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Ma Market Research Project Alliance University, Bangalore 2011 School of Business MR: Marketing Research Impact of Space layout on consumer buying preference. PREPARED BY: GROUP-1 SUNAM PAL CHANDRADEEP BHATTACHATYA Prepared by : SUNAM PAL CHANDRADEEP Page 1
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Ma Market Research Project Alliance University, Bangalore 2011 Table of Contents 1.Introduction .............................................................................................................................. 5 2.Literature Review .................................................................................................................. 5 2.1 Solution Approaches to Automated Space Planning ......................................... 5 2.2 Additive Space Allocation ............................................................................................. 7 2.3 Structure of the program .............................................................................................. 7 2.4 A typical floor plan ........................................................................................................... 7 2.5 Floor plan graph with dual graph .............................................................................. 8 3 Methodology ............................................................................................................................. 9 3. 1 Overview of Work ........................................................................................................... 9 3.2 Sources of data................................................................................................................... 9 3.3 Sample design:- ................................................................................................................. 9 3.4 Sample size ....................................................................................................................... 10 3.5 Target Group ................................................................................................................... 10 3.6 Data collection:- ............................................................................................................. 10 3.7 Type of Research:-......................................................................................................... 10 3.8 Statistical tool used .................................................................................................... 10 4.Questionnaire........................................................................................................................ 15 5.ANALYISIS ................................................................................................................................ 21 5.1 Tools used ......................................................................................................................... 21 5.2 ASSIGNING VALUES TO EACH RATINGS & RANKS ......................................... 21 5.2 Reliability Test ............................................................................................................ 22 5.2.1 XPSS output ............................................................................................................. 22 5.2.2 Interpretation ......................................................................................................... 23 Prepared by : SUNAM PAL CHANDRADEEP Page 2
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Ma Market Research Project Alliance University, Bangalore 2011 5.3 Linear Regression Analysis ......................................................................... 23 5.3.1 Independent Variable .......................................................................... 24 5.3.2 Dependent variable............................................................................... 24 5.3.3 Sample Size ............................................................................................... 24 5.3.4 XPSS OUTPUT .......................................................................................... 25 5.3.6 Interpretation.......................................................................................... 28 5.3.6.1 R square value ..................................................................................... 28 5.3.6.2 T-test ....................................................................................................... 28 5.3.6.3 Significance Level ............................................................................... 28 5.3.6.4 B value & C Value ............................................................................... 28 5.3.6.5 Linear Equations ................................................................................ 28 5.4 Correlation Analysis ...................................................................................... 29 5.4.1 Correlation Variable ............................................................................. 32 5.4.2 Sample Size ............................................................................................... 32 5.4.3 Correlation Matrix XPSS OUTPUT .................................................. 33 5.4.4 Interpretation.......................................................................................... 34 5.5 Kendal’s W-Test ............................................................................................... 34 5.5.1 Variable ...................................................................................................... 35 5.5.2 Sample Size ............................................................................................... 35 5.5.3 XPSS OUTPUT .......................................................................................... 35 5.5.4 Interpretation.......................................................................................... 35 5.6 Central Tendencies......................................................................................... 36 Mean ....................................................................................................................... 36 Median ................................................................................................................... 36 Mode ....................................................................................................................... 36 Range...................................................................................................................... 36 Prepared by : SUNAM PAL CHANDRADEEP Page 3
