mat 510,stayer mat 510,stayer mat 510 complete course,stayer mat 510 entire course,stayer mat 510 week 1,stayer mat 510 week 2,stayer mat 510 week 3,stayer mat 510 week 4,stayer mat 510 week 6,stayer mat 510 week 7,stayer mat 510 week 8,stayer mat 510 week 9,mat 510 final exam new,mat 510 midterm exam new,mat 510 tutorials,mat 510 assignments,mat 510 help
mat 300,strayer mat 300,mat 300 entire course new,mat 300 discussion questions,strayer mat 300 week 1,strayer mat 300 week 2,strayer mat 300 week 3,strayer mat 300 week 4,strayer mat 300 week 5,mat 300 case study,mat 300 discussion correlation and regression,mat 300 graphical representations,strayer mat 300 tutorials,strayer mat 300 assignments,mat 300 help
For more course tutorials visit
www.newtonhelp.com
MAT 510 Final Exam Set 1
Question 1
Improvement is needed for an organization to survive because:
Question 2
The Statistical Thinking Strategy has significant commonality with the scientific method. Which of the following statistical thinking principles is NOT generally associated with the scientific method?
Question 3
MAT 510 Effective Communication - tutorialrank.comBartholomew46
For more course tutorials visit
www.tutorialrank.com
MAT 510 Final Exam Set 1
Question 1
Improvement is needed for an organization to survive because:
Question 2
Reduction in customer complaints - Mortgage IndustryPranov Mishra
The project aims at analysis of Customer Complaints/Inquiries received by a US based mortgage (loan) servicing company..
The goal of the project is building a predictive model using the identified significant
contributors and coming up with recommendations for changes which will lead to
1. Reducing Re-work
2. Reducing Operational Cost
3. Improve Customer Satisfaction
4. Improve company preparedness to respond to customer.
Three models were built - Logistic Regression, Random Forest and Gradient Boosting. It was seen that the accuracy, auc (Area under the curve), sensitivity and specificity improved drastically as the model complexity increased from simple to complex.
Logistic regression was not generalizing well to a non-linear data. So the model was suffering from both bias and variance. Random Forest is an ensemble technique in itself and helps with reducing variance to a great extent. Gradient Boosting, with its sequential learning ability, helps reduce the bias. The results from both random forest and gradient boosting did not differ by much. This is confirming the bias-variance trade-off concept which states that complex models will do well on non-linear data as the inflexible simple models will have high bias and can have high variance.
Additionally, a lift chart was built which gives a Cumulative lift of 133% in the first four deciles
mat 510,stayer mat 510,stayer mat 510 complete course,stayer mat 510 entire course,stayer mat 510 week 1,stayer mat 510 week 2,stayer mat 510 week 3,stayer mat 510 week 4,stayer mat 510 week 6,stayer mat 510 week 7,stayer mat 510 week 8,stayer mat 510 week 9,mat 510 final exam new,mat 510 midterm exam new,mat 510 tutorials,mat 510 assignments,mat 510 help
mat 300,strayer mat 300,mat 300 entire course new,mat 300 discussion questions,strayer mat 300 week 1,strayer mat 300 week 2,strayer mat 300 week 3,strayer mat 300 week 4,strayer mat 300 week 5,mat 300 case study,mat 300 discussion correlation and regression,mat 300 graphical representations,strayer mat 300 tutorials,strayer mat 300 assignments,mat 300 help
For more course tutorials visit
www.newtonhelp.com
MAT 510 Final Exam Set 1
Question 1
Improvement is needed for an organization to survive because:
Question 2
The Statistical Thinking Strategy has significant commonality with the scientific method. Which of the following statistical thinking principles is NOT generally associated with the scientific method?
Question 3
MAT 510 Effective Communication - tutorialrank.comBartholomew46
For more course tutorials visit
www.tutorialrank.com
MAT 510 Final Exam Set 1
Question 1
Improvement is needed for an organization to survive because:
Question 2
Reduction in customer complaints - Mortgage IndustryPranov Mishra
The project aims at analysis of Customer Complaints/Inquiries received by a US based mortgage (loan) servicing company..
