The exploration of consumer power in online brand communities : a comparison case study in Australia and China

Aided by the development of information technology, the balance of power in the market place is rapidly shifting from marketers towards consumers and nowhere is this more obvious than in the online environment (Denegri-Knott, Zwick, & Schroeder, 2006; Moynagh & Worsley, 2002; Newcomer, 2000; Samli, 2001). From the inception and continuous development of the Internet, consumers are becoming more empowered. They can choose what they want to click on the Internet, they can shop and transact payments, watch and download video, chat with others, be it friends or even total strangers. Especially in online communities, like-minded consumers share and exchange information, ideas and opinions. One form of online community is the online brand community, which gathers specific brand lovers. As with any social unit, people form different roles in the community and exert different effects on each other. Their interaction online can greatly influence the brand and marketers. A comprehensive understanding of the operation of this special group form is essential to advancing marketing thought and practice (Kozinets, 1999). While online communities have strongly shifted the balance of power from marketers to consumers, the current marketing literature is sparse on power theory (Merlo, Whitwell, & Lukas, 2004). Some studies have been conducted from an economic point of view (Smith, 1987), however their application to marketing has been limited. Denegri-Knott (2006) explored power based on the struggle between consumers and marketers online and identified consumer power formats such as control over the relationship, information, aggregation and participation. Her study has built a foundation for future power studies in the online environment. This research project bridges the limited marketing literature on power theory with the growing recognition of online communities among marketing academics and practitioners. Specifically, this study extends and redefines consumer power by exploring the concept of power in online brand communities, in order to better understand power structure and distribution in this context. This research investigates the applicability of the factors of consumer power identified by Denegri-Knott (2006) to the online brand community. In addition, by acknowledging the model proposed by McAlexander, Schouten, & Koenig (2002), which emphasized that community study should focus on the role of consumers and identifying multiple relationships among the community, this research further explores how member role changes will affect power relationships as well as consumer likings of the brand. As a further extension to the literature, this study also considers cultural differences and their effect on community member roles and power structure. Based on the study of Hofstede (1980), Australia and China were chosen as two distinct samples to represent differences in two cultural dimensions, namely individualism verses collectivism and high power distance verses low power distance. This contribution to the research also helps answer the research gap identified by Muñiz Jr & O'Guinn (2001), who pointed out the lack of cross cultural studies within the online brand community context. This research adopts a case study methodology to investigate the issues identified above. Case study is an appropriate research strategy to answer “how” and “why” questions of a contemporary phenomenon in real-life context (Yin, 2003). The online brand communities of “Haloforum.net” in Australia and “NGA.cn” in China were selected as two cases. In-depth interviews were used as the primary data collection method. As a result of the geographical dispersion and the preference of a certain number of participants, online synchronic interviews via MSN messenger were utilized along with the face-to-face interviews. As a supplementary approach, online observation was carried over two months, covering a two week period prior to the interviews and a six week period following the interviews. Triangulation techniques were used to strengthen the credibility and validity of the research findings (Yin, 2003). The findings of this research study suggest a new definition of power in an online brand community. This research also redefines the consumer power types and broadens the brand community model developed by McAlexander et al. (2002) in an online context by extending the various relationships between brand and members. This presents a more complete picture of how the perceived power relationships are structured in the online brand community. A new member role is discovered in the Australian online brand community in addition to the four member roles identified by Kozinets (1999), in contrast however, all four roles do not exist in the Chinese online brand community. The research proposes a model which links the defined power types and identified member roles. Furthermore, given the results of the cross-cultural comparison between Australia and China showed certain discrepancies, the research suggests that power studies in the online brand community should be country-specific. This research contributes to the body of knowledge on online consumer power, by applying it to the context of an online brand community, as well as considering factors such as cross cultural difference. Importantly, it provides insights for marketing practitioners on how to best leverage consumer power to serve brand objective in online brand communities. This, in turn, should lead to more cost effective and successful communication strategies. Finally, the study proposes future research directions. The research should be extended to communities of different sizes, to different extents of marketer control over the community, to the connection between online and offline activities within the brand community, and (given the cross-cultural findings) to different countries. In addition, a greater amount of research in this area is recommended to determine the generalizability of this study.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

What student attributes affect experience of PBL in Virtual Space?

Some Engineering Faculties are turning to the problem-based learning (PBL)paradigm to engender necessary skills and competence in their graduates. Since, at the same time, some Faculties are moving towards distance education, questions are being asked about the effectiveness of PBL for technical fields such as Engineering when delivered in virtual space. This paper outlines an investigation of how student attributes affect their learning experience in PBL courses offered in virtual space. A frequency distribution was superimposed on the outcome space of a phenomenographical study on a suitable PBL course to investigate the effect of different student attributes on the learning experience. It was discovered that the quality, quantity, and style of facilitator interaction had the greatest impact on the student learning experience. This highlights the need to establish consistent student interaction plans and to set, and ensure compliance with, minimum standards with respect to facilitation and student interactions.

Childhood and social interaction in everyday life : an epilogue

The epilogue pulls together the conceptual and methodological significance of the papers in the special issue exploring childhood and social interaction in everyday life in Sweden, Norway, United States and Australia. In considering the special issue, four domains of childhood are identified and discussed: childhood is a social construct where children learn how to enter into and participate in their social organizations, competency is best understood when communicative practices are examined in situ, children’s talk and interaction show situated culture in action, and childhood consists of shared social orders between children and adults. Emerging analytic interests are proposed, including investigating how children understand locations and place. Finally, the epilogue highlights the core focus of this special issue, which is showing children’s own methods for making sense of their everyday contexts using the interactional and cultural resources they have to hand.

