22 Jul HOW CLEAR SPECIFIC AND UNDERSTANDABLE ARE THE AIMS RQS AND/OR HYPOTHESES?
Order Description
Assessment name: Quantitative research design
Description: You will be provided with a quantitative research article to critique. The critique will involve the identification of the strengths and limitations in the design of the study based upon the principles and assumptions of good research design consistent with realising the articles stated research purpose(s) from a quantitative perspective. On this basis individuals will then develop an alternative research design consistent with realising the research purpose(s) again based on upon the principles of good research design from a quantitative perspective. Opportunities to practice article critique and designing quantitative research based upon the principles of good research design will be provided in class. Conceptual material to support research design decisions is provided on Blackboard. Note that this assessment does not involve data collection.
Length: 2500 words excluding references Figures and Tables
Formative or Summative: Formative and Summative
A TEMPLATE FOR READING AND EVALUATING RESEARCH
Article: Lukas B.A. Whitwell G.J. &Heide J.B. (2013). Why Do Customers Get More Than They Need? How Organizational Culture Shapes Product Capability Decisions. Journal of Marketing 77 1-12.
Methodology / research Issue Description Evaluation strengths and limitations Redesign options to address negative evaluations (where appropriate)
PART A. PURPOSE OF THE STUDY.
The aims research questions and/or hypotheses
In your own words explain the purpose of the study and the RQs/hypotheses of the study.
Identify:
a. independent and dependent variables (more commonly associated with experimental designs) or;
b. predictor and outcome variables (more commonly associated with non-experimental designs);
c. mediators or intervening variables;
d. control variables;
e. any other variables.
Aim was to explain why firms provide products to customers with greater capabilities (e.g. functions) than the customers require or want. Authors hypothesised that two particular dimensions of organizational culture specifically the degree of adhocracy culture and market culture lead to increased over-provision of product capabilities. They argue this is the case because these two culture types are reflect a commitment to firm differentiation via being leading edge and competitive and potentially leading to over-provisioned products. These relationships are then hypothesised to be moderated by customer orientation the degree to which firms place customer interests before firm interests. Higher customer orientation is hypothesised to reduce the effect of culture on over-provision compared to the effect of lower customer orientation.
Predictors or IVs: 2 dimensions of organisational culture adhocracy culture and market culture.
Outcome or DV: product capability provision.
Mediators: none.
Moderators: customer orientation.
Control variables: four: supplier reputation; and supplier product experience (see page 5 col 1 para 6); and two other culture dimensions bureaucracy and clan culture.
Other variables: none. How clear specific and understandable are the aims RQs and/or hypotheses?
The aims and hypotheses seem specific.
To evaluate if they are sound hypotheses requires understanding the literature in this area. It is difficult to evaluate how sound hypotheses and RQs are without good content knowledge.
We can however identify the types of variables in the study. Are the RQs and/or hypotheses able to be written more clearly?
I think they are fine.
PART B. THE RESEARCH DESIGN
Categorise and briefly describe the research design.
1. Categorise the design: Note that more than one of the above may apply.
Experimental design?
Quasi-experimental research?
Quasi-experimental design
Written questionnaire survey research?
Interview design
Case study design
Cross-sectional?
Longitudinal?
Other?
Combination of above?
2. Briefly describe the research design. Include any significant design elements present?
1. A cross sectional questionnaire design was used. The unit of analysis was the dyad and the focus of the data collection concerned one product (from within one strategic business unit within a firm. More detail is provided about the product selected by the supplier on page 5 Col 2.
2. Matched supplier-customer dyads were used. Suppliers provided data on the IVs and moderator (dimensions of culture and customer orientation) and the customer provided data about the DV. See p4 and p 10. Thus different sources of data were used for IVs and DVs reducing the likelihood of common source bias in the data. Weakness: Use of a cross-sectional design has the inability to demonstrate causality. The detailed reasons for this are taken up in Part E (lack of temporal separation inability to control other plausible causes).
Weakness: Common method bias could exist in that the same method of data collection was used for all data (all written questionnaires).
Strength: The use of matched dyadic data collection. Several positive aspects to this:
1. Increases validity of results as we know customers know the product;
2. prevent the elevation of the correlations (and thus regression coefficients) between the IVs and DV through using different sources of data. (i.e. reduced extent of one form of common method bias). (but see the other weakness in method above)
Strength: Use of control variables to control other plausible explanations.
How well is the research design aligned to the stated research purpose? Would a different /adapted design be better? If so what changes do you suggest and why?