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Ma Market Research Project Alliance University, Bangalore 2011 1.Introduction Now a days shopping mall attract lots of customer. Sales frequency has increased in shopping mall recently. How does consumer make purchase decision shopping mall? How does purchase in one shopping mall differ from other shopping mall? So we consider two important aspect of consumer decision making shopping mall; one is space and other is design .How does space and design consumer decision making process in shopping mall, we conducted a research on this matter and try to find out related finding regarding this topic. For our research we choose selected shopping mall in Bangalore and tried to find out consumer decision making relative to those shopping mall. 2.Literature Review 2.1 Solution Approaches to Automated Space Planning Kalay (2004) categorizes computational design synthesis methods as: Procedural Methods Heuristic Methods Evolutionary Methods In this categorization, “Procedural Methods” are introduced as first methods to be employed. They leverage our ability, as human designers, to specify local conditions and the ability of the computer to apply or test for these relationships over much larger sets of variables. The basic procedural approach is the attempt to completely enumerate all the possible arrangements of floor plans from a given set of rooms. Then, architects can choose the most appropriate one from those alternatives for a given design project. However, the numbers of possible solutions rise up dramatically by increasing the number of design parameters. Therefore, it is an inefficient approach for computers to try to calculate all the possible solutions. Even if a Prepared by : SUNAM PAL CHANDRADEEP Page 5
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Ma Market Research Project Alliance University, Bangalore 2011 computer can generate a large number of possible solutions, no architect has sufficient time and energy to review all those solutions (Kalay, 2004). Another procedural approach to computerize arranging rooms in a floor plan is to enlist the services of the computer in the layout of spaces in a building according to some rational principles (mostly minimization of distances between spaces that ought to be close to each other). This approach is known as “Space Allocation”. The uses of space allocation approaches however are limited to building types that the main important factor in their design is distances (like schools, hospitals and warehouses) (Kalay, 2004). Attempts to improve space allocation with the help of procedural methods continued by including additional design criteria (e.g., lighting, privacy and orientation) in the decision-making process of placement algorithm. Different “Constraint Satisfaction” methods then introduced to include multiple objectives in space allocation. With some exceptions the results of space allocations with constraint satisfaction methods were poor compared to the results obtained by competent architects. In fact, satisfying more constraints with some sort of satisfying results needs the more heuristic methods of simulation (Kalay, 2004). “Heuristic Methods” are the computational design methods that are inspired by analogies, just like the design synthesis methods that are typically inspired by analogies and guided by the architect‟s own or another designer’s previous experiences. These methods rely on personal and professional expertise accumulated over lifetimes of confronting a variety of design issues (Kalay, 2004). One of the interesting approaches to computerized space layout planning by means of Heuristic Methods was to borrow the idea of simulating space arrangements in layouts from the rules that has derived from other sciences. These methods are known as Final Paper……..…….……………………………...Arch 588- Research Practice 3 Prepared by : SUNAM PAL CHANDRADEEP Page 6
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Ma Market Research Project Alliance University, Bangalore 2011 2.2 Additive Space Allocation An example of a program that has implemented additive methods of space allocation is GRAMPA (for Graph Manipulating Package). Final Paper……..…….……………………………...Arch 588- Research Practice 4 2.3 Structure of the program (Grason, 1971) Grason‟s approach to computerized space planning is based on the methods of solution for the formal class of floor plan design problems. The methods of solution depend on a special linear graph representation for floor plans called the „dual graph‟1 representation. 1 In mathematics, a dual graph of a given planar graph G has a vertex for each plane region of G, and an edge for each edge joining two neighboring regions. The term "dual" is used because this property is symmetric, meaning that if G is a dual of H, then H is a dual of G; in effect, these graphs come in pairs. As shown in Figure 1 a “space” is defined to be either a room or one of the four outside spaces. A problem statement will consist of a set of adjacency and physical dimension requirements that have to be satisfied, and a problem solution is a floor plan that satisfies all of the design requirements. 2.4 A typical floor plan (Grason, 1971) In applying graph theory to floor plan layout, rooms are pictured as labeled nodes possessing certain attributes, such as intended use, area, and shape. Adjacencies between rooms are indicated by drawing lines (edges) connecting the nodes to the corresponding rooms. These notions can be implemented by dealing with the dual graph of a floor plan Final Paper……..…….……………………………...Arch 588- Research Practice 5 Prepared by : SUNAM PAL CHANDRADEEP Page 7
