Research & Skills for BA
#1 – Student Projects
There are two research paradigms to be used in student field work in Business or Management,
being the explanatory paradigm and the design science paradigm. The explanatory paradigm is
mostly used in social sciences, whereas the design science paradigm is used in medical/engineering
schools. A project following the explanatory approach (gaining knowledge) aims to produce
descriptive and explanatory knowledge, following the empirical cycle. On the other hand, the design
science paradigm (finding a solution) aims to produce solutions to field problems and can follow the
problem-solving cycle. In a master studies, both approaches can be combined.
The empirical and problem-solving cycle have some things in common, but may also be very different
in some aspects. The empirical cycle is observation -> induction (developing theory) -> deduction
(generating hypotheses) -> testing -> evaluation. Observation concerns phenomena in the real world
and what is written about them and may result in the conclusion that more companies deal with an
issue, but the issue is not worked out in the literature yet. During the induction step, possible
explanation are developed for the issue and is thus a theory-developing step. At the deduction step,
the most promising ideas of the induction step are transformed into hypotheses, which are tested in
the testing step through statistics or qualitative approaches. Finally, the evaluation step s used to
examine and interpret the outcomes of the testing and may lead to new research question or a
restart of the cycle. The empirical cycle can also be used for non-academic reasons, for example to
come to know needs of customers.
The problem-solving cycle is driven by a business problem of a company, chosen by stakeholders
(problem mess). To formulate the problem, the mess has to be identified and structured, resulting in
a problem definition. The next step is analysis and diagnosis, being the analysis of the context and
the causes of the problems, after which a solution is designed. Following this, the intervention step is
where the solution is implemented after which the solution is evaluated and assessed, which may
lead to new business problems.
There are two knowledge generating processes that can be used for descriptive or explanatory
theory:
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Theory development. This process is based on the first part of the empirical cycle and based
on the work of Eisenhardt. This starts with a business phenomenon, the observation of that
phenomenon, a development of the explanation while comparing findings with theories, and
deriving propositions that are changes of or additions to existing theories. This is especially
needed when the phenomenon has not been addressed in the literature before or when it is
very exploratory in nature.
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Theory testing: This process is based on the second part of the empirical cycle, following the
deduction, testing, and evaluation steps, eventually resulting in a newly tested part of a
theory. It starts with a business phenomenon, after which important variables are identified
and a model is build. Following this, large scale data collection may find results through
statistical analysis, followed by implications and future research. Here, literature is already
less scarce.
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Academic problem solving: This is based on the problem-solving cycle, though addressing a
generic type of business problem. The main focus is on selecting the company’s business
problem, analyzing and diagnosing, data collection and analysis while cross-checking with
literature, and finding a solution and eventually, possibly a pilot implementation.
It is dangerous to mix the research processes and to assume that a mixture of two logics is
consistent. Mixing theory development with theory-testing is wrong for two reasons. First, it is not
valid to develop and test theory on the basis of the same data. Second, a test on the basis of a few
cases is not a large scale test that is meant in the theory-testing process. So it is very difficult to come
to generalizations in this way.
All in all, as a master student, choose one of the three process, select it based on the research
question and business phenomenon you want to study, and do not mix different types of knowledge
producing processes.
#2 – Quality Criteria for Research
If a product does not meet its quality criteria, it loses much of its value. Quality criteria can be used in
two ways, for managing the quality of one’s own research, but also to evaluate the research done by
others. Furthermore, quality criteria can be used by the principal of the steering committee to asses
the problem-solving project that is done for them. Problem-solving project can be evaluated on
several criteria in which there is a distinction between research oriented criteria and change oriented
criteria. Research oriented criteria concern the research aspects of problem-solving projects, they
concern the statements on the business system researched and its context. These are particularly
relevant for diagnosis and evaluation, since these comprise most research activities. Change-oriented
criteria are more relevant during problem definition, redesign and implementation.
There is debate on whether you can ever yield true conclusions. Many authors acknowledge that the
central aim of research is to strive after inter-subjective agreement, referring to consensus between
the actors.