Improving the management of building life cycle : a data mining approach

The construction industry has adapted information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. The data generated within the construction industry has become increasingly overwhelming. Data mining is a sophisticated data search capability that uses classification algorithms to discover patterns and correlations within a large volume of data. This paper presents the selection and application of data mining techniques on maintenance data of buildings. The results of applying such techniques and potential benefits of utilising their results to identify useful patterns of knowledge and correlations to support decision making of improving the management of building life cycle are presented and discussed.

International epidemiology of prostate cancer : geographical distribution and secular trends

This review outlines current international patterns in prostate cancer incidence and mortality rates and survival, including recent trends and a discussion of the possible impact of prostate-specific antigen (PSA) testing on the observed data. Internationally, prostate cancer is the second most common cancer diagnosed among men (behind lung cancer), and is the sixth most common cause of cancer death among men. Prostate cancer is particularly prevalent in developed countries such as the United States and the Scandinavian countries, with about a six-fold difference between high-incidence and low-incidence countries. Interpretation of trends in incidence and survival are complicated by the increasing impact of PSA testing, particularly in more developed countries. As Western influences become more pronounced in less developed countries, prostate cancer incidence rates in those countries are tending to increase, even though the prevalence of PSA testing is relatively low. Larger proportions of younger men are being diagnosed with prostate cancer and living longer following diagnosis of prostate cancer, which has many implications for health systems. Decreasing mortality rates are becoming widespread among more developed countries, although it is not clear whether this is due to earlier diagnosis (PSA testing), improved treatment, or some combination of these or other factors.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Using data mining to improve building life cycle

The building life cycle process is complex and prone to fragmentation as it moves through its various stages. The number of participants, and the diversity, specialisation and isolation both in space and time of their activities, have dramatically increased over time. The data generated within the construction industry has become increasingly overwhelming. Most currently available computer tools for the building industry have offered productivity improvement in the transmission of graphical drawings and textual specifications, without addressing more fundamental changes in building life cycle management. Facility managers and building owners are primarily concerned with highlighting areas of existing or potential maintenance problems in order to be able to improve the building performance, satisfying occupants and minimising turnover especially the operational cost of maintenance. In doing so, they collect large amounts of data that is stored in the building’s maintenance database. The work described in this paper is targeted at adding value to the design and maintenance of buildings by turning maintenance data into information and knowledge. Data mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated through the maintenance process can be turned into useful information. This can be done using classification algorithms to discover patterns and correlations within a large volume of data. This paper presents how and what data mining techniques can be applied on maintenance data of buildings to identify the impediments to better performance of building assets. It demonstrates what sorts of knowledge can be found in maintenance records. The benefits to the construction industry lie in turning passive data in databases into knowledge that can improve the efficiency of the maintenance process and of future designs that incorporate that maintenance knowledge.

The impact of perceived stage of cancer on carers’ anxiety and depression during the patients’ final year of life

Objective: This paper explores the effects of perceived stage of cancer (PSOC) on carers' anxiety and depression during the patients' final year. Methods: A consecutive sample of patients and carers (N=98) were surveyed at regular intervals regarding PSOC, and anxiety and depression using the Hospital Anxiety and Depression Scale. Means were compared by gender using the Mann-Whitney U-test. The chi-square was used to analyse categorical data. Agreement between carers' and patients' PSOC was estimated using kappa statistics. Correlations between carers' PSOC and their anxiety and depression were calculated using the Spearman's rank correlation. Results: Over time, an increasing proportion of carers reported that the cancer was advanced, culminating at 43% near death. Agreement regarding PSOC was fair (kappa=0.29-0.34) until near death (kappa=0.21). Carers' anxiety increased over the year; depression increased in the final 6 months. Females were more anxious (p=0.049, 6 months; p=0.009, 3 months) than males, and more depressed until 1 month to death. The proportion of carers reporting moderate-severe anxiety almost doubled over the year to 27%, with more females in this category at 6 months (p=0.05). Carers with moderate-severe depression increased from 6 to 15% over the year. Increased PSOC was weakly correlated with increased anxiety and depression. Conclusions: Carers' anxiety exceeded depression in severity during advanced cancer. Females generally experienced greater anxiety and depression. Carers were more realistic than patients regarding the ultimate outcome, which was reflected in their declining mental health, particularly near the end.

Initial steps in optimal planning of a distribution system

This paper reports the initial steps of research on planning of rural networks for MV and LV. In this paper, two different cases are studied. In the first case, 100 loads are distributed uniformly on a 100 km transmission line in a distribution network and in the second case, the load structure become closer to the rural situation. In case 2, 21 loads are located in a distribution system so that their distance is increasing, distance between load 1 and 2 is 3 km, between 2 and 3 is 6 km, etc). These two models to some extent represent the distribution system in urban and rural areas, respectively. The objective function for the design of the optimal system consists of three main parts: cost of transformers, and MV and LV conductors. The bus voltage is expressed as a constraint and should be maintained within a standard level, rising or falling by no more than 5%.

Preliminary report on data mining techniques for life prediction

This project is an extension of a previous CRC project (220-059-B) which developed a program for life prediction of gutters in Queensland schools. A number of sources of information on service life of metallic building components were formed into databases linked to a Case-Based Reasoning Engine which extracted relevant cases from each source. In the initial software, no attempt was made to choose between the results offered or construct a case for retention in the casebase. In this phase of the project, alternative data mining techniques will be explored and evaluated. A process for selecting a unique service life prediction for each query will also be investigated. This report summarises the initial evaluation of several data mining techniques.