Read the paper so you understand the context in which to tell what you learn from the data analysis

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

This assignment like all the others is a learning assignment. It is intended to teach you new things rather than just exercise what has been learned.

Background

Perceived Contract Violation and Broken Promises

• Back on the Planet Krypton they heard of this amazing paper on Perceived Contract Violation and Broken Promises

• You have a copy that paper by Pavlou and Gefen (2005) on BB Learn. It is in the folder

Paper Readings Throughout the Course which is located in Pre‐work for Residency I

• And, in honor of Sir William Ramsay who in 1898 discovered the birthplace of Clark Joseph Kent and in recognition of the Nobel Prize in chemistry given to Sir Ramsey in 1904 on account of it, they decided to test the conclusions of that paper

• Clark Joseph Kent is the legendary writer for the newspaper Daily Planet

• There is a caveat, however. True to its name ρυπτός (Greek kryptos for "the hidden one”), there some hidden outliers

• Your mission, should you decide to accept it, and you really have no choice, is in the next slides.

Psychological Contract Violation in Online Marketplaces: Antecedents, Consequences, and Moderating Role

Read the paper so you understand the context in which to tell what you learn from the data analysis. This is the story. The logic is as in the paper. Knows Dan/ Does not know Dan/Real KryptonatesTrust in the KSC Community Psychological Contract Violation through Broken Promises Amount willing to pay to buy a ticket to the Phantom Zone Jor‐L, an aspiring scientist, ran this survey among his colleagues at the Kryptonian Space Center, aka KSC, to study if previously broken promises by Detective Dan have any effect on their willingness to participate in an auction to buy a ticket to the Phantom Zone. He assumes this effect on their intention might be mediated by their trust in the KSC community. Unbeknown to Jor‐L some of his colleagues know of Detective Dan, some do not, and some are real Kryptonites.

Your mission is to reconstruct the model, assess how strong the effect of broken promises is, verify the mediation, and through the moderation of Knowing Dan identify the real Kryptonates who could not care less about Dan.

Answers in the survey are on a 1‐7 Likert scale ranging from strongly disagree to strongly agree. Missing values in the survey are marked by ‐1. You can leave all those as-is.

Analyses

• Analyses

I. Run descriptive stats

II. Show the frequency of Kryptonates

III. Run a PCA to verify which items to include in the analysis. Do not include Buy Tickets or Kryptonate in the PCA.

• Variable name prefix tells you what the grouping should be. Variable names starting with BP are Broken_Promises, starting with TC are Trust_in_Community

Analyses

• Analyses

IV. Verify that the Cronbach Alphas are okay

V. Create constructs

VI. Create the model with two Proc Regs

Analyses

VIII. Looking at the moderation outliers identify the real Kryptonates.

• That is easy. Real Kryptonates do not care.

• To verify that you identified the Kryptonates correctly there is a variable named Kryptonate. Its value of 1 means that data point is a Kryptonate.

• You are not suppose to identify Kryptonate based on that. Only to verify your answer.

• To identify the Kryptonates, run a regression to model the orange arrows. Keep as output from that regression the DFFITS values in a new dataset.

• Because Kryptonates do not care, their DFFITS values will be out of sync with the mean values of other their DFFITS values

• Identify those rows and print them. Check that in those rows the value of Kryptonate is 1.

Deliverables

I. The SAS code

II. SAS Results

III. Your interpretation of each analysis in Results

IV. Tell the story revealed by the data analysis in the theoretical context of the paper

V. Assess the validity of the original Pavlou and Gefen (2005) paper by proposing 2 issues of validity that could have been improved and suggesting how to do so. Limit 1000 words

Attachments:

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