The goal of the project is building a predictive model using the identified significant
contributors and coming up with recommendations for changes which will lead to
1. Reducing Re-work
2. Reducing Operational Cost
3. Improve Customer Satisfaction
4. Improve company preparedness to respond to customer.
Three models were built - Logistic Regression, Random Forest and Gradient Boosting. It was seen that the accuracy, auc (Area under the curve), sensitivity and specificity improved drastically as the model complexity increased from simple to complex.
Logistic regression was not generalizing well to a non-linear data. So the model was suffering from both bias and variance. Random Forest is an ensemble technique in itself and helps with reducing variance to a great extent. Gradient Boosting, with its sequential learning ability, helps reduce the bias. The results from both random forest and gradient boosting did not differ by much. This is confirming the bias-variance trade-off concept which states that complex models will do well on non-linear data as the inflexible simple models will have high bias and can have high variance.
Additionally, a lift chart was built which gives a Cumulative lift of 133% in the first four deciles
Chi Square Test of Association is used to determine whether there is a statistically significant association between the two categorical variables. This technique is used to determine if the relationship exists between any two business parameters that are of categorical data type.
This research only implies marital condition is correlated to the duration of calls, but did not find the quantitative relationship between them. Besides, duration’s relationship with other dimensions of information is also important for us to predict duration and target at valuable customers, which needs further research such as regression analysis.
DoWhy Python library for causal inference: An End-to-End toolAmit Sharma
As computing systems are more frequently and more actively intervening in societally critical domains such as healthcare, education, and governance, it is critical to correctly predict and understand the causal effects of these interventions. Without an A/B test, conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for causal reasoning.
Much like machine learning libraries have done for prediction, "DoWhy" is a Python library that aims to spark causal thinking and analysis. DoWhy provides a unified interface for causal inference methods and automatically tests many assumptions, thus making inference accessible to non-experts.
For a quick introduction to causal inference, check out amit-sharma/causal-inference-tutorial. We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (KDD 2018) conference: causalinference.gitlab.io/kdd-tutorial.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Testing Model Of Development Organic Farming Dragon Fruit Based Market ResearchIJMER
This study aims to test the model of the development of organic farming dragon fruit which has been obtained in previous studies. This follow-up study to test the model that has been obtained, through a comprehensive marketing research by analyzing the factors which influence consumers purchasing organic dragon fruit Object of the research in Pamekasan, The method used is the analysis of factors that influence consumer buying decisions in determining organic dragon fruit. The data used is the 60 respondents, the number of variables studied were 23 variables, factor analysis is based on 22
variables that can be further analyzed. One variable ignored because the value of the MSA is less than
0.5, the results showed that of the 22 variables were analyzed. The results showed that of the 22 variables
analyzed, there are 8 factors that influence consumers to buy organic dragon fruit, while the results of the 8 factors analyzed are: [1] Psychological (eigen value = 5,025), [2] The product (eigen value = 3,015), [3] Social (eigen value = 2,186), [4] Distribution (eigen value = 1.640), [5] Price (eigen value =1.354), [6] Promotion (eigen value = 1,286), [7] Individuals (eigen value = 1,196), [8] Service (eigen
value = 1.115), overall there are 3 of the most dominant factor affecting the marketing of organic dragon fruit, is the first factor of the product, the second is the social factor and the third factor is the price.
Measuring effectiveness of machine learning systemsAmit Sharma
Many online systems, such as recommender systems or ad systems, are increasingly being used in societally critical domains such as education, healthcare, finance and governance. A natural question to ask is about their effectiveness, which is often measured using observational metrics. However, these metrics hide cause-and-effect processes between these systems, people's behavior and outcomes. I will present a causal framework that allows us to tackle questions about the effects of algorithmic systems and demonstrate its usage through evaluation of Amazon's recommender system and a major search engine. I will also discuss how such evaluations can lead to metrics for designing better systems.
The project describes the analysis of gap in the expected and delivered level of service in the college mess. It includes data collection from students and analysing the data by using SPSS tool.
