This summary of the Advanced Research Methods course encompasses a wide range of important terms and concepts. It covers exploratory research, theory building, and theory testing, as well as decision science research. Both qualitative and quantitative research are addressed, with empirical data pla...
Exploratory/descriptive research: Investigates new or unclear problems, focusing on understanding
and describing phenomena. Investigates new or ill-defined problems. Generates descriptive
knowledge → Knowledge question (example): ‘What are …’.
- Management problem: Problem is new or not yet well-defined.
- Review of evidence: Academic literature on new subjects is usually scarce, some relevant
theories from other topics can be borrowed.
- Research design/Scientific Reasoning/Logic: Inductive logic (or abductive if subject is not
totally new)
- Possible strategies: case study, secondary data analysis, focus groups, interview, etc., or a
combination of those (multi-method studies)
- Data collection/types: Likely qualitative data, but quantitative also an option (for example in a
survey).
- Data analysis: Depending on data type, both quantitative and qualitative approaches.
- Research outcomes: A better description of what i.e. ‘procurement digitization’ refers to, or
which benefits/risks it brings, or how other companies approach its implementation.
- Recommendations: Exploratory studies cannot provide strong advice, but they can assist in
understanding the problem more clearly and develop the next steps in acting on it.
Theory-building research: Formulates theoretical propositions to explain phenomena. Aims to
understand relationships between phenomena. Generates explanatory knowledge → Knowledge
question (example): ‘How…?’.
- Management problem: Problem is new but clear. Understanding of main components and
relationships is required.
- Review of evidence: Academic literature is restricted, potentially related to other empirical
contexts. Literature is useful for comprehending possible important concepts; useful theories
from other fields can also be used. Business literature likely to be available and helpful.
- Research design/Scientific Reasoning/Logic: Inductive logic (or abductive for theory
refinement).
- Possible strategies: case study, focus groups, secondary data analysis, interview or
combination of those (multi-method studies).
- Data collection/types: Qualitative data is likely, but quantitative data also possible (for theory
refinement).
- Data analysis: Depending on the type of data, both qualitative and quantitative approaches.
- Research outcomes: Assumptions (theoretical propositions) about how i.e. procurement
digitization is linked to several practices in SRM, generalizability is always a potential issue.
- Recommendations: It cannot give strong advice (as propositions have not been tested), but
may be used as an attempt to explain how something works; recommendations can be very
contextual.
,Theory-testing research: Test hypotheses derived from theories. Seeks to validate or refute existing
theories. Generates predictive knowledge → Knowledge question (example)
A hypothesis can be formulated: ‘digitization of purchase order processing will increase buyer
productivity.’
- Management problem: There is theoretical knowledge (assumptions) about the topic, but not
verified/proved yet.
- Review of evidence: Academic literature on the subject is present, theory is formulated and
maybe even in some aspects tested, yet there is a lack of evidence of relationships that form
the subject of this specific research.
- Research design/Scientific Reasoning/Logic: Deductive logic.
- Possible strategies: surveys, experiments; it is unlikely that sufficient secondary data exists on
this example, but in general may be available.
- Data collection/types: Quantitative data
- Data analysis: Quantitative (statistical) analysis
- Research outcomes: Proof (or rejection) of i.e. positive effect of digital technologies on buyer
productivity.
- Recommendations: Recommendations likely to be ‘convincing’, if theory is tested and true:
also perceived as generalizable. Can be used as proof for making managerial decisions (but
sensitive to its own context).
Decision science research: Develops tools and models to support decision-making. Focuses on
practical problem-solving through models and algorithms. Generates prescriptive knowledge →
Knowledge question (example): ‘Forecast prices for raw materials for adequate budgeting.’
- Management problem: Topics can be already studied generally, but represent a particular
management problem for a company.
- Review of evidence: Literature on cost models and market price dynamics for raw materials.
- Research design/Scientific Reasoning/Logic: Inductive logic.
- Possible strategies: development of mathematical models (cost models).
- Data collection/types: Quantitative data; likely market data and historical purchasing data +
simulation data.
- Data analysis: Data is used to train, test and improve models.
- Research outcomes: Mathematical tools (models) for raw material forecasting; pricing
scenarios.
- Recommendations: Decision-support tools with recommendations on their use provided.
Likely to be highly contextual (if constructed with company information) and thus relevant for
the firm; for use in other contexts the model may have to be changed (thus include different
parameters).
Four types of knowledge questions:
Descriptive knowledge: how things are
Explanatory knowledge: why things are that way
Predictive knowledge: how things will be
Prescriptive knowledge: how things should be done
(Evaluative knowledge): how successful something is
Types of claims
In exploratory (descriptive) studies: how large, how frequent, how important certain things are;
In theory-building studies: how concepts relate to each other, and through which mechanisms;
In theory-testing studies: estimates of certain parameters (e.g., regression coefficients) and whether
a hypothesis needs to be rejected, or not;
In decision science studies: what the optimal decision or system design is.