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Ma Market Research Project Alliance University, Bangalore 2011 which is itself treated as a linear graph. An example of such a floor plan graph is shown in Figure 2, with black nodes. In the floor plan graph, “edges” and “nodes” will be called “wall segments” and “corners” respectively. A special dual of the floor plan graph can be obtained by placing a node inside each space and constructing edges to join the nodes of adjacent spaces. This special type of dual graph of the floor plan is the design representation to be used for the class of problems described in this paper. The general idea of its application is to first set down the four nodes and four edges of the dual graph that represent the four outside walls of a building. Then nodes and edges are added one by one to the dual graph in response to design requirements and other considerations until a completed dual graph is obtained. 2.5 Floor plan graph with dual graph (Grason, 1971) The incomplete dual graphs that are produced in the intermediate stages of this design process present special problems. Since edges can be colored, directed, and weighted, it is not always clear whether or not there exists at least one physically realizable floor plan satisfying the relationships expressed in the incomplete dual graph. To treat this problem, appropriate properties of the dual graph representation have been developed and are presented in Grason‟s paper. These include the definitions of “Planarity”, “Well-Formed Nodes”, “Well-Formed Terminal Regions” and “The Turn Concept”. Based on these properties three theorems on physical realizability are established. Final Paper……..…….……………………………...Arch 588- Research Practice 6 The use of these theorems enables the program to configure whether the graph is planer or not. It also makes it possible to generate various possible geometric realizations of the dual graph. A geometric realization of a planar graph is simply one of the possibly many ways in which it can be drawn in a plane. Four different realizations of a particular planar graph are shown in Figure 3. Prepared by : SUNAM PAL CHANDRADEEP Page 8
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Ma Market Research Project Alliance University, Bangalore 2011 3 Methodology 3. 1 Overview of Work So far the task accomplished is is identifying, filtering and filling up questionnaire from respondents which are suitable for the research. The applicants are filtered based on age groups and if they belonged to Bangalore. A broad database was gathered which consists of a pool of applicant who may or may not fall in the target bracket. Amongst these, the potential ones are selected, met and kept track of. The whole idea is to collect as many prospects as possible and then filter them as per the requirements. Source: https://spreadsheets.google.com/spreadsheet/viewform?formkey=dHNSS3B3Sl pIaVJoeXJmLWFmLURkT2c6MQ 3.2 Sources of data Primary data: We mainly collected primary data by taking survey among Alliance student. On the basis of questionnaire we get our primary data. 3.3 Sample design:- The sample design used for the purpose of the research is convenient non- probability sampling. Population is totally unknown we are just taking sample for our research . The sample design used for the purpose of the research was applicants within Bangalore only. It basically comprised of all corporate from manufacturing & IT sector that fill the questionnaire and were ready to give their valuable feedback. Prepared by : SUNAM PAL CHANDRADEEP Page 9
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Ma Market Research Project Alliance University, Bangalore 2011 3.4 Sample size We took 30 samples for our research. 3.5 Target Group 1. Group: Students of Alliance and IT professionals 2. Location: Bangalore 3. Age: 20-30 3.6 Data collection:- Primary data such as name, occupation, gender of the applicant was collected through questionnaire. Data were mainly collected through online. Google docs were used to collect data. The questionnaire had no open ended questions. 3.7 Type of Research:- Causative: Relation between Space layout design and various factors Quantitative: Use of statistical tools Non-probability: Population size unknown 3.8 Statistical tool used Reliability Test Regression Correlation Kendal’s W-test Central Tendencies Mean Median Mode Standard Deviation Variance Prepared by : SUNAM PAL CHANDRADEEP Page 10
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Ma Market Research Project Alliance University, Bangalore 2011 Ranges Skewness Kurtosis Simple percentage analysis Graphical Analyis Frequency table Pi-charts NORMAL PROBABILITY DISTRIBUTION In probability theory, the normal (or Gaussian) distribution, is a continuous probability distribution that is often used as a first approximation to describe real-valued random variables that tend to cluster around a single mean value. The graph of the associated probability density function is “bell”-shaped, and is known as the Gaussian function or bell curve: Where parameter μ is the mean (location of the peak) and σ 2 is the variance (the measure of the width of the distribution). The distribution with μ = 0 and σ 2 = 1 is called the standard normal. BINOMIAL PROBABILITY DISTRIBUTION probability theory and statistics, the binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p. Prepared by : SUNAM PAL CHANDRADEEP Page 11
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Ma Market Research Project Alliance University, Bangalore 2011 Such a success/failure experiment is also called a Bernoulli experiment or Bernoulli trial. In fact, when n = 1, the binomial distribution is a Bernoulli distribution. The binomial distribution is the basis for the popular binomial test of statistical significance Probability mass function In general, if the random variable K follows the binomial distribution with parameters n and p, we write K ~ B(n, p). The probability of getting exactly k successes in n trials is given by the probability mass function: For k = 0, 1, 2, ..., n, where is the binomial coefficient (hence the name of the distribution) "n choose k", also denoted C(n, k), nCk, or nCk. The formula can be understood as follows: we want k successes (pk) and n − k failures (1 − p)n − k. However, the k successes can occur anywhere among the n trials, and there are C(n, k) different ways of distributing k successes in a sequence of n trials. In creating reference tables for binomial distribution probability, usually the table is filled in up to n/2 values. This is because for k > n/2, the probability can be calculated by its complement as So, one must look to a different k and a different p (the binomial is not symmetrical in general). However, its behavior is not arbitrary. There is always an integer m that satisfies As a function of k, the expression ƒ(k; n, p) is monotone increasing for k < m and monotone decreasing for k > m, with the exception of one case where (n + 1)p is an integer. In this case, there are two maximum values for m = (n + 1)p and m − 1. m is known as the most probable (most likely) outcome of Bernoulli trials. Note that the probability of it occurring can be fairly small. The cumulative distribution function can be expressed as: Prepared by : SUNAM PAL CHANDRADEEP Page 12
