Market-oriented new product development of functional beverages

Strategic reviews of the Irish Food and Beverage Industry have consistently emphasised the need for food and beverage firms to improve their innovation and marketing capabilities, in order to maintain competitiveness in both domestic and overseas markets. In particular, the functional food and beverages market has been singled out as an extremely important emerging market, which Irish firms could benefit from through an increased technological and market orientation. Although health and wellness have been the most significant drivers of new product development (NPD) in recent years, failure rates for new functional foods and beverages have been reportedly high. In that context, researchers in the US, UK, Denmark and Ireland have reported a marked divergence between NPD practices within food and beverage firms and normative advice for successful product development. The high reported failure rates for new functional foods and beverages suggest a failure to manage customer knowledge effectively, as well as a lack of knowledge management between functional disciplines involved in the NPD process. This research explored the concept of managing customer knowledge at the early stages of the NPD process, and applied it to the development of a range of functional beverages, through the use of advanced concept optimisation research techniques, which provided for a more market-oriented approach to new food product development. A sequential exploratory research design strategy using mixed research methods was chosen for this study. First, the qualitative element of this research investigated customers’ choice motives for orange juice and soft drinks, and explored their attitudes and perceptions towards a range of new functional beverage concepts through a combination of 15 in-depth interviews and 3 focus groups. Second, the quantitative element of this research consisted of 3 conjoint-based questionnaires administered to 400 different customers in each study in order to model their purchase preferences for chilled nutrient-enriched and probiotic orange juices, and stimulant soft drinks. The in-depth interviews identified the key product design attributes that influenced customers’ choice motives for orange juice. The focus group discussions revealed that groups of customers were negative towards the addition of certain functional ingredients to natural foods and beverages. K-means cluster analysis was used to quantitatively identify segments of customers with similar preferences for chilled nutrient-enriched and probiotic orange juices, and stimulant soft drinks. Overall, advanced concept optimisation research methods facilitate the integration of the customer at the early stages of the NPD process, which promotes a multi-disciplinary approach to new food product design. This research illustrated how advanced concept optimisation research methods could contribute towards effective and efficient knowledge management in the new food product development process.

Innovation in Ireland's 'high-technology' businesses: the roles of interaction and proximity

This thesis explores the drivers of innovation in Irish high-technology businesses and estimates, in particular, the relative importance of interaction with external businesses and other organisations as a source of knowledge for innovation at the business-level. The thesis also examines the extent to which interaction for innovation in these businesses occurs on a local or regional basis. The study uses original survey data of 184 businesses in the Chemical and Pharmaceutical, Information and Communications Technology and Engineering and Electronic Devices sectors. The study considers both product and process innovation at the level of the business and develops new measures of innovation output. For the first time in an Irish study, the incidence and frequency of interaction is measured for each of a range of agents, other group companies, suppliers, customers, competitors, academic-based researchers and innovation-supporting agencies. The geographic proximity between the business and each of the most important of each of each category of agent is measured using average one-way driving distance, which is the first time such a measure has been used in an Irish study of innovation. Utilising econometric estimation techniques, it is found that interaction with customers, suppliers and innovation-supporting agencies is positively associated with innovation in Irish high-technology businesses. Surprisingly, however, interaction with academic-based researchers is found to have a negative effect on innovation output at the business-level. While interaction generally emerges as a positive influence on business innovation, there is little evidence that this occurs at a local or regional level. Furthermore, there is little support for the presence of localisation economies for high-technology sectors, though some tentative evidence of urbanisation economies. This has important implications for Irish regional, enterprise and innovation policy, which has emphasised the development of clusters of internationally competitive businesses. The thesis brings into question the suitability of a cluster-driven network based approach to business development and competitiveness in an Irish context.