The most important research-oriented quality criteria are controllability, reliability, and validity,
which provide the basis for inter-subjective agreement:
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Controllability: this functions as prerequisite for testing reliability and validity. Researchers
have to reveal how they executed a study. This enables others to replicate it and check
whether they get the same outcomes, which is also the threshold for this criteria. In addition,
controllability also requires that results are presented as precisely as possible.
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Reliability: The results of a study are reliable when they are independent of the particular
characteristics of that study and can therefore be replicated in other studies. There are four
sources of possible bias; the researcher, the instrument, the respondents, and the situation.
Thus, results should be independent of the these sources. A replication of the study by
another researcher, with different instruments, different respondents and in another
situation, should yield similar results. As a result, a common strategy for determining the
reliability is to repeat the study. Repetition also gives the opportunity to increase reliability as
measurements can be combined.
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Researchers and reliability: Results are more reliable when they are independent of
the person who has conducted the study. Goldman distinguished between hot and
cold biases. Hot biases refer to the influence of interests, motivation, and emotions
of researchers on their results. Cold biases refer to subjective influences of the
researchers that have a cognitive origin (e.g., self-fulfilling prophecy). Some
instruments level more room for biases than others, such as interviews. Having
somebody else conducting a study may work as assessment. When objects or events
are coded, it is common to have two people doing it, in which the reliability can be
assessed by determining the correlation between the codes, also called the interrater-reliability. Another option is standardization, being the development and use of
explicit procedures for data collection, analysis, and interpretation (e.g., structured
interviews or a case-study protocol).
Instruments and reliability: multiple techniques may be available for studying the
same phenomenon, though outcomes may differ. If one gets different results, these
may be both complementary and contradictory. Overall, using multiple research
increases reliability through the concept of triangulation. Triangulation is the
combination of multiple sources of evidence, such as interviews, documents,
archives, observations, and surveys and can remedy shortcomings and biases of
instruments by complementing or correcting each other. In interviews or surveys,
reliability can be achieved by asking a similar question with different wordings. The
statistical correlation (Cronbach Aplha) shows whether the scales used were reliable.
Respondents and reliability: People within a company can have widely diverging
opinions. When the opinion of interviewees serves as a source of information, you
want the information to correspond to reality or at least to get an inter-subjective
view by combining perspectives. Important here are samples and populations. Three
principles should be followed to counter respondent unreliability. (1) All roles,
departments, and groups need to be represented among the respondents, (2) in a
large groups, respondents need to be selected at random, and (3) the general
strategy to increase reliability is to increase the number of respondents.
Circumstances and reliability: Timing of the study may influence results (e.g., cranky
in the morning). Hence, a study should be carried out at different moments in time.
Validity is the third major criterion; a research result is valid when it is justified by the way it
is generated. The way it is generated should provide good reasons to believe that the
research result is true or adequate. Thus, validity refers to the relationship between a
research result or conclusion and the way it has been generated. Validity presupposed
reliability, as an unreliable measurement limits our reasons to believe that the result are
true. There are three separate types of validity:
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Construct validity: the extent to which a measuring instrument measures what it is
intended to measure. The concept should be covered completely, and the
measurement should have no components that do not fit the meaning of the
concept. It can be assessed in multiple ways: (1) evaluating measuring instruments
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and collecting techniques by yourself, (2) ask experts to evaluate the instruments,
and (3) determine the correlation of their operationalization with measurements of
other concepts with which it should correlate. Sometimes, triangulation can be
valuable as it helps covering all aspects. Furthermore, construct validity and reliability
sometimes pose contradictory demands as some instruments are more reliable, but
have a lower construct validity.
Internal validity: this concept concerns conclusions about the relationship between
phenomena. These results are internally valid when conclusions about relationships
are justified and complete. In order to so, researchers have to make sure that there
are no plausible competing explanations. Thus, a conclusion is internally valid when
such alternative explanations are ruled out. In the problem-solving cycle, internal
validity is high when most of the actual causes of the business problem are found.
Studying the problem area from multiple perspectives, theoretical triangulation, can
facilitate the discovery of all causes. Finally, internal validity can be improved by
systematic analysis.