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Ma Market Research Project Alliance University, Bangalore 2011 where is the "floor" under x, i.e. the greatest integer less than or equal to x. It can also be represented in terms of the regularized incomplete beta function, as follows: For k ≤ np, upper bounds for the lower tail of the distribution function can be derived. In particular, Hoeffdings inequality yields the bound and Chernoffs inequality can be used to derive the bound Moreover, these bounds are reasonably tight when p = 1/2, since the following expression holds for all k ≥ 3n/8 ean and variance If X ~ B(n, p) (that is, X is a binomially distributed random variable), then the expected value of X is and the variance is Prepared by : SUNAM PAL CHANDRADEEP Page 13
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Ma Market Research Project Alliance University, Bangalore 2011 This fact is easily proven as follows. Suppose first that we have a single Bernoulli trial. There are two possible outcomes: 1 and 0, the first occurring with probability p and the second having probability 1 − p. The expected value in this trial will be equal to μ = 1 · p + 0 · (1−p) = p. The variance in this trial is calculated similarly: σ2 = (1−p)2·p + (0−p)2·(1−p) = p(1 − p). The generic binomial distribution is a sum of n independent Bernoulli trials. The mean and the variance of such distributions are equal to the sums of means and variances of each individual trial: Mode and median Usually the mode of a binomial B(n, p) distribution is equal to ⌊(n + 1)p⌋, where ⌊ ⌋ is the floor function. However when (n + 1)p is an integer and p is neither 0 nor 1, then the distribution has two modes: (n + 1)p and (n + 1)p − 1. When p is equal to 0 or 1, the mode will be 0 and n correspondingly. These cases can be summarized as follows: In general, there is no single formula to find the median for a binomial distribution, and it may even be non-unique. However several special results have been established: If np is an integer, then the mean, median, and mode coincide. Any median m must lie within the interval ⌊np⌋ ≤ m ≤ ⌈np⌉. A median m cannot lie too far away from the mean: |m − np| ≤ min{ ln 2, max{p, 1 − p} }. The median is unique and equal to m = round(np) in cases when either p ≤ 1 − ln 2 or p ≥ ln 2 or |m − np| ≤ min{p, 1 − p} (except for the case when p = ½ and n is odd) When p = 1/2 and n is odd, any number m in the interval ½(n − 1) ≤ m ≤ ½(n + 1) is a median of the binomial distribution. If p = 1/2 and n is even, then m = n/2 is the unique median. Prepared by : SUNAM PAL CHANDRADEEP Page 14
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Ma Market Research Project Alliance University, Bangalore 2011 Covariance between two binomials If two binomially distributed random variables X and Y are observed together, estimating their covariance can be useful. Using the definition of covariance, in the case n = 1 we have The first term is non-zero only when both X and Y are one, and μX and μY are equal to the two probabilities. Defining pB as the probability of both happening at the same time, this gives and for n such trials again due to independence If X and Y are the same variable, this reduces to the variance formula given above. 4. Questionnaire Source: https://spreadsheets.google.com/spreadsheet/viewform?formkey=dHNSS3B 3SlpIaVJoeXJmLWFmLURkT2c6MQ 1. NAME * Your Full name 20-25 2. AGE * 3. GENDER * MALE FEMALE Prepared by : SUNAM PAL CHANDRADEEP Page 15
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Ma Market Research Project Alliance University, Bangalore 2011 4. PLACE * Place you are living currently If you are in Bangalore, For how many years you have been staying 5. Which shopping center/Retail Malls you have visited you can choose more than one option FORUM MALL GARUDA MALL CENTRAL GOPALAN MANTRI ROYAL MEENAKSHI MALL SHOPPERS STOP BIG BAZAAR FOOD BAZAAR RELIANCE MART RELIANCE FRESH TOTAL MALL Other: 5.A Your Favorite Mall * 5.B MARITAL STATUS MARRIED UNMARRIED 5.C You stay with Prepared by : SUNAM PAL CHANDRADEEP Page 16
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Ma Market Research Project Alliance University, Bangalore 2011 6.Which cinema multiplex have you visited PVR INOX VISION CINEPOLIS FUN CINEMAS GOPALAN CINEMAS Other: 7. Name the multiplex in Forum Mall 8. TOTAL MALL has its center in Bangalore at 9. CHOOSE THE ODD ONE RELIANCE FRESH FOOD WORLD FOOD BAZAAR BIG BAZAAR 10. You would prefer a shopping mall because Neither Strongly Strongly Agree agree nor Disagree Agree Disagree disagree It has space for parking It has sufficient space to walk & roam around It has place to sit Service help desk is available Close to your home Prepared by : SUNAM PAL CHANDRADEEP Page 17
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Ma Market Research Project Alliance University, Bangalore 2011 11. What makes you visit a shopping mall 1- Very frequently & 5- very rarely? 1 2 3 4 5 Cinema Multiplex Shopping Experience Have food in restaurant Hang around with friends Watch out trade shows 12. How frequently you visit shopping mall 13. With whom do you prefer going to shopping mall FRIEND GIRL FRIEND/BOY FRIEND PARENTS KIDS BROTHERS/SISTERS RELATIVES COLLEAGUES SPOUSE ALONE Prepared by : SUNAM PAL CHANDRADEEP Page 18
