calculate how each of my 30 product categories score on every of mentioned dimensions.

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Assignment description

Master thesis in marketing trying to inspect if product dimensions ( hedonic, risk, haptic, vertical differentiation) influence price paid when using mobile i.e. if these product dimensions moderate the relationship between price paid and mobile. The hypothesis are that product that scores higher on the hedonic dimension influence consumers in the way they are willing to pay a higher price when using mobile ( and similarly to all other categories).

 1.      My the first part of the analysis was to calculate how each of my 30 product categories score on every of mentioned dimensions. I did that by creating mean for every product on every dimension from my survey observations. This looks like this:

 

Category	Hedonic	Haptic	Risk	Vertical1	Vertical2
Adapter	1.4211	2.5746	3.1184	4.7368	4.9342
Alarm system	1.3239	2.1315	4.4310	5.2958	5.2958
Autographs	6.2126	2.2850	3.6319	4.0000	3.7101
Baby-stroller	1.5429	5.1524	3.8200	5.2714	5.8429
Bathrobe	2.5462	5.9598	2.8096	4.6867	5.2048
Cigars	6.6373	4.0882	3.4353	5.0588	5.0441
Digital camrecorder	4.1221	4.2113	4.3887	5.8592	5.9437
Guitar	5.4030	5.9701	4.1821	5.6716	5.5522
Hammer	1.6715	4.9855	2.6029	3.5942	4.3478
Iron	1.4300	3.7005	3.5275	5.0870	5.7101
Lego	6.1727	4.0789	2.9349	4.8434	4.2289
Liquor	6.1746	2.6614	3.3429	5.2540	5.5714
Mountain bike	4.6422	5.0686	4.4118	5.9265	5.7941
Pet food	1.4187	2.1057	2.4659	5.1707	5.6951
Picture frame	4.1711	4.0789	2.2684	3.5526	5.4737
Pots&pans	1.8468	4.3018	3.2108	5.2838	5.6757
Radio-contolled models	5.0423	3.6296	4.4698	5.1111	5.0635
Robot vacuum	1.8942	2.9365	4.5619	5.4127	5.2540
Router	2.3778	2.0222	3.7333	4.2667	4.4933
Shampoo	1.7791	3.3735	3.0554	5.1205	6.1084
Sitting chair	1.9552	5.5622	3.3075	4.9851	5.6866
Smartwatch	3.1526	4.1687	4.4434	5.6024	5.4578
Snowboard	6.2598	4.4314	4.1824	5.3382	5.2941
Soccer shoes	3.5176	5.6471	3.7741	5.4824	5.6118
Tablet PC	3.8011	4.6452	4.5839	5.9194	6.1452
Tent	4.4627	3.9652	3.8418	4.8806	4.9701
TV	4.9095	2.8025	4.6938	5.6667	6.1852
Video games	6.2250	2.0610	4.1829	4.9146	5.3171
Vinyl records	6.3429	3.2905	3.2343	4.1429	4.5714
Wheel rim	1.9516	2.8172	3.7226	4.5968	4.6452

 

2.      Now, in this second part that I am doing, I need to establish a connection between this dimension, mobile, and price paid.

 My professor gave me these instructions for R:

 What you would have to do to do it right:

• Merge the data (as you said) – this can be done easily in excel. Shouldn’t take more than half an hour max. Without knowledge in R, I think you could not work with the dataset I sent you, so I made you one that is already prepared, and where you just have to add the category characteristics. (I have already done this, table from above)
• Do a regression model of the following form: Price paid = a + c * mobile * risk + d * mobile * hedonic + e * mobile * haptic etc.; in R it looks like this:

reg2 <- lmer(lprice1 ~ mean_mobile * risk + mean_mobile * hedonic +  mean_mobile * haptic + log_days_online + wday + l_start_price + price_infor + lnbuy_now_price +

               (1| newcateg) + (1| customerid),

             data=daprod2, REML = TRUE)

 > you would only do the first line, I guess, because the rest is too difficult to understand ad hoc now. So that’s a regular OLS regression of ln(price) on the mobile variable and the category characteristics, including a few control variables.

> mean_mobile is the share of bids on mobile (vs. desktop devices). This is your mobile variable. You include this, the characteristics, and their interactions, while their interactions are the important effects because you want to know whether the effect of mobile differs depending on these characteristics (like in your framework).

Basically, I need to do a regression to see how these variables and their interactions influence my price paid. Since I am not familiar with R my professor told me I can also do a very simple, ordinary regression model. 

 

The basic thing I want to see is if these dimensions in combination with mobile effect price paid. I need to include mean_mobile variable that represents my usage of mobile device, the dimensions (hedonic, risk, haptic, vertical1 and vertical2), and their interactions, while their interactions are the important effects because I want to know whether the effect of mobile differs depending on these dimensions.

Attachments:

Related Questions

. The fundamental operations of create, read, update, and delete (CRUD) in either Python or Java

CS 340 Milestone One Guidelines and Rubric  Overview: For this assignment, you will implement the fundamental operations of create, read, update,

. Develop a program to emulate a purchase transaction at a retail store. This  program will have two classes, a LineItem class and a Transaction class

Retail Transaction Programming Project  Project Requirements:  Develop a program to emulate a purchase transaction at a retail store. This

. The following program contains five errors. Identify the errors and fix them

7COM1028   Secure Systems Programming   Referral Coursework: Secure

. Accepts the following from a user: Item Name Item Quantity Item Price Allows the user to create a file to store the sales receipt contents

Create a GUI program that:Accepts the following from a user:Item NameItem QuantityItem PriceAllows the user to create a file to store the sales receip

. The final project will encompass developing a web service using a software stack and implementing an industry-standard interface. Regardless of whether you choose to pursue application development goals as a pure developer or as a software engineer

CS 340 Final Project Guidelines and Rubric  Overview The final project will encompass developing a web service using a software stack and impleme