Exploring the behavioral intention to use e-government services: Validating t...Mark Anthony Camilleri
This study explores the online users’ behavioral intention to utilize the governments’ websites and their electronic services. The research methodology validates the measuring items from the unified theory of acceptance and use of technology (UTAUT) to better understand the participants’ attitudes toward their performance expectancy, effort expectancy, social norms, facilitating condition and behavioral intention to use the electronic government (e-gov) services. The findings from the structural equations modeling approach reported a satisfactory fit for this study’s research model. The results suggest that there were highly significant, direct effects from the UTAUT constructs, where the utilitarian motives predicted the online users’ behavioral intentions to use e-gov. Moreover, there were significant moderating influences from the demographic variables, including age, gender and experiences that effected the individuals’ usage of the governments’ online services. In conclusion, this contribution identifies its limitations and suggests possible research avenues to academia.
Chi Square Test of Association is used to determine whether there is a statistically significant association between the two categorical variables. This technique is used to determine if the relationship exists between any two business parameters that are of categorical data type.
This research only implies marital condition is correlated to the duration of calls, but did not find the quantitative relationship between them. Besides, duration’s relationship with other dimensions of information is also important for us to predict duration and target at valuable customers, which needs further research such as regression analysis.
DoWhy Python library for causal inference: An End-to-End toolAmit Sharma
As computing systems are more frequently and more actively intervening in societally critical domains such as healthcare, education, and governance, it is critical to correctly predict and understand the causal effects of these interventions. Without an A/B test, conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for causal reasoning.
Much like machine learning libraries have done for prediction, "DoWhy" is a Python library that aims to spark causal thinking and analysis. DoWhy provides a unified interface for causal inference methods and automatically tests many assumptions, thus making inference accessible to non-experts.
For a quick introduction to causal inference, check out amit-sharma/causal-inference-tutorial. We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (KDD 2018) conference: causalinference.gitlab.io/kdd-tutorial.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Testing Model Of Development Organic Farming Dragon Fruit Based Market ResearchIJMER
This study aims to test the model of the development of organic farming dragon fruit which has been obtained in previous studies. This follow-up study to test the model that has been obtained, through a comprehensive marketing research by analyzing the factors which influence consumers purchasing organic dragon fruit Object of the research in Pamekasan, The method used is the analysis of factors that influence consumer buying decisions in determining organic dragon fruit. The data used is the 60 respondents, the number of variables studied were 23 variables, factor analysis is based on 22
variables that can be further analyzed. One variable ignored because the value of the MSA is less than
0.5, the results showed that of the 22 variables were analyzed. The results showed that of the 22 variables
analyzed, there are 8 factors that influence consumers to buy organic dragon fruit, while the results of the 8 factors analyzed are: [1] Psychological (eigen value = 5,025), [2] The product (eigen value = 3,015), [3] Social (eigen value = 2,186), [4] Distribution (eigen value = 1.640), [5] Price (eigen value =1.354), [6] Promotion (eigen value = 1,286), [7] Individuals (eigen value = 1,196), [8] Service (eigen
value = 1.115), overall there are 3 of the most dominant factor affecting the marketing of organic dragon fruit, is the first factor of the product, the second is the social factor and the third factor is the price.
Measuring effectiveness of machine learning systemsAmit Sharma
Many online systems, such as recommender systems or ad systems, are increasingly being used in societally critical domains such as education, healthcare, finance and governance. A natural question to ask is about their effectiveness, which is often measured using observational metrics. However, these metrics hide cause-and-effect processes between these systems, people's behavior and outcomes. I will present a causal framework that allows us to tackle questions about the effects of algorithmic systems and demonstrate its usage through evaluation of Amazon's recommender system and a major search engine. I will also discuss how such evaluations can lead to metrics for designing better systems.
The project describes the analysis of gap in the expected and delivered level of service in the college mess. It includes data collection from students and analysing the data by using SPSS tool.
Exploring the behavioral intention to use e-government services: Validating t...Mark Anthony Camilleri
This study explores the online users’ behavioral intention to utilize the governments’ websites and their electronic services. The research methodology validates the measuring items from the unified theory of acceptance and use of technology (UTAUT) to better understand the participants’ attitudes toward their performance expectancy, effort expectancy, social norms, facilitating condition and behavioral intention to use the electronic government (e-gov) services. The findings from the structural equations modeling approach reported a satisfactory fit for this study’s research model. The results suggest that there were highly significant, direct effects from the UTAUT constructs, where the utilitarian motives predicted the online users’ behavioral intentions to use e-gov. Moreover, there were significant moderating influences from the demographic variables, including age, gender and experiences that effected the individuals’ usage of the governments’ online services. In conclusion, this contribution identifies its limitations and suggests possible research avenues to academia.