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Ma Market Research Project Alliance University, Bangalore 2011 14. Mark your preference to choose a cinema multiplex 1-High Preference 5-Low Preference 1 2 3 4 5 Position of sit from screen Screen Size Sound Quality Space between sits Food stalls & offering outside the cinema Combo offers like Movie ticket + Food Online booking facility 14. Mark your preference while shopping 1-High Preference 2-Low Preference 1 2 3 4 5 Impact of Lighting & background display Sufficient Space to walk inside stores Sufficient Space & width of accelerators Space between two retail stores Adequate space between dining tables in restaurants Prepared by : SUNAM PAL CHANDRADEEP Page 19
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Ma Market Research Project Alliance University, Bangalore 2011 1 2 3 4 5 Easy of security check at entry Easy to locate what you are looking for 15. How important is space layout to you in a shopping mall Rate on a scale of 1 - 10 (1- very Important, 10-Least Important) 1 2 3 4 5 6 7 8 9 10 16. Mark your preference 1 2 3 4 5 6 7 8 9 10 Service level Display layout 17. Mark your preference 1 2 3 4 5 6 7 8 9 10 Space layout Display layout 19. Mark your preference 1 2 3 4 5 6 7 8 9 10 Gopalan Cinemas Cinepolis Cinemas 20. Your confidence level while filling up the form 100% 95-100% 80-90% 50-80% below 50% Prepared by : SUNAM PAL CHANDRADEEP Page 20
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Ma Market Research Project Alliance University, Bangalore 2011 5.ANALYISIS 5.1 Tools used Reliability Test Regression Correlation Kendal’s W Test Central Tendencies Perecentage & Graphical Analysis 5.2 ASSIGNING VALUES TO EACH RATINGS & RANKS RATING VALUE ATTACHED Strongly Agree 10 Agree 8 Neither Agree nor 6 Disagree Disagree 4 Strongly Disagree 2 Rank-1 10 Rank-2 8 Rank-3 6 Rank-4 4 Rank-5 2 Prepared by : SUNAM PAL CHANDRADEEP Page 21
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Ma Market Research Project Alliance University, Bangalore 2011 5.2 Reliability Test You learned in the Theory of Reliability that its not possible to calculate reliability exactly. Instead, we have to estimate reliability, and this is always an imperfect endeavor. Here, I want to introduce the major reliability estimators and talk about their strengths and weaknesses. There are four general classes of reliability estimates, each of which estimates reliability in a different way. They are: Inter-Rater or Inter-Observer Reliability Used to assess the degree to which different raters/observers give consistent estimates of the same phenomenon. Test-Retest Reliability Used to assess the consistency of a measure from one time to another. Parallel-Forms Reliability Used to assess the consistency of the results of two tests constructed in the same way from the same content domain. Internal Consistency Reliability Used to assess the consistency of results across items within a test. 5.2.1 XPSS output Case Processing Summary N % Cases Valid 27 89.3 Excluded 3 10.7 a Total 30 100.0 a. Listwise deletion based on all variables in the procedure. Reliability Statistics Prepared by : SUNAM PAL CHANDRADEEP Page 22
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Ma Market Research Project Alliance University, Bangalore 2011 Cronbachs Alpha Based on Cronbachs Standardize N of Alpha d Items Items .238 .284 16 Summary Item Statistics Minimu Maximu Maximum / Varianc N of Mean m m Range Minimum e Items Item Means 7.958 3.667 9.333 5.667 2.545 2.265 16 Item Variances 5.375 1.000 17.333 16.333 17.333 37.583 16 Inter-Item 1.310 -4.667 17.333 22.000 -3.714 12.924 16 Covariances Inter-Item .323 -1.000 1.000 2.000 -1.000 .386 16 Correlations 5.2.2 Interpretation Around 27 observations are valid. Around 3 observations has to be excluded. Cronbach’s alpha is 0.284 which is <0.5 and close to zero shows that the data are significant. Reliability = 89.3% ( > 50%) 5.3 Linear Regression Analysis In statistics, regression analysis includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables — that is, the average value of the Prepared by : SUNAM PAL CHANDRADEEP Page 23
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Ma Market Research Project Alliance University, Bangalore 2011 dependent variable when the independent variables are held fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function, which can be described by a probability distribution. 5.3.1 Independent Variable Presence of Multiplex ( X1) Shopping experience ( X2) Hanging around with friends ( X3 ) Trade show (X4) Parking ( X5) Space to walk around ( X6 ) Space to sit ( X7) Help Desk service ( X8) Closeness to home ( X9) Lighting ( X10) Space between stores ( X11) Width of accelerators ( X12) Space inside retail outlets ( X13) Dining table space ( X14 ) Ease of security check ( X15 ) Easy to locate products ( X16 ) 5.3.2 Dependent variable Importance of Space layout Design ( Y ) 5.3.3 Sample Size 30 samples Prepared by : SUNAM PAL CHANDRADEEP Page 24
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Ma Market Research Project Alliance University, Bangalore 2011 5.3.4 XPSS OUTPUT Model Summaryb Std. Error of the Model R R Square Adjusted R Square Estimate a 1 .804 .747 .746 1.858 a. Predictors: (Constant), EASELOCATE, CLSOETOHOME, ACCELERATORSWIDTH, SPACESIT, TRADESHOW, SPACEWALKAROUND, DINNINGTABLESPACe, MULTIPLEX, HANGAROUND, SHOPPINGEXPERIENCE, RESTAURANTS, PARKING, SECUTITYSCHECK, HELPDESK, SPACESTORES, LIGHTING, RETAILOUTLETSPACE b. Dependent Variable: IMPLAYOUT b ANOVA Model Sum of Squares df Mean Square F Sig. 1 Regression 63.122 17 3.713 1.076 .046 Residual 34.508 10 3.451 Total 97.630 27 a. Predictors: (Constant), EASELOCATE, CLSOETOHOME, ACCELERATORSWIDTH, SPACESIT, TRADESHOW, SPACEWALKAROUND, DINNINGTABLESPACe, MULTIPLEX, HANGAROUND, SHOPPINGEXPERIENCE, RESTAURANTS, PARKING, SECUTITYSCHECK, HELPDESK, SPACESTORES, LIGHTING, RETAILOUTLETSPACE b. Dependent Variable: IMPLAYOUT R$esiduals Statisticsa Minimum Maximum Mean Std. Deviation N Predicted Value 4.51 11.08 8.30 1.501 29 Residual -2.084 2.040 .000 1.110 29 Std. Predicted Value -2.475 1.823 .000 .982 29 Std. Residual -1.122 1.098 .000 .598 29 a. Dependent Variable: IMPLAYOUT Prepared by : SUNAM PAL CHANDRADEEP Page 25
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Ma Market Research Project Alliance University, Bangalore 2011 Charts Prepared by : SUNAM PAL CHANDRADEEP Page 27
