Customer experience management through emotional connection: an Indian perspective
05 Apr 2010-International Journal of Indian Culture and Business Management (Inderscience Publishers)-Vol. 3, Iss: 3, pp 333-348
TL;DR: In this article, the authors make an attempt to integrate these two broad themes with the propositions that it will infuse more life and realism into an understanding of consumer purchase process and theory of consumer satisfaction.
Abstract: The marketing power of emotion traces the manner in which companies rely on emoting to connect with consumers, develop new products, improve their strategic position and increase brand recognition. Emotion is not the only element in making buying decisions, but a necessary condition if decisions are not to be continually postponed. It has been debated in the literature that consumers while making purchase, decisions are purely driven by cognitions. Recent studies, while examining how consumers actually make decisions in various shopping contexts, establish the fact that consumers are more often mindless rather than mindful decision makers. For many reasons, market research communities have been giving too much emphasis to consider rationally driven behaviours, thereby largely ignoring the influence of emotions. The present study makes an attempt to integrate these two broad themes with the propositions that it will infuse more life and realism into an understanding of consumer purchase process and theory of consumer satisfaction.
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TL;DR: In this paper, a graph theoretic approach is proposed to consider all the design aspects together in a single methodology with the help of matrix algebra and permanents, which is essentially a virtual methodology which decides the process, the product strength and the weakness with a multinomial defined by using matrix algebra.
Abstract: The electroplating process has been widely accepted for plating automobile parts, aircraft parts and spacecraft components with high strength and stiffness to weight ratios, high quality and reliability, dimensional accuracy and surface finish of the product. This process is widely acceptable in various industries. Designing the electroplating products requires a lot of skill with multidisciplinary knowledge. The present approach offers a ‘virtual design’, which optimises the concurrent design approach and ultimately leads to the achievement of the six sigma limits, i.e. almost defect-free products from the plating technology. The new design utilises the advantages of the graph theoretic approach to consider all the design aspects together in a single methodology with the help of matrix algebra and permanents. It is essentially a virtual methodology which decides the process, the product strength and the weakness with the help of a multinomial defined by using matrix algebra. The design index, developed u...
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TL;DR: In this article , the authors investigated the influence of self-service technology applications' attributes on customer experience, relationship quality and reuse intention among customers in deposit money banks, and found that perceived ease of use and perceived usefulness were positively related with cognitive and affective experience.
Abstract: Services providers’ recent inclusion of technology-enabled mechanisms in services delivery especially in banking sector in developing economies are significantly replacing earlier human-to-human dominated pattern of bank–customer relationships. Notwithstanding glaring penetration of self-service technology applications in bank service delivery, how its attributes influence customer experience, relationship quality and reuse intention has been largely eluding research attention. Therefore, this article investigates influence of self-service technology applications’ attributes on customer experience, relationship quality and reuse intention among customers in deposit money banks. Data was collected from 310 respondents using online-based questionnaire. Structural Equation Modelling approach with the aid of SmartPLS serve as the analytical tool in the examination of hypothesized paths in the research schema. Findings reveal that perceived ease of use and perceived usefulness were positively related with cognitive and affective experience. Also, cognitive and affective experience had positive-significant influence on customer satisfaction; however, cognitive and affective experiences demonstrate statistically insignificant relationships with trust. Furthermore, satisfaction and trust positively and significantly correlate with reuse intention. Implication for theory and practice were put forward as well as suggestions for future research.
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Book•
01 Jan 1982
TL;DR: In this article, the authors present an overview of the basic concepts of multivariate analysis, including matrix algebra and random vectors, as well as a strategy for analyzing multivariate models.