6. hapzi ali, et al., 2016, mercu buana univversity,iosr jbbHapzi Ali
Prof. Dr. Hapzi Ali, CMA
Universitas Mercu Buana (Mercu Buana University), Jakarta Indonesia
Bidang Ilmu: Marketing & Business Management, Research Method, MIS, Good Corporate Governance
www.mercubuana.ac.id.
email: hapzi.ali@gmail.com, hapzi.ali@mercubuana.ac.id
[Project] Customer experience and buying behaviour in e-commerce sitesBiswadeep Ghosh Hazra
The growing usage of internet in India provides an extremely lucrative market for many retailers and businesses. If e-retailers get to know the factors that broadly affect online behaviour, and the corresponding relationships between the type of online buyers and these factors, then they can further fine tune their marketing strategies to convert potential customers into permanent customers, while keeping the existing online ones.
This project on consumer behaviour is a part of a study, that broadly focuses on the factors which Indian online buyers keep in mind while they are shopping online. The research conducted found that Customer Service, Customer Review/Recommendations and Discount/Offers are the three dominant factors that influence online consumer perception. Consumer behaviour is an applied discipline because some decisions are significantly affected by their expected actions. The two perspectives that demand application of its knowledge are societal and micro perspectives. Internet is changing the very method consumers shop, buy goods and services, and has rapidly become a global phenomenon.
Today all companies must use the Internet with the goal of cutting marketing costs, and at the same time, received quantitative information; thereby reducing the price of the services and products, the companies offer. High competition compels companies to continuously look for cost cutting measures. Companies also use internet to communicate, convey and disseminate information, to take feedback, conduct satisfaction surveys with customers and most importantly, to sell the product.
Impact of Gender on Customer Satisfaction for Service Quality: A Case Study o...deshwal852
Satisfaction of customers is an integral part for the success of the business. Customer satisfaction is based on perceived service quality. Service quality is a comparison of expectations with performance. Customer satisfaction is based on perceived service quality. Service quality is a crucial factor for the triumph of the business firm. This study is an attempt to examine the impact of gender on satisfaction level of customers
for service quality in hyper stores. All the relevant data has been collected through a sample survey of 70 customers purchasing goods from hyper stores in South West Delhi. Sample was drawn by convenient sampling. Retail Service Quality Scale was used as measurement instrument. It was developed by Dabholkar, Thrope and Rentz (1996). Likert’s five point scale was used to rate all the variables. A survey
was conducted to verify the hypothesis and research framework. Statistical techniques such as mean,
standard deviation and t- test were used. Major Findings exhibit that there is no significant difference between male and female customers for different variables of service quality in hyper stores.
Complete the following assignments using excel and the following tLynellBull52
Complete the following assignments using excel and the following template:
· Assignment – Statement
· Identify Business Problem -
“Define Problem statement [aka Case Analysis Assignment]”
· Analytics Tools and Models used and results
· Interpretation, Discussion, and Analysis of Findings and Results –
Interpretation, Discussion, and Analysis of outcomes and results of Analytics Tools and Models used
· Tip: Support your Interpretation, Discussion, and Analysis of Results with the numbers you developed in your:
· Analytics Tools and Models used results
· Business Analytics Case Analysis EXCEL model(s) and outcomes and results
· Recommendations
Assignment information:
The worksheet Purchasing Survey in the Performance Lawn Care database provides data related to predicting the level of business (Usage Level) obtained from a third-party survey of purchasing managers of customers Performance Lawn Care.
The seven PLE attributes rated by each respondent are
8 The data and description of this case are based on the HATCO example on pages 28–29 in Joseph F. Hair, Jr., Rolph E. Anderson, Ronald L. Tatham, and William C. Black, Multivariate Analysis, 5th ed. (Upper Saddle River, NJ: Prentice Hall, 1998).