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Ma Market Research Project Alliance University, Bangalore 2011 5.3.6 Interpretation 5.3.6.1 R square value R Square value = 0.746 It shows that the relationship is 74.6% accurate to define the existing relationship between Y & X[1,2,3…..16]. 5.3.6.2 T-test The following independent variable had t-value > 0.5. X5, X6, X7, X8, X9,X11, X12, X13, X14,X15 & X16 Which say that they have a greater impact on the output and forms a strong relation with it. 5.3.6.3 Significance Level Out of above X5 > 0.05, hence it is not significant 5.3.6.4 B value & C Value Slopes X9,X13,X15 -> they are negatively related Slopes X6,X7,X8,X11,X12,X14,X16- > they are Positively related Constant -> It is negative 5.3.6.5 Linear Equations Y = F(X) + C C = -3.611 F(X) = 0.028 X6 + 0.151 X7 + 0.465 X8 -0.354 X9 + 0.24 X11 + 1.116 X12 - 1.99 X13 + 0.329 X14 -0.361 X15+0.179 X16 Prepared by : SUNAM PAL CHANDRADEEP Page 28
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Ma Market Research Project Alliance University, Bangalore 2011 5.4 Correlation Analysis A correlation function is the correlation between random variables at two different points in space or time, usually as a function of the spatial or temporal distance between the points. Correlation functions of different random variables are sometimes called cross correlation functions to emphasize that different variables are being considered and because they are made up of cross correlations. Correlation functions are a useful indicator of dependencies as a function of distance in time or space, and they can be used to assess the distance required between sample points for the values to be effectively uncorrelated. In addition, they can form the basis of rules for interpolating values at points for which there are observations. For random variables X(s) and X(t) at different points s and t of some space, the correlation function is where is described in the article on correlation. In this definition, it has been assumed that the stochastic variable is scalar-valued. If it is not, then more complicated correlation functions can be defined. For example, if one has a vector Xi(s), then one can define the matrix of correlation functions Regression Analysis In linear regression, the model specification is that the dependent variable, yi is a linear combination of the parameters (but need not be linear in the independent variables). For example, in simple linear regression for modeling n data points there is one independent variable: xi, and two parameters, β0 and β1 : straight line: Prepared by : SUNAM PAL CHANDRADEEP Page 29
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Ma Market Research Project Alliance University, Bangalore 2011 In multiple linear regression, there are several independent variables or functions of independent variables. For example, adding a term in xi2 to the preceding regression gives: parabola: This is still linear regression; although the expression on the right hand side is quadratic in the independent variable xi, it is linear in the parameters β0, β1 and β2.In both cases, is an error term and the subscript i indexes a particular observation. Given a random sample from the population, we estimate the population parameters and obtain the sample linear regression model: The residual, , is the difference between the value of the dependent variable predicted by the model, and the true value of the dependent variable yi. One method of estimation is ordinary least squares. This method obtains parameter estimates that minimize the sum of squared residuals, SSE: Minimization of this function results in a set of normal equations, a set of simultaneous linear equations in the parameters, which are solved to yield the parameter estimators, . Prepared by : SUNAM PAL CHANDRADEEP Page 30
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Ma Market Research Project Alliance University, Bangalore 2011 Illustration of linear regression on a data set. In the case of simple regression, the formulas for the least squares estimates are where is the mean (average) of the x values and is the mean of the y values. See simple linear regression for a derivation of these formulas and a numerical example. Under the assumption that the population error term has a constant variance, the estimate of that variance is given by: This is called the mean square error (MSE) of the regression. The standard errors of the parameter estimates are given by Under the further assumption that the population error term is normally distributed, the researcher can use these estimated standard errors to create confidence intervals and conduct hypothesis tests about the population parameters. Prepared by : SUNAM PAL CHANDRADEEP Page 31
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Ma Market Research Project Alliance University, Bangalore 2011 Correlation The population correlation coefficient ρX,Y between two random variables X and Y with expect values μX and μY and standard deviations σX and σY is defined as: where E is the expected value operator, cov means covariance, and, corr a widely used alternative notation for Pearsons correlation. The Pearson correlation is defined only if both of the standard deviations are finite and both of them are nonzero. It is a corollary of the Cauchy–Schwarz inequality that the correlation cannot exceed 1 in absolute value. The correlation coefficient is symmetric: corr(X,Y) = corr(Y,X). 5.4.1 Correlation Variable Only those variable that are a part of regression equations are taken into account Parking ( X5) Space to walk around ( X6 ) Space to sit ( X7) Help Desk service ( X8) Closeness to home ( X9) Space between stores ( X11) Width of accelerators ( X12) Space inside retail outlets ( X13) Dining table space ( X14 ) Ease of security check ( X15 ) Easy to locate products ( X16 ) Importance of Space layout Design ( Y ) 5.4.2 Sample Size 30 samples Prepared by : SUNAM PAL CHANDRADEEP Page 32