External validity: this refers to the generalizations or transfer of research results and
conclusions to other people, organizations, countries, and situations and is especially
important in theory-oriented research. It is less important in problem-solving
projects, since these projects focus on one specific problem. However, when the
study is a pilot study, it is necessary to assess whether the result can also be
expected in other departments or organizations. External validity can be increased by
increasing the number of objects studied, selected at random or on theoretical
grounds.
The final criterion concerns the recognition of results, referring to the degree to which he
principal, the problem owner and other organization members recognize research result in
problem-solving projects. If they cannot make sense of the results, it is hard to reach intersubjective agreement. The recognition of results can be assessed by presenting
(intermediate) results to those involves, also called a member check. In addition, using
narratives may help you in understanding the insider.
All in all, it is important to notice that a positive assessment of reliability and validity is not a
guarantee for truth or adequacy as some important things may still be overlooked. In addition, not all
scientists consider the criteria mentioned in this chapter to be very important . Finally, alternative
criteria that are mentioned are fairness, referring to the degree to which the perspectives and values
of different parties are acknowledged, and educational authenticity, being the degree to which
research contributes to the mutual understanding people have of each others world.
#3 – A Multi-Dimensional Framework of Organizational Innovation: A Systematic Review of the
Literature
According to management scholars, innovation capability is the most important determinant of firm
performance. Though tens of thousands articles are written, review and meta-analyses are rare and
narrowly focused, either around the level of analysis or the type of innovation. This resulting
fragmentation of the field prevents seeing the relations between facets. The goal fo this article is to
bring together all part by consolidating research, establishing connections, and identifying gaps
between different research streams.
There are many different definitions of innovation, which the authors combined into a broad new
one. Due to the wide range of meanings, the authors intentionally cast the net wide in order to
understand all definitional nuances. The initial step was a review and categorization of the findings,
which were synthesized into a comprehensive multi-dimensional framework of organizational
innovation.
On the methodology, an analytical review scheme was necessary for systematically evaluating the
contribution of a given body of literature. A systematic review uses an explicit algorithm, as opposed
to a heuristic, to perform a search and critical appraisal of the literature. They improve the quality of
the review process and outcome by employing a transparent and reproducible procedure (quality
criteria!). This was important to overcome the challenges of synthesizing data from different
disciplines. The review process comprised three parts:
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Data collection: As opposed to employing subjective collection methodologies, this paper –
using the systematic review approach – removed the subjectivity of data collection by using a
predefined selection algorithm.
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Data analysis: The goal was to provide a conceptual, rather than an empirical, consolidation.
As a result, the authors were methodologically limited to descriptive rather than statistical
methods (depth for breadth). As the data was qualitative, pattern-matching and explanation
building were selected for the review.
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Data synthesis: this is the primary value-added product of a review as it produces new
knowledge based on thorough data collection and analysis. The ten dimensions that were
found were mapped onto a framework consisting of the two sequential components:
innovation as a process (how?), and innovation as an outcome (what?). Additionally, the
authors compiled the determinants of organizational innovation and their associated
measures, and organized them around three theoretical lenses found in organizational
research.
All in all, the authors use a systematic review, with as main aim a conceptual consolidation across a
fragmented field using systematic data collection procedures, descriptive and qualitative data
analysis techniques, and theoretically grounded synthesis.
The authors following a three-stage procedure: planning (defining objectives and identifying data
sources), execution ((1) identifying selection criteria –keywords and search terms, (2) grouping
publications, (3) compiling a consideration set into reviews and meta-analyses, highly cited papers,
and recent papers and was done by two panelists (4) classification and typology of the results, and
(5) synthesis), and reporting. They choose to limit their sources to peer-reviewed journals (SSCI) as
these can be considered validated knowledge .
The analysis proceeded in three steps. First, they reviewed the spectrum of theoretical lenses used in
group 2 (highly cited papers), organized them by level of analysis, and summarized the findings.
The authors used a framework whereby a set of determinants lead to their phenomenon of interest.
The authors call for a practice-based view, which can combine the individual, firm, contextual, and
process variables prevalent in the literature. PBC considers the activities that organizational actors
conduct (micro level), their consequences for organizational outcomes (macro level), and the
feedback loop from contextual and organization variables back to the actors.
