The student as customer model and its impact on the academic leadership role in higher education

This paper posits that the 'student as customer' model has a negative impact upon the academic leadership which in turn is responsible for the erosion of objectivity in the assessment process in the higher education sector. The paper draws on the existing literature to explore the relationship between the student as customer model, academic leadership, and student assessment. The existing research emanating from the literature provides the basis from which the short comings of the student as customer model are exposed. From a practical perspective the arguments made in this paper provide the groundwork for possible future research into the adverse affects of the student as customer model on academic leadership and job satisfaction in the academic work force. The concern for quality may benefit from empirical investigation of the relationship between the student as customer model and quality learning and assessment outcomes in the higher education sector. The paper raises awareness of the faults with the present reliance on the student as customer model and the negative impact on both students and academic staff. The issues explored have the potential to influence the future directions of the higher education sector with regard to the social implications of their quest for quality educational outcomes. The paper addresses a gap in the literature in regard to use of the student as customer model and the subsequent adverse affect on academic leadership and assessment in higher education.

Investigating relationships between relationship quality, customer loyalty and cooperation : an empirical study of convenience stores' franchise chain systems

Small business has been shown to contribute significantly to a nation’s economic development. Small business owners typically confront challenges, uncertainty, and risks while operating new businesses. Franchising has become a way to minimize the risks of small business management (Chiou et al., 2004); however, a franchise system is not a guarantee of business success (Lee and Karkovista, 2001). A poor franchising relationship between franchisors and franchisees can result in franchise failure, such as termination and closure, or franchisee exit (Frazer and Winzar, 2005).

A conceptual model to link deep customer insights to both growth opportunities and organisational strategy in SME’s as part of a design led transformation journey

Although Design Led Innovation activities aim to raise the value of design within the business, knowledge about which tools are available to support companies and how to apply them to make the connection between design for new product development and design as a strategic driver of growth is needed. This paper presents a conceptual method to supplement existing process and tools to assist companies to grow through design. The model extends the authors’ previous work to explore how through storytelling, customer observation can be captured and translated into new meaning, then creating new design propositions shaped into product needs, which can drive internal business activities, brand and the strategic vision. The paper contributes to a gap in the theoretical frameworks and literature by highlighting the need to align and scale design processes which match the needs of SME’s as they transition along a trajectory to become design led businesses.

From supply to demand-driven opportunities : unravelling the role of the customer in opportunity identification processes

Successful identification and exploitation of opportunities has been an area of interest to many entrepreneurship researchers. Since Shane and Venkataraman’s seminal work (e.g. Shane and Venkataraman, 2000; Shane, 2000), several scholars have theorised on how firms identify, nurture and develop opportunities. The majority of this literature has been devoted to understanding how entrepreneurs search for new applications of their technological base or discover opportunities based on prior knowledge (Zahra, 2008; Sarasvathy et al., 2003). In particular, knowledge about potential customer needs and problems that may present opportunities is vital (Webb et al., 2010). Whereas the role of prior knowledge of customer problems (Shane, 2003; Shepherd and DeTienne, 2005) and positioning oneself in a so-called knowledge corridor (Fiet, 1996) has been researched, the role of opportunity characteristics and their interaction with customer-related mechanisms that facilitate and hinder opportunity identification has received scant attention.

Integration of opinion into customer analysis model

Nowadays, Opinion Mining is getting more important than before especially in doing analysis and forecasting about customers’ behavior for businesses purpose. The right decision in producing new products or services based on data about customers’ characteristics means profit for organization/company. This paper proposes a new architecture for Opinion Mining, which uses a multidimensional model to integrate customers’ characteristics and their comments about products (or services). The key step to achieve this objective is to transfer comments (opinions) to a fact table that includes several dimensions, such as, customers, products, time and locations. This research presents a comprehensive way to calculate customers’ orientation for all possible products’ attributes. A use case study is also presented in this paper to show the advantages of using OLAP and data cubes to analyze costumers’ opinions.

Beyond broadcasting: customer service, community and information experience in the Twittersphere

What opportunities does a channel like Twitter offer to libraries, beyond the realm of marketing? We would like to highlight three roles for Twitter in the academic library environment: Twitter as a service delivery and service recovery channel; Twitter as a community builder; Twitter as a site for information experience.

Identifying customer behaviour and dwell time using soft biometrics

In a commercial environment, it is advantageous to know how long it takes customers to move between different regions, how long they spend in each region, and where they are likely to go as they move from one location to another. Presently, these measures can only be determined manually, or through the use of hardware tags (i.e. RFID). Soft biometrics are characteristics that can be used to describe, but not uniquely identify an individual. They include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional biometrics, soft biometrics can be acquired by surveillance cameras at range without any user cooperation. While these traits cannot provide robust authentication, they can be used to provide identification at long range, and aid in object tracking and detection in disjoint camera networks. In this chapter we propose using colour, height and luggage soft biometrics to determine operational statistics relating to how people move through a space. A novel average soft biometric is used to locate people who look distinct, and these people are then detected at various locations within a disjoint camera network to gradually obtain operational statistics

Investigating Customer-Oriented Deviance (COD) from a frontline employee's perspective

While frontline employees (FLEs) are known to bend the rules or act in non-conforming ways for customers, the phenomenon of FLEs over-servicing customers is not well understood. This paper proposes a behavioural concept termed customer-oriented deviance (COD) and a conceptual model of its key drivers. Using a qualitative study involving 22 in-depth interviews with FLEs, the analysis reveals three categories of COD behaviours: deviant service adaptation (DSA), deviant service communication (DSC), and deviant use of resources (DUR). The drivers of COD are categorised as individual (risk-taking, service aptitude, and pro-social moral values), situational (resource availability, social capita with customers, legitimacy of customer problems, and avoidance of hassles), and organisational (unconducive service climate and anticipated rewards). This paper contributes to understanding how and why FLEs over-service customers and extends current research by exploring multiple categories of behaviours within a services marketing context.

Chronological categorization and decomposition of customer loads

The majority of distribution utilities do not have accurate information on the constituents of their loads. This information is very useful in managing and planning the network, adequately and economically. Customer loads are normally categorized in three main sectors: 1) residential; 2) industrial; and 3) commercial. In this paper, penalized least-squares regression and Euclidean distance methods are developed for this application to identify and quantify the makeup of a feeder load with unknown sectors/subsectors. This process is done on a monthly basis to account for seasonal and other load changes. The error between the actual and estimated load profiles are used as a benchmark of accuracy. This approach has shown to be accurate in identifying customer types in unknown load profiles, and is used in cross-validation of the results and initial assumptions.

Integration of opinion into customer analysis model

As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. In order to enhance customer satisfaction and their shopping experiences, it has become important to analysis customers reviews to extract opinions on the products that they buy. Thus, Opinion Mining is getting more important than before especially in doing analysis and forecasting about customers’ behavior for businesses purpose. The right decision in producing new products or services based on data about customers’ characteristics means profit for organization/company. This paper proposes a new architecture for Opinion Mining, which uses a multidimensional model to integrate customers’ characteristics and their comments about products (or services). The key step to achieve this objective is to transfer comments (opinions) to a fact table that includes several dimensions, such as, customers, products, time and locations. This research presents a comprehensive way to calculate customers’ orientation for all possible products’ attributes.