Subjects for future PhD projects

Prospective students are encouraged to develop their own research ideas and must write their own research proposal (around 2 A4) as part of the application package.
However a fit with current research in the programme is a must and therefore the list below provides an overview of research lines that are currently pursued in the programma. By clicking on the title some background information is provided. Please note these are only examples and no vacancies.

On Experiencing Scarcity

Although consumers live in an age of plenty, the recent recession again demonstrated that wealth is not distributed equally across times or populations. For many people much of the time, having to deal with acute or chronic scarcity of valuable resources is a fact of life. But what does it mean psychologically to experience scarcity, and how do consumers cope with such a state? How do consumers experience an empty wallet, an empty stomach or an empty shelf in the supermarket? Despite its profound significance, surprisingly little research has systematically addressed this issue. In this project we will examine the impact of dwindling resources on consumer cognition, emotion, judgment and choice. We will focus both on acute and chronic experiences of scarcity, including financial resources, but also other limited commodities such as time or satiety.

The specific focus of the project will be determined in a collaboration between the PhD candidate and the supervising faculty. After synthesizing and integrating the (piecemeal) results of previous studies, we will develop a set of testable working hypotheses, to be assessed in a series of field and lab studies, using experimental, survey and/or archival research methods. The results of this project may point to new strategies of consumer education and empowerment.

Further reading:

• Shah, A.K., Mullainathan, S., & Shafir, E. (2012). Some consequences of having too little. Science, 338 (6107), 682-685.

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Customer Experience: Measurement, Drivers and Return

Delivering value to customers has become an important theme in the last years. Firms are constantly considering how they can improve the customer experiment in different customer touch points and channels. Delivering a superior customer experience is believed to be of utmost importance in today’s competitive market place.

Despite this growing attention for customer experience, there is a lack of knowledge on what customer experience actually is and how it can be measured (see also MSI research priorities). And how is it different from existing constructs, such as service quality or customer satisfaction. Interestingly, firms frequently use a single item measure, such as the Net Promoter Score or the Customer Effort Score, as a measure for customer experience. But do these measures really reflect the total customer experience.

Firms also struggle how to influence customer experience. And how they should gain knowledge on drivers. Traditional survey methods might not be so appropriate as they heavily suffer from methodological problems, such as common method variance. Big data developments may help, as intermediate input in experiences in different customer touch points and channels, can be measured and be linked to perceptual customer experience measures.

Finally, although customer experience is considered as very important, the returns on investing in it are not sufficiently clear. More specifically, firms would like to know in which aspects of the customer experience they should invest.

Hence, in this project we raise the following three main research questions:

1. Can we define customer experience and measure it?
2. Can we use a big data approach to understand the drivers of customer experience?
3. What are the returns on improving the customer experience.

We are likely to use large data sets on customer experience of customers of a very large service provider in The Netherlands.

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Grocery store: A sweet maze of seduction

The WHO estimates that by 2015, 3 billion people will be overweight, at least 700 million of them obese—a direct consequence of an increased consumption of energy-dense foods high in saturated fats and sugars (WHO). Despite the growing understanding of the negative consequences of eating energy-dense foods, consumers continue to purchase and eat these foods. This can in part be explained by the grocery store environment—a complex decision environment where shoppers make many purchase decisions while being seduced by mix of ambient, structural, social, and aesthetic cues. Instead of trying to reduce the consumption of energy-dense foods, it seems more effective to help consumers make healthier choices and purchase nutrition-rich instead of energy-dense foods.

How can consumers be stimulated to make healthier choices when shopping for groceries? Students interested in working on this project will empirically address this question, using a combination of field and lab studies.

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What's bugging us about insects and other innovative foods?

Scientists stress that we should include more healthy and environmentally-sustainable foods into our diets. This is however easier said than done, as people’s food preferences are deeply rooted in culture. Some particularly promising alternatives (e.g., edible insects) may be considered taboo, thereby hindering their wide-scale adoption.

Where do these food taboos originate, and how do they evolve? And, more importantly, how can they be overcome? Students interested in working on this project will empirically address this question, using a combination of field and lab studies.

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Big Insights from Big Data?

Today’s data-rich business environment offer companies the opportunity to identify the needs and preferences of their customers at an unprecedented detailed level. However, because of the overwhelming size of the data, the dazzling speed at which new data come in and the abundance of different data sources, companies find it difficult to seize these opportunities and translate them in to additional profits.

The questions they run into are: what types of data provide most value to the firm? When to stop adding new data sources? How quickly do data lose their value? How can data from different sources (e.g. structured and unstructured data) be combined efficiently? How to translate the results of data analyses into business insights?

Students interested in working on this project will address these questions mainly from an empirical point of view, in close cooperation with CIC member companies.

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‘One size fits all’ analytics and Big Data

One of the consequences of the Big Data development is that more and more companies are unable to store their customer data in local databases. Instead, the data are distributed over many data centers outside the company in e.g. Hadoop clusters. This has consequences for analyzing these data, because for most analyses it is assumed that the data reside in the computer that performs the necessary computations.

For some computations (e.g. additions) it is straightforward how to break them down in steps that are executed on the many computers where the data are stored, and to combine the outcomes efficiently in a meaningful overall outcome. This does unfortunately not hold for all traditional marketing research tools (e.g. clustering), and the question arises what adjustments are needed to accommodate these changes.

Students interested in working on this project will address this question mainly from a methodological point of view, but there will be ample opportunities to empirically test the newly developed approaches using data from CIC members.

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Frightful or fantastic? The use of social robots in services

With a 61% increase in sales of service robots for professional use in 2018, it is estimated that 85% of all customer interactions will occur without a human agent already by 2020. Service robots distinguish themselves from previous generations of technology and automation because they often go beyond a merely functional role and engage their users on a social level, coined as robots having “Automated Social Presence”. Yet, literature shows that service robots can be received with scepticism and trigger negative feelings and compensatory behaviors.

Given that the use of service robots is on the rise, and in some areas even seen as unavoidable (e.g. health and elderly care), this research aims to investigate whether the negative feelings that social service robots trigger can be attenuated by

• WHEN robots are used
• HOW robots are used
• WHICH tasks robots do (and what humans remain doing)
• HOW humans works together with robots

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Why do we buy more than we (can) eat? – Understanding food waste

Estimates are that worldwide a third of the food that is produced for human consumption is wasted. And it’s not just the food itself that is wasted – resources are expended to grow, cool, transport, and stockpile food. All that goes to waste if the food is thrown out instead of being eaten. The food sector aims to provide people with food anywhere, anytime. People themselves have a tendency to focus on having plenty of food in inventory at home, just in case. This ensures that people have food when they want it, but comes at considerable societal costs due to inefficient resource use. This research line aims not only to uncover why consumers buy more than they (can) eat, but also develop a roadmap towards a food system with minimal food waste.  

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