Towards an understanding of creativity and innovation within professional services and marketing

Greatly increased competition in the professional services and marketing sectors has reinforced the need for firms to develop an organisational culture that facilitates adaptation to changing conditions and competitive threats. More generally, competitive changes within business environments require new and creative ways of organising and managing firms. In order for marketing practitioners to be ahead of competitors in identifying and meeting customer needs, creative thought is essential. An organisational culture that supports creativity and facilitates the adoption of innovative practices, products and services, improves a firm’s ability to remain competitive within an uncertain environment. This paper presents a conceptual model of an organisational culture supporting creativity and innovation. The model posits relationships between creativity and innovation and organisational performance. The main contribution of the paper is to build a foundation for future research directed primarily towards exploring the relationships in the theoretical model of interest. Implications for marketing practitioners are also considered.

Participatory approach for the identification of dairy industry needs in the design of research, development and extension actions: Australian and Brazilian case studies

In both Australia and Brazil there are rapid changes occurring in the macroenvironment of the dairy industry. These changes are sometimes not noticed in the microenvironment of the farm, due to the labour-intensive nature of family farms, and the traditionally weak links between production and marketing. Trends in the external environment need to be discussed in a cooperative framework, to plan integrated actions for the dairy community as a whole and to demand actions from research, development and extension (R, D & E). This paper reviews the evolution of R, D & E in terms of paradigms and approaches, the present strategies used to identify dairy industry needs in Australia and Brazil, and presents a participatory strategy to design R, D & E actions for both countries. The strategy incorporates an integration of the opinions of key industry actors ( defined as members of the dairy and associated communities), especially farm suppliers ( input market), farmers, R, D & E people, milk processors and credit providers. The strategy also uses case studies with farm stays, purposive sampling, snowball interviewing techniques, semi-structured interviews, content analysis, focus group meetings, and feedback analysis, to refine the priorities for R, D & E actions in the region.

Introducing employee social identification to customer satisfaction research: a hotel industry study

The purpose of this paper is to address the concept of linkage research and propose the addition of social identity theory as an important consideration in managing employee-customer interactions and customer satisfaction.

Warranty servicing strategies to improve customer satisfaction

Customer satisfaction with a purchased product depends on its performance under warranty and during the remainder of its useful life. Dissatisfaction with an item is important to a manufacturer since it can lead to the loss of potential customers through the negative word-of-mouth effect as well as existing customers switching to a competitor. In this paper, we define satisfaction in terms of the likelihood of a customer not switching to a different manufacturer when a new item needs to be purchased. Manufacturers can use specific servicing strategies to reduce warranty costs and this topic has already been addressed in the literature without considering the effect of customer dissatisfaction. In this paper, we propose particular strategies that will increase customer satisfaction and we discuss methods for obtaining the optimal parameters of these strategies.

Making decisions with the future in mind: Developmental and comparative identification of mental time travel

Mechanisms that produce behavior which increase future survival chances provide an adaptive advantage. The flexibility of human behavior is at least partly the result of one such mechanism, our ability to travel mentally in time and entertain potential future scenarios. We can study mental time travel in children using language. Current results suggest that key developments occur between the ages of three to five. However, linguistic performance can be misleading as language itself is developing. We therefore advocate the use of methodologies that focus on future-oriented action. Mental time travel required profound changes in humans' motivational system, so that current behavior could be directed to secure not just present, but individually anticipated future needs. Such behavior should be distinguishable from behavior based on current drives, or on other mechanisms. We propose an experimental paradigm that provides subjects with an opportunity to act now to satisfy a need not currently experienced. This approach may be used to assess mental time travel in nonhuman animals. We conclude by describing a preliminary study employing an adaptation of this paradigm for children. (c) 2005 Elsevier Inc. All rights reserved.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.