Three elements of innovation can be isolated: practice (e.g. shared routines), praxis (theories in use,
and practitioners. Much of the research on innovation fits in the category of practice. It is at this
micro level that the managerial reality enfolds every day, therefore a theory of innovation needs to
connect the action (praxis) with the managerial and academic theories (practice) by understanding
the role of the agents (practitioners) (future research)
Limitations: (1) focus was to integrate prior research, so authors did not offer detailed propositions
linking the elements, (2) only one database of record, (3) the filtering process may have omitted
relevant research, (4) a complicated framework like this one may highlight previously neglected
connections, while failing to capture others.
#4 – Success factors in new ventures: a meta-analysis
New technology ventures (NTV) can have positive effects on employment and could rejuvenate
industries with disruptive technologies. However, they have a limited survival rate. Thus, it is
important to examine how new technology ventures can survive better. Current academic literature
does not offer much insight, with numerous studies focus on success factors for new technology
ventures, but the empirical results are often controversial and fragmented. The inconsistent and
often contradictory results can stem from methodological problems, different study design, different
measurements, omitted variables in the regression models, and noncomparable samples. Hence,
this study provides a solution through a meta-analysis.
Meta-analysis is a statistical research integration technique with a quantitative character. They
require statistical techniques specifically designed to integrate the results of a set of primary
empirical studies. This allows meta-analysis to pool all existing literature on a topic and not only the
most influential ones. At the same time, meta-analysis compensates for quality differences by
correcting for different artifacts and sample sizes. There are two main types of meta-analytic studies
in the literature. The first focuses on relationships between two variables or a change in one variable
across different groups of respondents. In general, this type of analysis is strongly guided by one or
two theories. The second type examines a large number of metafactors related to one particular
focal construct and are largely atheoretical because the research they combine rests on
heterogeneous theoretical grounds. Because this paper focuses on numerous theoretical streams
where only the setting (new firms) is the common denominator, the decision was made to focus on
the second type of meta-analysis.
The first step is to select studies as input for the analysis. The literature was combed for research
that discussed the success factors of NTVs, using the ABI-INFORM system and the internet, based on
a group of key words. After articles were gathered, cross-referenced studies were added from them.
Next, an effort was made to ensure that the articles on the list represented the correct level of
analysis, significantly reflected NTVs, and reported a correlation matrix with at least one antecedent
of performance and one performance measure. When coding the studies, care was taken to refer to
the scales reported in the primary studies so that dissimilar elements would not be combined
inappropriately and so that conceptually similar variables would not be coded separately to
compensate for the slightly different labels that authors use to refer to similar constructs.
The most important consideration for the meta-analysis was to the ability to make comparisons
across research studies. To do this, the research could draw on Pearson correlations between a
metafactor and the dependent variable or the regression coefficient between the metafactor and the
dependent variable. This is necessary because correlations between two variables are independent
of the other variables in the model. Furthermore, using this method has the advantage of using
random effects models instead of fixed effects model. Fixed effects models assume that exactly the
same true correlation value between metafactor and dependent variable underlies all studies in the
meta-analysis, whereas random effects models allow for the possibility that population parameters
vary from study to study. Given the range of definitions for NTVs, the choice for random effects was
appropriate.
The second step is to correct metafactors for dichotomization, sample size differences, and
measurement errors. First, to correct dichotomized metafactors, a conservative correction was made
by dividing the observed correlation coefficient of the sample by 0,8, because dichotomization
reduces the real correlation coefficient by at least 0,8. Furthermore, to correct sampling error, the
sample correlation was weighted by sample size. Finally, to remedy measurement errors, Cronbach’s
alphas were used. The correlation coefficient was divided by the products of the square root of the
reliability of the metafactor and the square root of the reliability of performance.
The third step in the meta-analysis protocol was to determine whether a metafactor was a success
factor. To accomplish this, three conditions were assessed. First, the study should have, in essence,
the same correlation which can be checked through a chi-square test on homogeneity. However, this
seems to have a bias because of uncorrected artifacts. For homogenous metafactors, two
significance test were applied. First, it was determined whether whole confidence interval was above
zero. Second, if it was, the p-value was calculated for the real correlation to estimate the degree of
significance. Only when all three conditions held was a given metafactor considered to be a success
metafactor for NTVs. For those heterogeneous metafactors, a moderator analysis was conducted
were the date was divided into subgroups according to methodological characteristics. A separate
meta-analysis was conducted for each subgroup. Finally, the ‘file drawer’ was reviewed in an attempt
to assess publication bias, resulting from a general tendency to publish only significant results.