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Ma Market Research Project Alliance University, Bangalore 2011 * EASELOCAT .383 .152 -.071 .136 .168 .152 .015 .319 .526 .301 1.000 E .057 .444 .709 .507 .441 .435 .937 .100 .012 .151 . 24 25 26 22 20 26 23 23 22 22 27 * IMPLAYOUT .347 .080 .400 .149 .073 .328 .197 .131 .100 -.096 -.022 .066 .668 .027 .453 .706 .073 .282 .476 .605 .636 .904 24 25 26 21 21 26 23 23 23 21 26 5.4.4 Interpretation The following parameters were strongly correlated with correlation coefficient value above R > 0.50 and significance value < 0.06 Parking & retail space are positively correlated Parking & space to walk are positively correlated Space to walk & space to sit are positively correlated Closesness to home & service are positively correlated Service & store space are positively correlated Retail space & dinning space are positively correlated Retail space & accelerator width are positively correlated 5.5 Kendal’s W-Test Kendalls W (also known as Kendalls coefficient of concordance) is a non- parametric statistics. It is a normalization of the statistic of the Friedman test, and can be used for assessing agreement among raters. Kendalls W ranges from 0 (no agreement) to 1 (complete agreement). Suppose, for instance, that a number of people have been asked to rank a list of political concerns, from most important to least important. Kendalls W can be calculated from these data. If the test statistic W is 1, then all the survey respondents have been unanimous, and each respondent has assigned the same order to the list of concerns. If W is 0, then there is no overall trend of agreement among the respondents, and their responses may be regarded as essentially random. Intermediate values of W indicate a greater or lesser degree of unanimity among the various responses. Prepared by : SUNAM PAL CHANDRADEEP Page 34
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Ma Market Research Project Alliance University, Bangalore 2011 While tests using the standard Pearson correlation coefficient assume normally distributed values and compare two sequences of outcomes at a time, Kendalls W makes no assumptions regarding the nature of the probability distribution and can handle any number of distinct outcomes. 5.5.1 Variable Importance of Space layout Design ( Y ) 5.5.2 Sample Size 30 samples 5.5.3 XPSS OUTPUT ANOVA with Friedmans Test Sum of Mean Friedmans Squares df Square Chi-Square Sig Between People 50.042 2 25.021 Within Between 101.917a 15 6.794 20.486 .154 People Items Residual 121.958 30 4.065 Total 223.875 45 4.975 Total 273.917 47 5.828 Grand Mean = 7.96 Kendalls coefficient of concordance W = .772. 5.5.4 Interpretation The grand weighted mean is 7.96, which states that average scores rated to importance of space layout design is 7.9. However the kendals’ W test, say that 77.2% of ranking provided by respondents are inclined to each other & is jutify enough to satisfy the relationship as it is geater than 0.5. W>0.5 Prepared by : SUNAM PAL CHANDRADEEP Page 35
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Ma Market Research Project Alliance University, Bangalore 2011 5.6 Central Tendencies The terms mean, median, mode, and range describe properties of statistical distributions. In statistics, a distribution is the set of all possible values for terms that represent defined events. The value of a term, when expressed as a variable, is called a random variable. Mean The most common expression for the mean of a statistical distribution with a discrete random variable is the mathematical average of all the terms. To calculate it, add up the values of all the terms and then divide by the number of terms. This expression is also called the arithmetic mean. There are other expressions for the mean of a finite set of terms but these forms are rarely used in statistics. Median The median of a distribution with a discrete random variable depends on whether the number of terms in the distribution is even or odd. If the number of terms is odd, then the median is the value of the term in the middle. This is the value such that the number of terms having values greater than or equal to it is the same as the number of terms having values less than or equal to it. Mode The mode of a distribution with a discrete random variable is the value of the term that occurs the most often. It is not uncommon for a distribution with a discrete random variable to have more than one mode, especially if there are not many terms. This happens when two or more terms occur with equal frequency, and more often than any of the others. A distribution with two modes is called bimodal. Range The range of a distribution with a discrete random variable is the difference between the maximum value and the minimum value. For a distribution with a continuous random variable, the range is the difference between the two extreme points on the distribution curve, where the value of the function falls Prepared by : SUNAM PAL CHANDRADEEP Page 36
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Ma Market Research Project Alliance University, Bangalore 2011 to zero. For any value outside the range of a distribution, the value of the function is equal to 0 5.6.1 Variable Importance of Space layout Design ( Y ) 5.6.2 Sample Size 30 samples 5.6.3 XPSS OUTPUT Statistics IMPLAYOUT N Valid 27 Missing 2 Mean 8.30 Std. Error of Mean .373 Median 8.64a Mode 8 Std. Deviation 1.938 Variance 3.755 Skewness -1.653 Std. Error of Skewness .448 Kurtosis 2.709 Std. Error of Kurtosis .872 Range 7 Minimum 3 Maximum 10 Sum 224 Percentile 25 7.59b s 50 8.64 75 9.65 a. Calculated from grouped data. b. Percentiles are calculated from grouped data. Prepared by : SUNAM PAL CHANDRADEEP Page 37
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Ma Market Research Project Alliance University, Bangalore 2011 IMPLAYOUT Cumulative Frequency Percent Valid Percent Percent Valid 3 2 6.9 7.4 7.4 5 1 3.4 3.7 11.1 7 1 3.4 3.7 14.8 8 10 34.5 37.0 51.9 9 4 13.8 14.8 66.7 10 9 31.0 33.3 100.0 Total 27 93.1 100.0 Missing 6 1 3.4 System 1 3.4 Total 2 6.9 Total 29 100.0 5.6.4 Interpretation The average rating score is is 8.3 on an 1-10 scale. People have rated ‘8’ for maximum times with frquency of 10. 50% of observation lies below 8.6 and 50% lies above it. 25% of observation lies below 7.6, 25% between 7.6 to 8.6, 25% between 8.6 to 9.65 & rest 25% between 9.6 to 10 The expected deviation can be expected to be 1.9 from mean. The range of rating is 7. The maximum rating has been 10, where as minimum rating has been 3. Skewness of mean from median is 0.44. 37% of sample people have rated 8 33% of sample people have rated 10 Lease rating rating were given as 5 & 7 that is around just 3.7% Prepared by : SUNAM PAL CHANDRADEEP Page 38
