Topic: Strategic decision-making, managerial cognition, and environmental uncertainty
Supervisor: Professor Riccardo Vecchiato
Concepts of uncertainty have long been at the heart of many of the core theories used in strategic management to understand competitive advantage as well as organisational boundaries. Uncertainty has many important implications for numerous dimensions of organisation (such as new organisational forms, global R&D, integration-responsiveness), as well as the many initiatives (such as corporate venturing, corporate transformation) or techniques (such as scenario planning, real options analysis) that might be used in the strategy process to cope with uncertainty. Some of these approaches have been used for some time with uneven success, and others are just now being adopted by organisations.
The proposed project will conduct a number of qualitative and quantitative analyses to investigate how organisations might address growing uncertainty and enhance their effectiveness in responding to changes in the global business environment. We also aim at exploring the interplay between strategy making and cognition and the impact of this interplay on competition outcomes.
Topic: Foresight for innovation and creativity in corporate organisations
Supervisors: Professor Riccardo Vecchiato, and Dr Evy Sakellariou
Literature in the field of foresight indicates that there is a gap on connecting organisational foresight to innovation and R&D management practices.
Therefore, the main goal of this PhD project is to explore how to integrate foresight techniques such as scenario-planning into a company's innovation process. The study aims to develop a framework which can be used by organisations to foster the development of (future) inventions. In order to gain in-depth understanding of the field and to contribute with valuable knowledge, the candidate should conduct multiple research and/or multi-staged studies. Taking into consideration the nature of the topic and field, qualitative research methods would fit best for this kind of project. Workshops, interviews, and ethnographic research can be applied. Due to the multi-staged approach, conducting single or multiple case studies with selected companies are considered. Data would be collected by taking field notes, audio and/or video recordings and generating transcripts. The data would be analysed also with qualitative research software.
Topic: Future-oriented sense making for novel understandings and creativity
Supervisors: Dr Evy Sakellariou and Dr Ivan Zupic
Future-oriented sense making is an important social process that enables organisational actors to structure the future by imagining a new desirable state (new prototypes, new services). To make sense is to organise, and sense making refers to processes of organising complex or ambiguous information using the technology of language-processes of articulating, labelling and categorising, for instance. Although past studies have widely explored retrospective sense making in organisational crisis or transformational change, little is known of the process of making and giving future-oriented sense within groups for discovering new meanings in complex phenomena.
This research examines how organisational actors and their collaborators (such as groups of innovation managers/entrepreneurs and customers) make plausible collective sense of customer-related problems to create new products or services. Research methods include ethnography, textual and video analysis, and real-time analysis of situations and events.
Topic: Organisational creativity, storytelling and metaphors
Supervisors: Dr Evy Sakellariou and Dr Bahare Afrahi
Organisational creativity (the generation of novel ideas within small or established organisations) is enhanced from dialectic – for example, discussions based around the different perspectives, experiences and expertise of individuals within innovation teams. However, studies on organisational creativity have largely focused on individual cognitive processes and creativity techniques.
This research will examine how the use of stories and metaphors in discussions within organisational teams and/or workshops (in established firms or in start-ups) can enhance organisational creativity. Research methods include in-depth interviews, non-participant observation, ethnography, textual and video analysis, and real-time analysis of situations and events.
Topics: Responsible management for sustainability, social responsibility management, global market and ethical responsibility, and sustainable development and community relations
Supervisors: Professor Fatima Annan-Diab and Dr Ana Pedraz Marcos
A PhD in Corporate Social Responsibility ("CSR") generally emphasises a multi-disciplinary approach on social, ethical and environmental issues. It links to community relations, global awareness and risk management abilities in business.
CSR is concerned with treating the stakeholders of a company or institution ethically or in a responsible manner. 'Ethically or responsible' means treating key stakeholders in a manner deemed acceptable according to international norms. Social responsibility includes economic and environmental responsibility.
Within the wider international business context, corporate responsibility has become an extremely important factor influencing the development of companies, their profits and brand image. The practice of CSR aims to preserve community relations and the profitability of the corporation or the integrity of the institution in order to achieve sustainable development in societies.
Students will grow their expertise as they develop extensive theoretical knowledge and expand their independent thinking capabilities in this research programme. PhD graduates in CSR practice in different work environments from academic institutions and non-profit organisations, to corporations, governmental bodies or consultancy firms.
Career prospects include: an academic career, strategic communicator, sustainability consultant, environmental and social risk manager, sustainability commercial director, corporate governance manager and senior ecologist etc.
Topic: Strategies for growth, the role of innovation.
Supervisor: Dr Pauline Parker
Email : p.parker@kingston.ac.uk
The drive for organic growth, particularly in technology markets, cannot rely on simple product enhancements to benefits and features. To drive the success of innovation initiatives the concept of an innovation strategy that encompasses all the (dynamic) capabilities of the organisation is needed.
This research aims to utilise the dynamic capabilities framework and identify key factors associated to the successful implementation of innovation initiatives.
Topic: The agile impact on innovation
Supervisor: Dr Pauline Parker
Email: p.parker@kingston.ac.uk
The focus on agility has become a major change in the way products are managed and innovation is implemented, particularly in a technology environment. The concepts of an agile organisation is to reduce waste and increase innovation, however the implantation to a new framework is often problematic.
This research aims to identify the key factors associated with the implementation of agile within an organisation and to assess the impact on innovation outputs.