The analysis revealed 24 metafactors, that were put into categories grounded in the literature in
order to make it more transparent and interpretable. Two researchers independently assigned each
variable to a category.
The explained variance must be more than 75 percent to yield a homogenous factor. In that case, the
real variance is less than 245 percent of the total variance of correlations from the primary studies.
The remaining variance is likely due to other unknown and uncorrected artifacts, and therefore it can
be neglected.
The study’s high-quality scale is either a ratio-interval measure or a Likert-type scale with a
Cronbach’s alpha of at least 0,7 that consists of at least three items. The last condition ensures that
liker-type scales will be reliable and that they will still hold a certain reserve for future studies.
An essential implication for future regression studies is that when results are contradicting or
insignificant it may be due to the study’s heterogeneous factors. In that case, a detailed study to
significant differences in correlation coefficients for various subsamples between factor and
dependent variable may explain the deviating results.
The study’s findings suggest that acquiring more experience in marketing and industry may lead to
higher NTV performance. Remarkable was that R&D investments were not a success factor.
Limitations were that the pearson correlations used for the study are primarily intended for
measurement of the strength of a linear relationship between two variables. Furthermore, the
primary studies based their samples on surviving NTVs because of the difficulties in assessing failed
NTVs. Therefore, any study in this area must be biased with two implications: meta-factors that
influence the success and mortality of a NTV could conceivably be substantially different, and
strategies that seem to deliver the best performance can be misleading. Finally, the 31 studies used
here may have been sufficient for a pre-mate analysis, but may not be enough for the real deal.
#5 – Organizational theories: Some Criteria for Evaluation
A theory is a statement of relations among concepts within a set of boundary assumptions and
constraints. The function of a theory is to prevent the observer from being dazzled by the complexity
o natural or concrete events. The purpose is twofold: to organize (parsimoniously) and to
communicate (clear).
What theory is not… Description, the features or qualities of individual things, acts or events must be
distinguished from theory. The vocabulary of science has two basic functions: to describe the objects
and events being investigated and to establish theories by which events and objects can be explained
and predicted. Specifically, three modes of description must be distinguished from theory:
categorization of raw data, typologies, and metaphors. Typologies can be seen as a mental construct
formed by the synthesis of many diffuse individual phenomena which are arranged into a unified
analytical construct. Metaphors are statements that maintain that two phenomena are isomorphic.
The metaphor is used to ask how the phenomenon is related to another one.
Researchers can define a theory as a statement of relationships between units observed or
approximated in the empirical world. Approximated units mean constructs, which by their very
nature cannot be observed directly. Observed units mean variables, which are operationalized
empirically by measurement. The primary objective of a theory is to answer the questions of how,
when, and why, unlike the goal of description, which is to answer the question of what. Thus, a
theory may be viewed as a system of constructs and variables in which the constructs are related to
each other by propositions and the variables are related to each other by hypotheses.
The system is bounded by the theorist’s assumptions. They set the limitations in applying the theory.
These assumptions include the implicit values of the theorist (values) and the often explicit
restrictions regarding space and time. Theories cannot be compared on the basis of their underlying
values, as these tend to be idiosyncratic products of the theorist’s creative imagination and
experience. These can never be eliminated and can be revealed by studying psychoanalytic,
historical, and ideological works of the theories. Spatial and temporal assumptions are often
relatively apparent. Spatial boundaries are conditions restricting the use of the theory to specific
units of analysis. Temporal contingencies specify the historical applicability of a theoretical system.
Taken together, spatial boundaries and temporal contingencies limit the generalizability of the
theory. However, some are more bounded by this than others. There is a continuum ranging from
empirical generalization (rich in detail, bounded by space and time) to grand theoretical statements
(abstract, but unbound).
Propositions state the relationships among constructs, whereas hypothesis that are derived from the
propositions specify the relations among variables. Hence, specific terminology is required by
researchers.