Finding optimal alternatives based on efficient comparative preference inference

Choosing the right or the best option is often a demanding and challenging task for the user (e.g., a customer in an online retailer) when there are many available alternatives. In fact, the user rarely knows which offering will provide the highest value. To reduce the complexity of the choice process, automated recommender systems generate personalized recommendations. These recommendations take into account the preferences collected from the user in an explicit (e.g., letting users express their opinion about items) or implicit (e.g., studying some behavioral features) way. Such systems are widespread; research indicates that they increase the customers' satisfaction and lead to higher sales. Preference handling is one of the core issues in the design of every recommender system. This kind of system often aims at guiding users in a personalized way to interesting or useful options in a large space of possible options. Therefore, it is important for them to catch and model the user's preferences as accurately as possible. In this thesis, we develop a comparative preference-based user model to represent the user's preferences in conversational recommender systems. This type of user model allows the recommender system to capture several preference nuances from the user's feedback. We show that, when applied to conversational recommender systems, the comparative preference-based model is able to guide the user towards the best option while the system is interacting with her. We empirically test and validate the suitability and the practical computational aspects of the comparative preference-based user model and the related preference relations by comparing them to a sum of weights-based user model and the related preference relations. Product configuration, scheduling a meeting and the construction of autonomous agents are among several artificial intelligence tasks that involve a process of constrained optimization, that is, optimization of behavior or options subject to given constraints with regards to a set of preferences. When solving a constrained optimization problem, pruning techniques, such as the branch and bound technique, point at directing the search towards the best assignments, thus allowing the bounding functions to prune more branches in the search tree. Several constrained optimization problems may exhibit dominance relations. These dominance relations can be particularly useful in constrained optimization problems as they can instigate new ways (rules) of pruning non optimal solutions. Such pruning methods can achieve dramatic reductions in the search space while looking for optimal solutions. A number of constrained optimization problems can model the user's preferences using the comparative preferences. In this thesis, we develop a set of pruning rules used in the branch and bound technique to efficiently solve this kind of optimization problem. More specifically, we show how to generate newly defined pruning rules from a dominance algorithm that refers to a set of comparative preferences. These rules include pruning approaches (and combinations of them) which can drastically prune the search space. They mainly reduce the number of (expensive) pairwise comparisons performed during the search while guiding constrained optimization algorithms to find optimal solutions. Our experimental results show that the pruning rules that we have developed and their different combinations have varying impact on the performance of the branch and bound technique.

Symptom burden in individuals with inflammatory bowel disease: a mixed methods study

To investigate the symptom burden experiences of individuals with inflammatory bowel disease (IBD). An explanatory sequential mixed methods study was conducted. A cross-sectional, correlational survey was first undertaken. Symptom burden was measured using a modified disease specific version of the Memorial Symptom Assessment Scale, which was administered to a consecutive sample of individuals with IBD (n = 247) at an IBD Outpatients department in one urban teaching hospital in Ireland. Disease activity was determined using clinical disease activity indices, which were completed by the consulting physician. A sequential qualitative, descriptive study was then conducted aimed at explaining noteworthy quantitative findings. A criterion-related purposeful sample of seven participants from the quantitative study was recruited. Semi-structured face to face interviews were conducted using an interview guide and data were analysed using content analysis. Findings revealed that participants experienced a median of 10 symptoms during the last week, however as many as 16 symptoms were experienced during active disease. The most burdensome symptoms were lack of energy, bowel urgency, diarrhoea, feeling bloated, flatulence and worry. Total symptom burden was found to be low with a mean score of 0.56 identified out of a possible range from 0 to 4. Participants with active disease (M = 0.81, SD = 0.48; n = 68) had almost double mean total symptom burden scores than participants with inactive disease (M = 0.46, SD = 0.43; n = 166) (p < 0.001). Mean total psychological symptom burden was found to be significantly greater than mean total physical symptom burden (rho = 0.73, n = 247, p < 0.001). Self-reported disease control, gender, number of flare ups in the last two years, and smoking status was found to be significant predictors of total symptom burden, with self-reported disease control identified as the strongest predictor. Qualitative data revealed tiredness, pain, bowel symptoms, worry and fear as being burdensome. Furthermore, symptom burden experiences were described in terms of its impact on restricting aspects of daily activities, which accumulated into restrictions on general life events. Psychological symptom burden was revealed as more problematic than physical symptom burden due to its constant nature, with physical and psychological symptoms described to occur in a cyclical manner. Participants revealed that disease control was evaluated not only in terms of symptoms, but also in terms of their abilities to control the impact of symptoms on their lives. This study highlights the considerable number of symptoms and the most burdensome symptoms experienced by individuals with IBD, both during active and inactive disease. This study has important implications on symptom assessment in terms of the need to encompass both physical and psychological symptoms. In addition, greater attention needs to be placed on psychological aspects of IBD care.