Abstract: (NOTE: Each chapter begins with an Introduction, and concludes with Exercises and References.) I. GETTING STARTED. 1. Aspects of Multivariate Analysis. Applications of Multivariate Techniques. The Organization of Data. Data Displays and Pictorial Representations. Distance. Final Comments. 2. Matrix Algebra and Random Vectors. Some Basics of Matrix and Vector Algebra. Positive Definite Matrices. A Square-Root Matrix. Random Vectors and Matrices. Mean Vectors and Covariance Matrices. Matrix Inequalities and Maximization. Supplement 2A Vectors and Matrices: Basic Concepts. 3. Sample Geometry and Random Sampling. The Geometry of the Sample. Random Samples and the Expected Values of the Sample Mean and Covariance Matrix. Generalized Variance. Sample Mean, Covariance, and Correlation as Matrix Operations. Sample Values of Linear Combinations of Variables. 4. The Multivariate Normal Distribution. The Multivariate Normal Density and Its Properties. Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation. The Sampling Distribution of 'X and S. Large-Sample Behavior of 'X and S. Assessing the Assumption of Normality. Detecting Outliners and Data Cleaning. Transformations to Near Normality. II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS. 5. Inferences About a Mean Vector. The Plausibility of ...m0 as a Value for a Normal Population Mean. Hotelling's T 2 and Likelihood Ratio Tests. Confidence Regions and Simultaneous Comparisons of Component Means. Large Sample Inferences about a Population Mean Vector. Multivariate Quality Control Charts. Inferences about Mean Vectors When Some Observations Are Missing. Difficulties Due To Time Dependence in Multivariate Observations. Supplement 5A Simultaneous Confidence Intervals and Ellipses as Shadows of the p-Dimensional Ellipsoids. 6. Comparisons of Several Multivariate Means. Paired Comparisons and a Repeated Measures Design. Comparing Mean Vectors from Two Populations. Comparison of Several Multivariate Population Means (One-Way MANOVA). Simultaneous Confidence Intervals for Treatment Effects. Two-Way Multivariate Analysis of Variance. Profile Analysis. Repealed Measures, Designs, and Growth Curves. Perspectives and a Strategy for Analyzing Multivariate Models. 7. Multivariate Linear Regression Models. The Classical Linear Regression Model. Least Squares Estimation. Inferences About the Regression Model. Inferences from the Estimated Regression Function. Model Checking and Other Aspects of Regression. Multivariate Multiple Regression. The Concept of Linear Regression. Comparing the Two Formulations of the Regression Model. Multiple Regression Models with Time Dependant Errors. Supplement 7A The Distribution of the Likelihood Ratio for the Multivariate Regression Model. III. ANALYSIS OF A COVARIANCE STRUCTURE. 8. Principal Components. Population Principal Components. Summarizing Sample Variation by Principal Components. Graphing the Principal Components. Large-Sample Inferences. Monitoring Quality with Principal Components. Supplement 8A The Geometry of the Sample Principal Component Approximation. 9. Factor Analysis and Inference for Structured Covariance Matrices. The Orthogonal Factor Model. Methods of Estimation. Factor Rotation. Factor Scores. Perspectives and a Strategy for Factor Analysis. Structural Equation Models. Supplement 9A Some Computational Details for Maximum Likelihood Estimation. 10. Canonical Correlation Analysis Canonical Variates and Canonical Correlations. Interpreting the Population Canonical Variables. The Sample Canonical Variates and Sample Canonical Correlations. Additional Sample Descriptive Measures. Large Sample Inferences. IV. CLASSIFICATION AND GROUPING TECHNIQUES. 11. Discrimination and Classification. Separation and Classification for Two Populations. Classifications with Two Multivariate Normal Populations. Evaluating Classification Functions. Fisher's Discriminant Function...nSeparation of Populations. Classification with Several Populations. Fisher's Method for Discriminating among Several Populations. Final Comments. 12. Clustering, Distance Methods and Ordination. Similarity Measures. Hierarchical Clustering Methods. Nonhierarchical Clustering Methods. Multidimensional Scaling. Correspondence Analysis. Biplots for Viewing Sample Units and Variables. Procustes Analysis: A Method for Comparing Configurations. Appendix. Standard Normal Probabilities. Student's t-Distribution Percentage Points. ...c2 Distribution Percentage Points. F-Distribution Percentage Points. F-Distribution Percentage Points (...a = .10). F-Distribution Percentage Points (...a = .05). F-Distribution Percentage Points (...a = .01). Data Index. Subject Index.
11,697 citations
TL;DR: In this article, the authors present an overview of the basic concepts of multivariate analysis, including matrix algebra and random vectors, as well as a strategy for analyzing multivariate models.
Abstract: (NOTE: Each chapter begins with an Introduction, and concludes with Exercises and References.) I. GETTING STARTED. 1. Aspects of Multivariate Analysis. Applications of Multivariate Techniques. The Organization of Data. Data Displays and Pictorial Representations. Distance. Final Comments. 2. Matrix Algebra and Random Vectors. Some Basics of Matrix and Vector Algebra. Positive Definite Matrices. A Square-Root Matrix. Random Vectors and Matrices. Mean Vectors and Covariance Matrices. Matrix Inequalities and Maximization. Supplement 2A Vectors and Matrices: Basic Concepts. 3. Sample Geometry and Random Sampling. The Geometry of the Sample. Random Samples and the Expected Values of the Sample Mean and Covariance Matrix. Generalized Variance. Sample Mean, Covariance, and Correlation as Matrix Operations. Sample Values of Linear Combinations of Variables. 4. The Multivariate Normal Distribution. The Multivariate Normal Density and Its Properties. Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation. The Sampling Distribution of 'X and S. Large-Sample Behavior of 'X and S. Assessing the Assumption of Normality. Detecting Outliners and Data Cleaning. Transformations to Near Normality. II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS. 5. Inferences About a Mean Vector. The Plausibility of ...m0 as a Value for a Normal Population Mean. Hotelling's T 2 and Likelihood Ratio Tests. Confidence Regions and Simultaneous Comparisons of Component Means. Large Sample Inferences about a Population Mean Vector. Multivariate Quality Control Charts. Inferences about Mean Vectors When Some Observations Are Missing. Difficulties Due To Time Dependence in Multivariate Observations. Supplement 5A Simultaneous Confidence Intervals and Ellipses as Shadows of the p-Dimensional Ellipsoids. 6. Comparisons of Several Multivariate Means. Paired Comparisons and a Repeated Measures Design. Comparing Mean Vectors from Two Populations. Comparison of Several Multivariate Population Means (One-Way MANOVA). Simultaneous Confidence Intervals for Treatment Effects. Two-Way Multivariate Analysis of Variance. Profile Analysis. Repealed Measures, Designs, and Growth Curves. Perspectives and a Strategy for Analyzing Multivariate Models. 7. Multivariate Linear Regression Models. The Classical Linear Regression Model. Least Squares Estimation. Inferences About the Regression Model. Inferences from the Estimated Regression Function. Model Checking and Other Aspects of Regression. Multivariate Multiple Regression. The Concept of Linear Regression. Comparing the Two Formulations of the Regression Model. Multiple Regression Models with Time Dependant Errors. Supplement 7A The Distribution of the Likelihood Ratio for the Multivariate Regression Model. III. ANALYSIS OF A COVARIANCE STRUCTURE. 8. Principal Components. Population Principal Components. Summarizing Sample Variation by Principal Components. Graphing the Principal Components. Large-Sample Inferences. Monitoring Quality with Principal Components. Supplement 8A The Geometry of the Sample Principal Component Approximation. 9. Factor Analysis and Inference for Structured Covariance Matrices. The Orthogonal Factor Model. Methods of Estimation. Factor Rotation. Factor Scores. Perspectives and a Strategy for Factor Analysis. Structural Equation Models. Supplement 9A Some Computational Details for Maximum Likelihood Estimation. 10. Canonical Correlation Analysis Canonical Variates and Canonical Correlations. Interpreting the Population Canonical Variables. The Sample Canonical Variates and Sample Canonical Correlations. Additional Sample Descriptive Measures. Large Sample Inferences. IV. CLASSIFICATION AND GROUPING TECHNIQUES. 11. Discrimination and Classification. Separation and Classification for Two Populations. Classifications with Two Multivariate Normal Populations. Evaluating Classification Functions. Fisher's Discriminant Function...nSeparation of Populations. Classification with Several Populations. Fisher's Method for Discriminating among Several Populations. Final Comments. 12. Clustering, Distance Methods and Ordination. Similarity Measures. Hierarchical Clustering Methods. Nonhierarchical Clustering Methods. Multidimensional Scaling. Correspondence Analysis. Biplots for Viewing Sample Units and Variables. Procustes Analysis: A Method for Comparing Configurations. Appendix. Standard Normal Probabilities. Student's t-Distribution Percentage Points. ...c2 Distribution Percentage Points. F-Distribution Percentage Points. F-Distribution Percentage Points (...a = .10). F-Distribution Percentage Points (...a = .05). F-Distribution Percentage Points (...a = .01). Data Index. Subject Index.
10,148 citations
TL;DR: In this paper, the authors argue for the recognition of important experiential aspects of consumption, such as the symbolic, hedonic, and esthetic nature of the experience of consumption.
Abstract: This paper argues for the recognition of important experiential aspects of consumption. Specifically, a general framework is constructed to represent typical consumer behavior variables. Based on this paradigm, the prevailing information processing model is contrasted with an experiential view that focuses on the symbolic, hedonic, and esthetic nature of consumption. This view regards the consumption experience as a phenomenon directed toward the pursuit of fantasies, feelings, and fun.
7,029 citations
TL;DR: In this paper, the authors report an empirical assessment of a model of service encounters that simultaneously considers the direct effects of quality, satisfaction, and value on consumers' behavioral intentions, and further suggest that indirect effects of the service quality and value constructs enhanced their impact on behavioral intentions.
6,176 citations
Book•
07 Jan 1999
TL;DR: Holbrook as mentioned in this paper provides an innovative framework for the study of consumer value which is used to critically examine the nature and type of value that consumers derive from the consumption experience - effiency, excellence, status, esteem, play, aesthetics, ethics, spirituality.
Abstract: As shoppers, what factors influence our decision to purchase an object or service? Why do we chose one product over another? How do we attribute value as part of the shopping experience? The theme of 'serving' the customer and customer satisfaction is central to every formulation of the marketing concept, yet few books attenpt to define and analyse exactly what it is that consumers want In this provocative collection of essays, Morris Holbrook brings together a team of the top US and European scholars to discuss an issue of great importance to the study of marketing and consumer behaviour This ground-breaking, interdisciplinary book provides an innovative framework for the study of consumer value which is used to critically examine the nature and type of value that consumers derive from the consumption experience - effiency, excellence, status, esteem, play, aesthetics, ethics, spirituality Guaranteed to provoke debate and controversy, this is a courageous, individualistic and idiosyncratic book which should appeal to students of marketing, consumer behaviour, cultural studies and consumption studies
1,614 citations