There is a strong match. The RQ could be answered but only as associations among variables not as causal links.
To remove all other plausible explanations would need an experiment. This would be impossible to achieve.
To temporally separate IV and DV might assist to demonstrate which came first. But depends on how long the it takes for culture to affect (cause) capability provision. It may take a number of years for changes in culture to influence capability provision. This would require a long term panel design in which all variables are measured repeatedly probably over many years. This will not in itself exclude other plausible causes.
To reduce common method bias further other forms of measures apart from questionnaires might be investigated. It is left to you to think what these might be.
PART C.
Measurement strategy.
For each of the variables identified in part A above provide:
1. The Conceptual definition;
2. The Operational definitions
For measured variables:
3. Was the measure an existing scale an existing scale with some adaptation; a new scale? Provide the name and reference for the scale if not original.
4. Briefly describe the measurement properties of the scale. E.g. type of scale (rating scale Semantic differential scale Thurston scale); number of items; Number of rating points; Anchor labels etc.
5. Was evidence of Reliability of the measure provided?
6. Was evidence of the construct Validity of the scale provided?
7. Was the measure provided or information provided as to its availability?
For experiments: (this was not an experiment so not relevant)
1. How were the IVs manipulated?
2. What evidence of the construct validity of the manipulations were provided? (e.g manipulation checks; other research).
Coding strategy specified? (No coding took place)
Below is an example of a critique of the measurement of the culture variables. A similar style of critique can occur for all variables.
Predictors or IVs: 2 dimensions of organisational culture.
Adhocracy culture and market culture.
1. Adhocracy: An external focus on firm differentiation through being leading edge and providing unique products/services.
2. Summed (or averaged) scores on items developed by Cameron and Quinn 2006.
3. An adaptation of the Competing Values Framework scale by Cameron and Quinn (2006). The scale was modified by asking participants to rate each culture on a 1-7 scale. (To see what this really means and to evaluate it one would need to find the original scale in which the items were developed and compare the scales).
4. Each culture dimension was assessed by rating 4 items on a 7 point scale anchored at the ends by completely inaccurate description and completely accurate description.
5. Reliability: Yes on page 6 and table 1. They reported composite reliability as their reliability measure (common in marketing studies). You would have to find out what composite reliability means to evaluate it.
6. Construct validity: Authors removed items which did not correlate with total scale. They do not report which items. None removed from culture measures. Provided a factor analysis of (composite/parcelled) items. Discriminant and divergent validity of (composite) items established. Evidence of construct validity not provided. But culture framework well established and the original measure widely used. Cameron and Quinn (2006) provide reliability and validity data in Appendix A of their book. Other studies have been conducted on the validity of the scale. To make a full assessment these would need to be examined.
Outcome or DV: product capability provision.
Mediators: none.
Moderators: customer orientation.
Control variables: two: supplier reputation; and supplier product experience (see page 5 col 1 para 6).
1 and 2. In particular how well do the conceptual and operational definitions match?
Inspection of the items suggests a reasonable match. The last item of adhocracy culture does seem to be very similar to product capability provision. My business unit emphasizes developing new products features and services.Trying new things and prospecting for opportunities are valued. (Underlining added).
3. In particular how did the adaptation or development occur?
Little information provided about this. Mention is made of using steps outlined by Churchill (1979) in terms of ensuring items match the construct. Some of the wording of the items is different from the original Cameron and Quinn (2006) measure.
5 and 6. In particular how well does the evidence provided support the reliability and construct validity of the measures?
Evidence of adequate reliability of the items is provided. Evidence of construct validity is not provided;But culture framework well established and the original measure widely used. Cameron and Quinn (2006) provide reliability and validity data in Appendix A of their book. Other studies have been conducted on the validity of the scale. To make a full assessment these would need to be examined. Strictly speaking scale adaptations should be re-validated. Construct validation of the revised scales would be advisable. The form of the original scales was significantly different from the rating scales used in the current study. (To see this it requires looking at the original format as presented by Cameron and Quinn (2006).
Knowledge of the statistical procedures used to evaluate the reliability of the scales is required to be able to critique that aspect. It is NOT expected that such critiques will be provided in BSn502. However in short the use of what is called item parcelling within the confirmatory factor analysis (CFA) may obscure issues with individual items.
PART D. SAMPLING STRATEGY
1. Was a target population specified? Can a target population be inferred?
2. Describe the overall strategy. How was the sample chosen? If possible label the strategy (e.g. simple random sampling cluster sampling convenience sampling etc)
For probability-based methods:
a. What sampling frame was used or developed? How good was this? How well does this frame match the population?
b. How was a probability sample drawn from the frame? How good was this process? How well does the sample drawn match the sampling frame?