No evaluation of a theory is possible unless there are broad criteria by which it is evaluated. The two
primary criteria upon which any theory may be evaluated are falsifiability and utility. Falsifiability
determines whether a theory is constructed such that empirical refutation is possible. Utility refers to
the usefulness of theoretical constructs and is the bridge that connects theory and research. At the
core of this are explanation and prediction. An explanation establishes the substantive meaning of
constructs, variables, and their linkages, while a prediction tests that substantive meaning by
comparing it to empirical evidence. .
Falsifiability
Utility
Measurement Issues
Variable Scope
Construct validity
Construct scope
Logical Adequacy
Explanatory Potential
Empirical Adequacy
Predictive Adequacy
Measurement issues: In order for a variable to be operationally specific, that variable must be
defined in terms of its measurement. Furthermore, for a theory to be falsifiable, these
operationalized variables must be coherent. That is, they must pass the tests of being a good
measurement model: validity, noncontinuousness, and reliability. Unless the theorist’s hypotheses
incorporate variables which can be meaningfully and correctly measured, any variance that may exist
in the object of analysis is essentially unobservable. If the antecedent is a sufficient condition for the
consequent, a continuous consequent would make testing impossible. Thus, the theorists is
demanded to specify the time and space parameters in the hypothesis so that the constructs may be
meaningfully measured.
Variables
Constructs
Relationships
Construct validity: it may be useful to define constructs in terms of other established and wellunderstood constructs. When combined, the indicators of construct and variable falsifiability are no
less than the criteria for construct validity itself. To achieve construct validity, at least the responses
from alternative measurements of the same construct must share variance (convergent validity),
while the identified objects of analysis must not share attributes and must be empirically
distinguishable from one another (discriminant validity). In determining convergent validity, the
theorist must confirm that evidence from different sources gathered in different ways all indiciate
the same or similar meaning of the construct. In determining discriminant validity, the theorist must
confirm that one can empirically differentiate the construct from other constructs that may be
wsimilar, and the one can point out what is unrelated to the construct. In cases of high collinearity, it
is impossible to talk of their independent effects. The key to meeting the falsifiability criterion for
constructs thus lies in showing that variables which should be derived from the constructs are indeed
correlated with the construct, regardless of the procedure used to test correlation, and those
variables which should be unrelated to the construct are indeed uncorrelated.
Logical and Emperical Adequacy: When evaluating the falsifiability of the relational properties of
theoretical systems, theorists must examine both the logical adequacy of the propositions and
hypotheses as well as their empirical adequacy (the capacity of the relationships implied in
propositions and hypotheses to be operationalized). Logical adequacy may be defined as the implicit
or explicit logic embedded in the hypotheses and proposition s which ensures that they are capable
of being disconfirmed. In this context, propositions and criteria must be nontautological and the
nature of the relationships between antecedent and consequent must be specified. For a proposition
or hypothesis to be falsifiable, the antecedent and the consequent may not be epiphenomenal. That
is, the sheer existence of the antecedent may not automatically imply the existence of the
consequent. However, there are also examples in which a tautological proposition is self-verifying
and not subject to disconfirmation. The theorist must incorporate in propositions and hypotheses
and explicit statement of whether the antecedent is a necessary and/or sufficient condition for the
consequent. By failing to explicitly specify the nature of these logical links, organizational theorists
make it impossible for their theories to ever be disproved. Finally, empirical adequacy is one in which
the propositions and hypotheses may be operationalized in such a manner as to render the theory
subject to disconfirmation. There either must be more than one object of analysis or that object must
exist at more than one point time. In addition, empirical adequacy at the relational level cannot be
achieved if the variable does not meet standards of a good measurement model.
Variable scope: For adequate scope, the variables included in the theoretical system must
sufficiently, although parsimoniously, tap the domain of the construct in question, while the
construct must, in turn, sufficiently, although parsimoniously, tap the domain of the phenomenon in
question. The goal must be the achievement of a balance between scope and parsimony.
Explanatory potential and predictive adequacy: Theorists must examine both the substantive as well
as probabilistic elements of propositions and hypotheses. The explanatory potential of theories can
be compared on the basis of (a) the specificity of their assumptions regarding objects of analysis, (b)
the specificity of their assumptions regarding determinative relations between antecedent and
consequent, and (c) the scope and parsimony of their propositions.
