Assignment Methodology & Technology
As indicated in the Course Guide, you will need to complete a group assignment which will count for
33% towards your total methods grade for the course. The minimum passing grade for the
assignment is 5.0 (without any rounding off).
To complete the assignment, each project group needs to take the following steps:
1. 23 datasets have been uploaded in Blackboard. You can find these datasets in the Assignment
folder. Each dataset contains a .sav (SPSS format) file. Further, the codebook to the datasets are in
this folder. Every group has been assigned to a dataset (see the table also in this folder).
2. Perform a linear regression statistical analysis using the dataset you are assigned to, and present
your findings in a report. The content of the report and the criteria for grading are described in the
tables below.
3. Once completed, you will need to upload your report in Blackboard, and the SPSS output file (.spv
file format). There is no word limit for your report, as long as it satisfies the criteria for grading
presented below. The deadline for submitting the report is Friday 19th January.
Criterion | |
| Cover page and formatting | The report should have the proper cover page and formatting (all student names and student numbers, page numbers, consistent font sizes, etc) |
| Hypothesis testing | Estimate at least two different linear regression models using the variables provided in each model to test your hypotheses, and interpret the results accordingly (e.g. paying attention to the coefficients, p-values, confidence intervals, and model fit measures). |
| Assumptions | Check whether the assumption for linearity holds for your preferred model that you estimated and, if it does not hold, discuss a strategy for dealing with the violations. |
| Influential cases | Check whether there are any influential cases in the preferred model that you estimated. |
| Sensitivity analysis | Perform a sensitivity analysis, either to check the effect of a model with and without control variables, OR to check the impact of removing influential cases. |
| Conclusions | Conclusion on the hypotheses should be formulated, while accounting for possible problems with your data (violation of assumption of linearity, influential cases) or research design. |
| Tables and figures | Tables and figures should not be simply copy/pasted from the SPSS output file. Tables need to be properly formatted using only the pertinent information, while figures need to have their axes labelled properly. Both tables and figures need to be numbered and named appropriately. |
Do design rules facilitate or complicate architectural innovation in innovation alliance
networks?
Architectural innovation is fundamental to the renewal of technological systems. However, it can
be a real challenge to organize architectural innovation, all the more so when success hinges
upon close collaboration with other firms that are responsible for different subsystems of the
end product. This study examines the impact of product design rules and the degree of
organizational coupling among innovation network partners on the performance of architectural
innovation projects.
Please study, with the data provided whether lead firms that initiate architectural innovation
can mitigate the likely pronounced negative effects of design rules by selecting appropriate
innovation network partners.
Formal hypotheses:
Hypothesis 1: The presence of design rules have a linear negative effect on and the
performance of collaborative architectural innovation.
Hypothesis 2. The degree of organizational coupling (tie strength) positively moderates the
relationship between design rules and the performance of architectural innovation.
As control variables, please use the following measures: Marketing resources, total budget and
firm size.
Perform a linear regression analysis while keeping in mind the criteria listed in the table above.