3. What was the final sample size? Was a response rate provided? How can it be calculated from the information provided? Show how or what information is lacking. Is this size sufficient for the research?
4. To be placed in the critique. How representative of target population/sampling frame/drawn sample was the final sample? How statistically generalizable are the findings? To whom are the findings statistically generalizable?
1. No target population as such specified. Can infer from the introduction and discussion that the target population appears to be and business to business firms that supply products (as opposed to services). For the distinction from services see P9 col 2 para 3 and P5 col 2.
2. A bought list of IT firms was used as a sampling frame of firms was used from which a random sample of 1024 firms was drawn. These were then screened for eligibility (that is they supplied products) leaving 317. One might make an argument for this being a convenience sample or a random sample. See the critique.
2a. A sampling frame was used. This was a bought list of IT companies. See Critique for 2.
2b. It is not stated how the sample was drawn: we can assume it was random as stated; that is every element in the frame had an equal probability of being included.
3. Final sample size was 100 matched supplier-customer dyads. From the sample of 317 only 105 firms were prepared to nominate a customer (response rate = 105/317 = 33%). From these 105 only 100 customers agreed to participate. The remaining 5 firms were dropped. Final sample size = 100; final response rate = 100/317 = .32.
1. This is not unusual. Theoretically informed work often has ill-defined populations. The operational population became IT firms then IT firms on the database then those sampled that provided services.
2 and 2a. It depends on how one defines the population. If it is B2B firms in the IT industry then the sampling strategy can only truly be a probability based sample if ALL IT firms have a known and equal chance of being sampled. All that the authors had access to was an existing database. It is not known if the database was complete or if all IT firms had a known and equal probability of being included in that database. It is most plausible that neither was the case; although without further information one cannot be certain. Under these circumstances one might claim a convenience sample was used.
If we assume the sampling frame is high quality (that is has high coverage of the population with no biasing in its make up) then a random sample was drawn.
Nevertheless the list may be better than anything the researchers could have built themselves. If this is the case then sampling from this list is a reasonable process.
2b. The screening of eligible firms AFTER sampling could be a concern. If selection was completely random then the 317 should still be a random sample of eligible firms from the database. I am prepared to argue that the sample of 317 is a random sample of the database.
3. Many studies have a smaller response rate. The final sample is small. It was sufficient for the research but the study would have benefited from a larger sample.
Q: How representative of target population/sampling frame/drawn sample was the final sample? How statistically generalizable are the findings? To whom are the findings statistically generalizable?
The large number of firms that did not provide customer details (non-response) undermines any claim as to the statistical representativeness of the final sample to the sampling frame or the population. Therefore strictly speaking findings cannot be claimed to be statistically generalizable. Given sound theorising a (tentative) claim might be made of analytic generalizability. 1. I think this is reasonable. It is not possible to have well defined populations of this nature. All that can be defined is a relevant sub-population such as they have done.
2. More information regarding the sampling frame is required. To assess this we need to know its coverage of the population. This is a large limitation to evaluation. Theoretically it is possible to develop a sampling frame of IT firms. The issue will be whether or not this will be better (more complete) than the list used. Developing a sampling frame is expensive. It would have been better if the sampling frame only contained eligible members of the population. Given that frame random sampling was the best process to undertake.
Overcoming non-response in this study would have been difficult. It is known if the non-response was ONLY because firms would not provide customer details: it may be other firms did not respond at all. Reducing non-response would be useful.
Overall this is a common sampling strategy but flawed as outlined. Ideally a higher response rate would have been achieved.
PART E.
Knowledge claim?
1. what does the authors claim to be true as a result of the application of their method
2. In what ways does the author generalise the conclusions? On what basis are these generalisations justified? (Think about analytic and statistical generalisabilty?)
Arguably there are several knowledge claims (abbreviated here as KC).
1. Page 7 has the main finding.
both adhocracy culture and market culture in themselves promoteoverprovision consistent with H1 and H2. However
a customer orientation only attenuates the overprovision
tendency of an adhocracy culture. ThusH3 is supported and H4 is not supported.
This is a statement of the statistical findings. They imply that culture affects the DV.
2. Generalization is attempted through both statistical and analytic generalizability.
a. Statistical generalizability. The use of statistical tests BY DEFINITION is based on claims of statistical generalizability.
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