How will theorists decide which theories to include? To answer this question, theorists need a clear
understanding of how a given theory fits in with the other preexisting and apparently related
theories. Two dimensions fit this: connective and transformational. Connectivity refers to the ability
of a new theory to bridge the gap between two or more different theories, thus explaining
something between the domains of previous theories. The theory is said to be transformational if it
causes preexisting theories to be reevaluated in a new light.
#6 – Does Strategic Planning Enhance or Impede Innovation and Firm Performance.
Many scholars have debated whether strategic planning enhances or impedes the generation of NPD
projects (new product developments). The traditional view claims that strategic planning promotes a
careful review of the different options in various business environments and therefore increase the
number of NPD projects and enhances firm performance. In contrast, some scholars indicate that
improvisation, or an experiemental approach may better increase the number. The purpose of this
study is to provide empirical evidence on the debated role of strategic planning by answering the two
questions: does strategic planning increase or decrease the number of NPD projects? And if so, how
can a firm manage controllable organizational factors to mitigate the adverse effect of strategic
planning.
The article continues by providing a background on strategic planning and the possible influence it
has on NPD projects as identified in the literature. Despite the abundance of literature they found on
the importance of either strategic planning or generating new NPD projects in strategies, the debate
continues about whether and how strategic planning influences the generation of NPD project ideas,
largely because of the limited availability of empirical research.
To address this unresolved issue, the authors propose a contingency framework that not only
explains their direct relationship, but also shows how organizational factors identified moderate the
relationships. The authors select variables that best represent resource-advantage theory to test if
they facilitate or impede the relationship. In order to provide a more comprehensive view of the
firm, the model also examines the direct relationship between strategic planning and firm
performance as well as the moderating effects of resource-advantage variables on this relationship,
and also include product innovativeness and three environmental variables as control variables that
are generally believed to influence outcomes of NPD activities.
The authors use a competing hypothesis approach that compares two plausible alternative
hypotheses to examine the debatable link. This approach is designed to elicit more objective and
reasonable explanations for competitng perspective and enhances objectivity. It generalizes the
findings by evaluating the pros and cons of the different views. This approach leads to
HI: Strategic planning decreases the number of NPD projects.
H2: The effect of strategic planning on the number of NPD projects is stronger in larger firms
than in smaller firms (Firm size: number of employees)
H3: The effect of strategic planning on the number of NPD projects is stronger in firms with
high R&D intensity than firms with low R&D intensity (R&D intensity: R&D expenditures as %
of revenues)
H4: The effect of strategic planning on the number of NPD projects is stronger in firms with
high organizational redundancy than in firms with low organizational redundancy
(redundancy: overlap of skills and resources, information, activities, and management
responsibility)
H5: Strategic planning enhances firm performance
Four control variables are added: product innovativeness, market turbulence, market growth, and
technological turbulence.
All study measures were adopted from well-validated measures (used in other research). To assess
the appropriateness of the measures for this research, the authors conducted in-depth interview
with 22 senior executives from seven organizations in the pre-test stage after which small
adjustments were made. The unit of analysis is the strategic business unit (SBU) and all survey
respondents were asked to refer to NPD projects of all NPD teams governed by their own SBUs when
they filled out the survey. The Dependent Variable, the number of NPD projects, is measured by the
number of NPD projects initiated during the previous 12 months at the SBU level. This measurement
is adopted as to ensure that the common method bias that can result from using perceptual
measures for variables is avoided. In addition, to measure whether NPD projects really matter for
NPD outcomes, the authors also collected firm performance data : return on investment (ROI) and
subjective overall performance, adopted from Song & Parry, which consists of three performance
measures relative to firm objectives. For measuring the independent variable of strategic planning,
the authors use a well-validated five-item Likert-type scale to measure the extent to which planning
reflects a formal decision-making process, the extent to which the strategic plan is strictly
implemented, and whether it includes an explicit process for determining specific, long-range
objectives and process for generating alternative strategies.On control variables, market turbulence,
market growth, and technology turbulence is measures using multi-item scales, also derived from
different authors.