· Delivery speed —the amount of time it takes to deliver the product once an order is confirmed
· Price level —the perceived level of price charged by PLE
· Price flexibility —the perceived willingness of PLE representatives to negotiate price on all types of purchases
· Manufacturing image —the overall image of the manufacturer
· Overall service —the overall level of service necessary for maintaining a satisfactory relationship between PLE and the purchaser
· Sales force image —the overall image of the PLE’s sales force
· Product quality —perceived level of quality
Responses to these seven variables were obtained using a graphic rating scale, where a 10-centimeter line was drawn between endpoints labeled “poor” and “excellent.” Respondents indicated their perceptions using a mark on the line, which was measured from the left endpoint. The result was a scale from 0 to 10 rounded to one decimal place.
Two measures were obtained that reflected the outcomes of the respondent’s purchase relationships with PLE:
· Usage level —how much of the firm’s total product is purchased from PLE, measured on a 100-point scale, ranging from 0% to 100%
· Satisfaction level —how satisfied the purchaser is with past purchases from PLE, measured on the same graphic rating scale as perceptions 1 through 7
The data also include four characteristics of the responding firms:
· Size of firm —size relative to others in this market (0=small;1=large)(0=small;1=large)
· Purchasing structure —the purchasing method used in a particular company (1=centralized procurement,0=dec ...
The Mediating Role of Customer Satisfaction and Customer Trust in the Relatio...AJHSSR Journal
ABSTRACT: This study aims to examine the effect of product quality on customer loyalty mediated by
customer satisfaction and customer trust. This research is a quantitative research with a case study in the
company CV. JMF Sidoarjo, Indonesia by distributing questionnaires to 109 customers. Data is processed using
Structural Equation Modeling with Smart PLS 3 program. The results showed that product quality had a
significant direct effect on customer satisfaction, trust and loyalty. Product quality has no significant effect on
customer satisfaction and trust. Likewise, customer satisfaction and trust cannot mediate the relationship
between product quality and customer loyalty. All relationships point in a positive direction.This work helps the
product manufacturing business better understand consumer behavior, demands, and preferences, which can
boost consumer satisfaction, trust, and loyalty. This study adds to the body of knowledge on the causes of
consumer loyalty.
KEYWORDS : Customer, loyalty, quality, satisfaction, trust
Online product recommendations (OPRs), which include provider recommendations (PRs) and consumer reviews (CRs), are widely used in ebusiness to improve consumers' shopping efficiency, which consists of product screening efficiency and product evaluation efficiency. We construct a research model to explore the effect of perceived quality of OPRs on consumers’ shopping efficiency and the moderating role of product type, which usually includes search and experience product. Using an online questionnaire survey with 174 valid participants, our findings provide strong support for the proposed model. The empirical results reveal that higher perceived quality of OPRs is associated with higher consumer shopping efficiency. What’s more, the impact of perceived quality of PRs on screening efficiency is stronger for experience products than for search products, but the effect of perceived quality of CRs on screening efficiency is stronger for search products than for experience products. However, the moderating effect of product type on the relationship between perceived quality of OPRs and evaluation efficiency is not significant.
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
Acorn Recovery: Restore IT infra within minutesIP ServerOne
Introducing Acorn Recovery as a Service, a simple, fast, and secure managed disaster recovery (DRaaS) by IP ServerOne. A DR solution that helps restore your IT infra within minutes.
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
Getting started with Amazon Bedrock Studio and Control Tower
Identifying key factors affecting consumer purchase behavior in an online shopping context
1. Identifying key factors affecting consumer
purchase behavior in an online shopping context in
Gwalior
Group members name
Kratika Agnihotri
Kuldeep Mathur
Sakshi Mishra*
Shivam Sharma*
Sumit Arora*
Research methodology
Objective and hypothesis
These are the following objectives of this study:
The aim of this study is to identify the key service quality dimensions that affect
relational benefit while choosing online shopping store.
On what basis customer choose the online shopping stores?
To know about the service provided by the online shopping stores.
To study the level of satisfaction of the respondents about the various facilities.
This study focuses on various satisfactory levels like quality, satisfaction, security
available of the online stores.
The study investigates the impact of product quality and security perception on relational
benefit.
In order to meet the study’s objectives and answer research questions, following
Hypotheses were proposed:
H1. There is a positive relationship between information satisfaction and product information
quality.