Topic: Ordinary user innovation through the eyes of the video camera
Supervisors: Dr Evy Sakellariou and Professor Gaëlle Vallée-Tourangeau
Ordinary users are those users who possess limited or no technological knowledge of a specific product/service domain and are rather passive with respect to innovative activities, as they tend to be rather satisfied with existing products. However, empirical research has shown that ordinary users provide new product solutions that can contribute to the R&D efforts for creativity and innovation. Studies have shown that half of the ordinary user innovators report their solutions through traditional forms of market research or through digital forums. This is important because these solutions can provide value to the society when dispersed and sold in the marketplace. The other half of the user innovators do not report their ideas; as a result, the user-developed solutions remain hidden and unexplored by organisations or entrepreneurs.
This research explores the antecedents and outcomes for ordinary user innovation by employing video-based ethnographic narrative as a research method. This is an advanced ethnographic method employing new video technologies (such as mobile phone camera, wearable camera or traditional video camera) in contextual observation and interviewing with systematic data analysis.
Topic: Aggregation of scenarios in health technology assessment
Supervisors: Professor Giampiero Favato and Dr Andrea Marcelluci
The major objection to the use of scenario analysis in health technology assessment remains the lack of solid and reliable mathematical basis for the justification of the "expected" solution derived in this fashion. The aim of this study is to identify an intuitive but robust method to aggregate multiple scenario solutions in an overall solution that will occur inside a constraint setting.
The proposed method to aggregate multiple scenarios will be derived from the analysis of the fuzzy distribution of relevant scenarios. In essence, fuzzy distribution assigns by default a degree of possibility to the three scenarios representing the three limits of the value assessed: base case (fully possible), worst case and best case (virtually impossible). The aggregate value is the positive fuzzy mean of the three limits, given by the base case plus the relative distance between the worst and best case. If the distance between the worst case and the base case is higher than the distance between the best and the base case, the aggregate value will be lower than the base case. If the distance between the best and the base case is higher than the distance between the base case and the worst case, the aggregate value will be higher than the base case. The fuzzy distribution will not consider outcomes outside the worst case and the best-case scenarios, therefore the values included define the pay-off distribution, which is treated as a fuzzy set.
The novel method stemming from the proposed research would be useful to find the "expected" solution to scenario analysis in Health Technology Assessment. The fuzzy-distribution method does not defer the Chebyshev's inequality Law, since the "expected" value occurs inside a constraint setting of possible outcomes (the fuzzy set).
Topic: Big data analytics in healthcare: developing a framework for quantifying subjective and algorithmic bias
Supervisors: Dr Elena Fitkov-Norris, Professor Chris Hand, and Professor Giampiero Favato
Machine learning and advanced data analytic techniques have promising future in healthcare (Panagiota & Korina, 2021). The ability of machine learning and advanced analytical techniques to sift through large quantities of data and identify patterns and dependencies, also known as big data mining, can be utilised for the benefit of personalised healthcare (Ahamed & Farid, 2019), optimising HIV treatment protocols (Ridgway, Lee, Devlin, Kerman, & Mayampurath, 2021) and a range of other medical fields (Panagiota & Korina, 2021). However, the decisions and recommendations of machine learning techniques rely on past data which is used the train the algorithm. The quality of recommendations is affected by the quality of the training data, which in many cases has been shown to contain inherent biases (ElShawi, Sherif, Al-Mallah, & Sakr, 2020). In addition to any bias inherent in the data, recent research has shown that data mining techniques could introduce algorithmic bias which could further affect the quality of the recommendations (Starke, De Clercq, & Elger, 2021).
This project proposes to build a framework to identify and quantify both the subjective bias inherent in past data used for training and algorithmic bias introduced by the machine learning technique in order to minimise its impact on the recommendations and decisions of machine learning handling wide varieties of data (both structured and semi-structured) (Starke et al., 2021). The framework will be tested in the context of a machine learning application to mine electronic healthcare records. This is a promising avenue of research that could add a significant contribution to the academic debate on machine learning as well as impact significantly on the application of machine learning in the medical field (Prosperi et al., 2020) as well as in business and industry.
Topic: The role of CMOs under high levels of uncertainty
Supervisor: Dr Hamed Mehrabi
Many firms operate in environments characterised by high levels of uncertainty. Top management teams (TMTs) play the main role in shaping the direction of the firm under uncertainty. Among the members of a TMT, chief marketing officers (CMOs) can significantly influence strategic decision-making in uncertain environments because they possess critical knowledge resources about markets and customers that might not otherwise be available or visible to the firm's TMT when making decisions. The proposed research will employ qualitative research (such as in-depth interviews) to explore the role of CMOs in strategic decision-making in highly uncertain environments. This will involve an investigation of the interaction between CMOs and other members of the TMT. The model developed based on qualitative research could then be empirically tested using the survey method and by comparing firms that operate with and without a CMO.
Topic: Antecedents and consequences of marketing big data analytics capabilities
Supervisor: Dr Hamed Mehrabi
The topic of big data has received a lot of attention in recent years. Although the general topic of big data analytics capabilities has been investigated extensively, domain-specific big data analytics capabilities, such as those related to marketing, have received less attention. The proposed research will employ qualitative, quantitative or mixed method approaches to conceptualise and examine the antecedents and consequences of marketing related big data analytics capabilities. In particular, it will investigate the factors that facilitate (hinder) the development of marketing related big data analytics capabilities.
