGNS (X3) IN BANKS IN SOUTH-SOUTH ZONE OF NIGERIA The equation for the regression model is given as (since there are three independent variables): y = a + b1x1 + b2x2 + b3x3 + e Where y, is the independent variable X1 = Ambient Condition X2 = Physical Architecture X3 = Signs y = dependent variable (Sales Volume) a = is the intercept b1, b2 & b3 = partial regression coefficients e = error term N = total number of observations = 42 Regression [DataSet1] F:\Data Files\Adiele\Ana-Adiele-Physical Evidence=20Apr13A.sav b Variables Ent ered/Removed Model Variables Entered Signs, Ambience Condition, a Spatial Layout Variables Removed Method Enter a All requested variables entered b Dependent Variable: Customer Satisfaction Model Summary Model R R Square 856 a 733 Adjusted R Square 711 Std Error of the Estimate 39383 a Predictors: (Constant), Signs, Ambience Condition, Spatial Layout 306 ANOVAb Model Regression Residual Total Sum of Squares 16.145 5.894 22.039 df Mean Square 5.382 155 38 41 F 34.699 Sig .000 a a Predictors: (Constant), Signs, Ambience Condition, Spatial Layout b Dependent Variable: Customer Satisfaction Coefficient sa Model (Constant) Ambience Condition Spatial Layout Signs Unstandardized Coefficients B Std Error -.168 219 567 140 151 162 396 140 Standardized Coefficients Beta 475 116 366 t -.767 4.057 934 2.836 Sig .448 000 356 007 a Dependent Variable: Customer Satisfaction APPENDIX 19 COMPUTING MULTIPLE REGRESSION ANALYSIS WITH PROFIT MARGIN (Y) AGAINST AMBIENT CONDITION (X1), PHYSICAL ARCHITECTURE (X2), AND SIGNS (X3) IN BANKS IN SOUTH-SOUTH ZONE OF NIGERIA The equation for the regression model is given as (since there are three independent variables): y = a + b1x1 + b2x2 + b3x3 + e Where y, is the independent variable X1 = Ambient Condition X2 = Physical Architecture X3 = Signs y = dependent variable (Profit margin) a = is the intercept b1, b2 & b3 = partial regression coefficients e = error term N = total number of observations = 42 307 Regression [DataSet1] F:\Data Files\Adiele\Ana-Adiele-Physical Evidence=20Apr13A.sav b Variables Ent ered/Removed Model Variables Entered Signs, Ambience Condition, Spatial a Layout Variables Removed Method Enter a All requested variables entered b Dependent Variable: Customer Loyalty Model Summary Model R R Square 846 a 715 Adjusted R Square 693 Std Error of the Estimate 35084 a Predictors: (Constant), Signs, Ambience Condition, Spatial Layout ANOVAb Model Regression Residual Total Sum of Squares 11.742 4.677 16.419 df 38 41 Mean Square 3.914 123 F 31.798 Sig .000 a a Predictors: (Constant), Signs, Ambience Condition, Spatial Layout b Dependent Variable: Customer Loyalty Coefficient sa Model (Constant) Ambience Condition Spatial Layout Signs Unstandardized Coefficients B Std Error 038 195 494 125 405 144 104 125 a Dependent Variable: Customer Loyalty 308 Standardized Coefficients Beta 480 360 111 t 197 3.969 2.806 835 Sig .845 000 008 409 APPENDIX 20 COMPUTING MULTIPLE REGRESSION ANALYSIS WITH CUSTOMER RETENTION (Y) AGAINST AMBIENT CONDITION (X1), PHYSICAL ARCHITECTURE (X2), AND SIGNS (X3) IN BANKS IN SOUTH-SOUTH ZONE OF NIGERIA The equation for the regression model is given as (since there are three independent variables): y = a + b1x1 + b2x2 + b3x3 + e Where y, is the independent variable X1 = Ambient Condition X2 = Physical Architecture X3 = Signs y = dependent variable (Customer retention) a = is the intercept b1, b2 & b3 = partial regression coefficients e = error term N = total number of observations = 42 Regression [DataSet1] F:\Data Files\Adiele\Ana-Adiele-Physical Evidence=20Apr13A.sav b Variables Ent ered/Removed Model Variables Entered Signs, Ambience Condition, Spatial a Layout Variables Removed Method Enter a All requested variables entered b Dependent Variable: Perceived Service Quality Model Summary Model R R Square 836 a 698 Adjusted R Square 675 Std Error of the Estimate 42854 a Predictors: (Constant), Signs, Ambience Condition, Spatial Layout 309 ANOVAb Model Regression Residual Total Sum of Squares 16.158 6.979 23.136 df 38 41 Mean Square 5.386 184 F 29.327 Sig .000 a a Predictors: (Constant), Signs, Ambience Condition, Spatial Layout b Dependent Variable: Perceived Service Quality Coefficient sa Model Unstandardized Coefficients B Std Error -.357 238 486 152 518 176 177 152 (Constant) Ambience Condition Spatial Layout Signs Standardized Coefficients Beta 398 388 159 t -1.501 3.199 2.939 1.162 a Dependent Variable: Perceived Service Quality Factor Analysis [DataSet1] F:\Data Files\Adiele\Ana-Adiele-Physical Evidence=20Apr13A.sav Communalit ies Ambience Condition Spatial Layout Signs Customer Satisfaction Customer Loyalty Perceived Service Quality Organization Size Information Tech Initial 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Extraction 733 678 732 839 810 831 462 331 Extraction Method: Principal Component Analysis 310 Sig .142 003 006 252 Tot al Variance Explained Component Total 5.416 969 539 385 256 189 151 096 Initial Eigenvalues % of Variance Cumulative % 67.698 67.698 12.115 79.813 6.735 86.548 4.807 91.354 3.203 94.557 2.358 96.915 1.890 98.806 1.194 100.000 Extraction Method: Principal Component Analysis a Component Mat rix Compone nt Ambience Condition 856 Spatial Layout 823 Signs 856 Customer Satisfaction 916 Customer Loyalty 900 Perceived Service Quality 912 Organization Size 680 Information Tech 575 Extraction Method: Principal Component Analysis a components extracted 311 Extraction Sums of Squared Loadings Total % of Variance Cumulative % 5.416 67.698 67.698