2. H2. There is a positive relationship between information satisfaction and service information
quality.
H3. There is a positive relationship between information satisfaction and user interface quality.
H4. There is a positive relationship between information satisfaction and security perception.
H5. There is a positive relationship between the product information quality and relational
benefit.
H6. There is a positive relationship between the service information quality and relational benefit
H7. There is a positive relationship between security perception and relational benefit.
H8. There is a positive relationship between site awareness and relational benefit.
H9. There is a positive relationship between relational benefit and site commitment.
H10. There is a positive relationship between information satisfaction and site commitment.
H11. There is a positive relationship between site commitment and purchasing behavior.
Sample size:
The sample size is 107 respondents.
Sampling Design
107 respondents were randomly selected. Respondents were only students who filled a
questionnaire the collected data were carefully assessed to the statistical software i.e. SPSS and
the results were taken as they were required for the analysis of this research study.
Data collection method:
In this study both primary and secondary sources of data will be included. The primary data for
this has been taken by the help of structured questionnaire that proved to be effective in
collection the relevant information; the data of questionnaire was collected from 107 respondents
which served as the primary source of data for the analysis of this research and that lead this
research study to the exploration of the customer choice behavior and customer satisfaction
towards restaurants. At the same time literature review of this research study will provided the
secondary. Source of secondary data which is gathered from published research articles.
Research Model
3. Data Collection
The target population of this study consists of all customers who shop from online stores. It
contains heterogeneous products and these response are reference on the basis of last visited
online store of customer.
The target customers was from Gwalior region.
Measures
When we developed the questionnaire, the multiple-item method was used and each item was
measured based on a five-point Likert scale from ‘‘strongly agree’’ to ‘‘strongly disagree’’.
All operational definitions of the constructs and instrument items are shown in Table .
Table I Descriptive statistics of the respondent profile
Measure items %
Gender Male 70.1
Female 29.9
Age 16-25yrs 86.9
26-34yrs 10.3
35-44yrs 1.9
44+yrs 0.9
Time to use internet ½ hrs per day 15.9
1 hr per day 19.6
2 hrs per day 43
More 21.5
4. Preferred online site Flipkart 57.9
Amazon 30.8
Other 11.2
Variety of products Electronics 35.5
Appliances 3.7
Clothes & Accessories 54.2
Home & Furnitures 2.8
Books & more 2.8
Other 0.9
Table2: AII Measurements of instrument of key constructs
Construct Items (anchors: strongly disagree/strongly agree)
Independent variables
User interface quality 1. This site is convenient to search for product
2. This site is convenient to order a product
3. This site is easy to navigate wanted product
4. This site is user friendly
Product information quality 1. This site provides up-to-date product information
2. This site provides sufficient product information
3. This site presents product information easy to
understand
4. The book information is consistent
5. The book information is playful
6. The book information is relevant
Service information quality 1. This site provides up-to-date service information
2. This site provides sufficient service information
3. This site presents service information easy to understand
4. The service information is consistent
5. The service information is playful
6. The service information is relevant
Site awareness 1. Neighbors know this site very well
2. This site is very famous as an Internet online store
3. This site is known through the advertising media (TV,
newspaper,Internet, etc.)
Security perception 1. My private information is managed securely on this site
5. 2. I am sure that payment information will be protected in
this site
3. This site provides detailed information about security
4. I am afraid that my private information will be used in
an unwanted manner.
Mediators and dependent variable Variables
Information satisfaction 1. I am satisfied with the information service of this site
compared to other shopping sites
2. Information service of this site satisfies my
expectations
3. I am satisfied with the overall information service of
this site
Relational benefit 1. At this site, I am able to reduce the time to purchase
wantedproducts
2. At this site, I am able to reduce efforts to purchase
wanted products
3. At this site, I am able to purchase wanted product that
are hard to purchase at other stores
4. I will receive credible customer service from this site.
Site commitment 1. I will not change my product shopping site in the future
2. I will continuously purchase products at this site in the
future
3. I will recommend this site to other people
4. I will visit this site first when I want to buy products
Purchasing behavior Please mark the frequency of product purchase at this site
in a year
.Reliability of measurement instrument
The Cronbach alpha coefficient was used to assess reliability of the measures (Straub, 1989). As
shown in Appendix 3, reliability coefficients were acceptable for all constructs, ranging from
0.8687 for service information quality to 0.6712 for relational benefit. While all the reliability
figures were higher than 0.6, the lowest acceptable limit for Cronbach’s alpha suggested by Hair
et al. (1998), variables with reliabilities lower than 0.8 deserve further refinement in future
research.