,Examples of research objectives (or questions)
One area that remains under-explored in the literature is the resource-based view of strategic
alliances, even though such alliances are rapidly increasing in importance in today’s competitive
landscape [...]. Our purpose here is to develop a more encompassing resource-based explanation of
strategic alliances than is currently available in the extant literature
Theory-building (development of explanation of how strategic alliances ‘function’ –
resource-based theory point of view)
Most existing classifications of business services have taken the perspective of the supplier
organizations. To address this imbalance, the purpose of this paper is to propose a classification of
business services based on how the buyer applies the service with respect to its own business
processes.
Exploratory (classification is a systematic description)
The main contribution of this paper consists of proposing an integer linear programming (ILP)
formulation for the timetable rescheduling problem to deal with large-scale disruptions on a real-world
railway network.
Decision science (optimization of schedule)
Complex contracting is an important mode of formal governance [...]. Formal contracts [...] are often
enmeshed within the context of an ongoing business relationship. Drawing on this insight, several
management scholars have begun to study how the extent to which relevant clauses are specified in
contracts—depends on the history of contracting parties working together[...] . Our contribution in this
paper is to analyse the conditions under which the learning effect is likely to be stronger or weaker by
predicting differences in the marginal effects of prior interactions on contractual detail..
Theory-testing (the intention is to ‘quantify’ (= bepalen) the learning effect related to different
contract conditions)
Consider the following management problem:
A supermarket chain has identified the following business problem: the profitability of the dairy
products category (milk, yoghurt, etc.) has been declining in the past year. Some products sell well,
and these are not just the cheapest products. Other products do not seem to be popular with
customers, despite periodical promotion campaigns. It also seems that changing the positions of some
products on the shelves in the supermarket leads to changes in the sales volumes of these products,
and also affects sales of other similar items.
The question that the supermarket chain is facing is: Which dairy products should be kept in the
assortment to improve the overall dairy category profitability?
In sum, the problem is complex as the demand for a dairy product is not fixed; it can be affected by the
presence of certain alternatives/substitutes, by price, by promotion policies, and by shelf space and
positioning.
Question: Describe a research type which, in your mind, is suitable to study this managerial
Problem
Decision science is the most suitable research type needed as it is a very complex and
contextual problem; so mathematical modelling is required.
, Read (parts of) an academic article introduction section below and identify the type of research:
Both scholars and practitioners agree that managers and employees have a key role in the successful
implementation of continuous improvement (CI) initiatives in organisations, but they have contradicting
perspectives on how to manage their involvement. Scholars often emphasise the need for employees’
participation through organisational mechanisms such as teamwork and suggestion schemes, implying
an active involvement of employees in the improvement process and a bottom-up management
approach. By contrast, practitioners often point to the central role of managers with high centralisation
of authority, advocating the use of a top-down management with employees as the executors of
orders.
The aim of this research is to bridge the gap between the two perspectives. We argue that they may
be reconciled if we consider that CI is a dynamic phenomenon. In other words, we need to explore
whether and to what extent employees’ participation and managers’ centralisation of authority are
relevant to CI that improves operational performance. We suggest that the CI–employee participation
fit enhances operational performance. This implies that CI and employee participation should be
progressively implemented together such that when CI is low, there is a scarce need to make
employees participate in decision-making processes, whereas as when CI improves, employees
become extensively involved in decision-making processes. [...] Based on a database of 330 firms
across fifteen countries, we analyse the effects of organisational fit between CI and employee
participation on operational performance..
1. There is already knowledge/theory/research
2. Lots of data points + Theory-testing (no decision science → no intention to find optimal
solution to a problem)
Identify type of research in this project:
The decision about where to locate a manufacturing facility – in which specific country – is very
important for any firm. Many cost drivers - labor availability and wages, transportation, customs tariffs,
etc. - can drive the location choice.
The recent phenomenon of reshoring – when Western firms bring manufacturing back home in
high-cost countries, - indicates that firms started looking beyond cost factors in this strategic decision,
and now consider additional variables, for instance, proximity of research and development
facilities. However, which additional factors exactly need to be taken into account when making the
reshoring decision may still be a puzzle for managers.
Decision-making about manufacturing facility locations is overall a well-studied problem: an extensive
literature on the geography of economic activities spans several decades and multiple disciplines. Still,
our theoretical understanding of why reshoring occurs is so far limited. In this paper we intend to find
out why production is moved back to high-cost environments and what factors drive the reshoring
decisions.
The empirical material for this paper comes from an in-depth study of 35 different manufacturing firms
across multiple industries. The data were collected in 2017-2019. Despite the unusually large number
of cases and the theory-elaborating emphasis, the logic of the data collection resembles that of a
multiple case study (Eisenhardt, 1989).
1. There is already knwoledge/theory/well developed
2. Limited → Theoretical understanding not yet fully there
3. Case study → Associated with theory-building (not decision science or theory testing)
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