6. REGRESSION ANALYSIS:
1. Impact of independent variables on relational benefit-
The first table is the Model Summary table, as shown below.
Model Summaryb
Mode
l R R Square
Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square
Change
F
Chan
ge df1 df2 Sig. F Change
1 .890a .793 .780 1.75727 .793 63.70
1
6 100 .000
a. Predictors: (Constant), secpertotal, uiqtotal, sattotal, siqtotal, sitawatotal, piqtotal
b. Dependent Variable: relbentotal
This table provides the R and R2 values. The R value represents the simple correlation and is
0.890 (the "R" Column), which indicates a high degree of correlation. The R2 value (the "R
Square" column) indicates how much of the total variation in the dependent variable, relational
benefit, can be explained by the independent variable, user interface qualitys,product information
quality,security perception,site awareness, reliability. In this case, 79.3% can be explained,
which is very large.
The next table is the ANOVA table, which reports how well the regression equation fits the data
(i.e., predicts the dependent variable) and is shown below:
ANOVAa
Model
Sum of
Squares df
Mean
Square F Sig.
1 Regression 1180.247 6 196.708 63.701 .000b
Residual 308.800 100 3.088
Total 1489.047 106
a. Dependent Variable: relbentotal
b. Predictors: (Constant), secpertotal, uiqtotal, sattotal, siqtotal, sitawatotal,
piqtotal
7. This table indicates that the regression model predicts the dependent variable significantly well.
Look at the "Regression" row and the "Sig." column. This indicates the statistical significance of
the regression model that was run. Here, p < 0.0005, which is less than 0.05, and indicates that,
overall, the regression model statistically significantly predicts the outcome variable (i.e., it is a
good fit for the data).
The required table shows that our findings have supported the hypotheses that
Hypothesis 1 proposed that information satisfaction has no impact on relational benefit.
Hypothesis 2 proposed that security perception has no impact on relational benefit.
A multiple regression analysis was conducted to verify this and explore how much variation in
relational benefit could be explained by the variability in different dimensions. Such analysis is
appropriate in the case that there a set of predictor variables (user interface quality, product
information quality, service information quality, site awareness, security perception, information
satisfaction and site commitment) and one response variable (relational benefit). The regression
results shown in Table 3 indicate that the independent variables have a significant and
information satisfaction and security perception have no effect on relational benefit. Therefore,
hypothesis 1 and hypothesis 2 is rejected.
The Coefficients table 5 provides us with the necessary information to predict satisfaction from
independent variables, as well as determine whether independent variables contribute statistically
significantly to the model (by looking at the "Sig." column). Furthermore, we can use the values
in the "B" column under the "Unstandardized Coefficients" column, as shown below:
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 1.191 .521 2.288 .024
sattotal .563 .110 .438 5.126 .000
uiqtotal -.057 .097 -.058 -.587 .559
piqtotal .027 .084 .041 .325 .746
siqtotal .055 .060 .085 .913 .364
sitawatotal -.024 .123 -.020 -.191 .849
secpertotal .435 .096 .454 4.511 .000
a. Dependent Variable: relbentotal
8. Conclusion
We developed and empirically validated a model of consumers’ relational purchasing behavior in
an online shopping context. The key affecting factors of user interface quality, product and
service information quality ,security perception and site awareness were found to have
significant effects on
consumer’s site commitment. Furthermore, we investigated whether information satisfaction and
relational benefit play a significant mediating role on consumers’ relationship purchasing
behavior. In an online
shopping context, the information feature of a shopping site was validated to be an important
factor determining consumers’ site loyalty and decision-making in terms of whether or not they
will shop at the store. This emphasizes the importance of product information quality and user
interface design in the online shopping site development. Other attributes of an online store were
also found to influence a consumer’s perceived relational benefits from online shopping. Service
information quality was found to be the most important factor among them.