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Ma Market Research Project Alliance University, Bangalore 2011 5.7 Graphical percentage Analysis & frequency table. 5.7.1 XPSS OUTPUT RESTAURANTS Cumulative Frequency Percent Valid Percent Percent Valid 2 2 6.9 9.5 9.5 4 7 24.1 33.3 42.9 8 7 24.1 33.3 76.2 10 5 17.2 23.8 100.0 Total 21 72.4 100.0 Missing 6 7 24.1 System 1 3.4 Total 8 27.6 Total 29 100.0 HANGAROUND Cumulative Frequency Percent Valid Percent Percent Valid 4 2 6.9 8.3 8.3 8 8 27.6 33.3 41.7 10 14 48.3 58.3 100.0 Total 24 82.8 100.0 Missing 6 4 13.8 System 1 3.4 Total 5 17.2 Total 29 100.0 Prepared by : SUNAM PAL CHANDRADEEP Page 39
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Ma Market Research Project Alliance University, Bangalore 2011 TRADESHOW Cumulative Frequency Percent Valid Percent Percent Valid 1 3 10.3 12.0 12.0 2 12 41.4 48.0 60.0 4 5 17.2 20.0 80.0 8 5 17.2 20.0 100.0 Total 25 86.2 100.0 Missing 6 3 10.3 System 1 3.4 Total 4 13.8 Total 29 100.0 SPACEWALKAROUND Cumulative Frequency Percent Valid Percent Percent Valid 8 12 41.4 46.2 46.2 10 14 48.3 53.8 100.0 Total 26 89.7 100.0 Missing 6 2 6.9 System 1 3.4 Total 3 10.3 Total 29 100.0 SPACESIT Cumulative Frequency Percent Valid Percent Percent Valid 4 1 3.4 3.7 3.7 8 14 48.3 51.9 55.6 10 12 41.4 44.4 100.0 Total 27 93.1 100.0 Missing 6 1 3.4 System 1 3.4 Total 2 6.9 Total 29 100.0 Prepared by : SUNAM PAL CHANDRADEEP Page 40
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Ma Market Research Project Alliance University, Bangalore 2011 HELPDESK Cumulative Frequency Percent Valid Percent Percent Valid 4 2 6.9 9.1 9.1 8 12 41.4 54.5 63.6 10 8 27.6 36.4 100.0 Total 22 75.9 100.0 Missing 6 6 20.7 System 1 3.4 Total 7 24.1 Total 29 100.0 CLSOETOHOME Cumulative Frequency Percent Valid Percent Percent Valid 4 5 17.2 23.8 23.8 8 6 20.7 28.6 52.4 10 10 34.5 47.6 100.0 Total 21 72.4 100.0 Missing 6 7 24.1 System 1 3.4 Total 8 27.6 Total 29 100.0 LIGHTING Cumulative Frequency Percent Valid Percent Percent Valid 4 1 3.4 4.2 4.2 8 12 41.4 50.0 54.2 10 11 37.9 45.8 100.0 Total 24 82.8 100.0 Missing 6 4 13.8 System 1 3.4 Total 5 17.2 Total 29 100.0 Prepared by : SUNAM PAL CHANDRADEEP Page 41
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Ma Market Research Project Alliance University, Bangalore 2011 SPACESTORES Cumulative Frequency Percent Valid Percent Percent Valid 8 11 37.9 40.7 40.7 10 16 55.2 59.3 100.0 Total 27 93.1 100.0 Missing 6 1 3.4 System 1 3.4 Total 2 6.9 Total 29 100.0 DINNINGTABLESPACe Cumulative Frequency Percent Valid Percent Percent Valid 2 1 3.4 4.3 4.3 8 13 44.8 56.5 60.9 10 9 31.0 39.1 100.0 Total 23 79.3 100.0 Missing 6 5 17.2 System 1 3.4 Total 6 20.7 Total 29 100.0 SECUTITYSCHECK Cumulative Frequency Percent Valid Percent Percent Valid 4 1 3.4 4.5 4.5 8 11 37.9 50.0 54.5 10 10 34.5 45.5 100.0 Total 22 75.9 100.0 Missing 6 6 20.7 System 1 3.4 Total 7 24.1 Total 29 100.0 RETAILOUTLETSPACE Prepared by : SUNAM PAL CHANDRADEEP Page 42
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Ma Market Research Project Alliance University, Bangalore 2011 Cumulative Frequency Percent Valid Percent Percent Valid 2 1 3.4 4.2 4.2 4 6 20.7 25.0 29.2 8 10 34.5 41.7 70.8 10 7 24.1 29.2 100.0 Total 24 82.8 100.0 Missing 6 4 13.8 System 1 3.4 Total 5 17.2 Total 29 100.0 ACCELERATORSWIDTH Cumulative Frequency Percent Valid Percent Percent Valid 2 1 3.4 4.2 4.2 4 4 13.8 16.7 20.8 8 9 31.0 37.5 58.3 10 10 34.5 41.7 100.0 Total 24 82.8 100.0 Missing 6 4 13.8 System 1 3.4 Total 5 17.2 Total 29 100.0 EASELOCATE Cumulative Frequency Percent Valid Percent Percent Valid 4 2 6.9 7.4 7.4 8 6 20.7 22.2 29.6 10 19 65.5 70.4 100.0 Total 27 93.1 100.0 Missing 6 1 3.4 System 1 3.4 Total 2 6.9 Total 29 100.0 Prepared by : SUNAM PAL CHANDRADEEP Page 43
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Ma Market Research Project Alliance University, Bangalore 2011 5.7.2 Google Docs output Source: https://spreadsheets.google.com/spreadsheet/viewform?formkey=dHNSS3B 3SlpIaVJoeXJmLWFmLURkT2c6MQ AGE Below 18 0% 18-20 0% 20-25 86% 25-30 14% above 30 0 0% GENDER MALE 79% FEMALE 21% PLACE BANGALORE 82% Not Bangalore 18% Prepared by : SUNAM PAL CHANDRADEEP Page 44
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Ma Market Research Project Alliance University, Bangalore 2011 Shopping center/Retail Malls have visited Shopping Mall frequency % FORUM MALL 25 89% GARUDA MALL 25 89% CENTRAL 22 79% GOPALAN 17 61% MANTRI 17 61% ROYAL MEENAKSHI MALL 12 43% SHOPPERS STOP 24 86% BIG BAZAAR 25 89% FOOD BAZAAR 17 61% RELIANCE MART 14 50% RELIANCE FRESH 20 71% TOTAL MALL 22 79% Other 5 18% Prepared by : SUNAM PAL CHANDRADEEP Page 45
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Ma Market Research Project Alliance University, Bangalore 2011 Favorite Mall Shopping Mall frequency % OTHER 1 4% GOPALAN 0 0% GARUDA 5 18% MEENAKSHI 2 7% SHOPPERS STOP 2 7% CENTRAL 7 25% MANTRI 2 7% TOTAL 0 0% FORUM MALL 9 32% Prepared by : SUNAM PAL CHANDRADEEP Page 46
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Ma Market Research Project Alliance University, Bangalore 2011 MARITAL STATUS MARRIED 1 4% UNMARRIED 24 86% Cinema multiplex have you visited Shopping Mall frequency % PVR 24 86% INOX 19 68% VISION 11 39% CINEPOLIS 13 46% FUN CINEMAS 12 43% GOPALAN CINEMAS 12 43% Other 4 14% People may select more than one checkbox, so percentages may add up to more than 100%. Prepared by : SUNAM PAL CHANDRADEEP Page 47
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Ma Market Research Project Alliance University, Bangalore 2011 CHOOSE THE ODD ONE RELIANCE FRESH 3 11% FOOD WORLD 5 18% FOOD BAZAAR 1 4% BIG BAZAAR 19 68% 6.FINDINGS Reliability = 89.3% ( > 50%) R Square value = 0.746 Linear Equations Y = F(X) + C C = -3.611 F(X) = 0.028 X6 + 0.151 X7 + 0.465 X8 -0.354 X9 + 0.24 X11 + 1.116 X12 - 1.99 X13 + 0.329 X14 -0.361 X15+0.179 X16 Parking & retail space are positively correlated Parking & space to walk are positively correlated Space to walk & space to sit are positively correlated Closesness to home & service are positively correlated Service & store space are positively correlated Retail space & dinning space are positively correlated Retail space & accelerator width are positively correlated Prepared by : SUNAM PAL CHANDRADEEP Page 48
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Ma Market Research Project Alliance University, Bangalore 2011 The average rating score is is 8.3 on an 1-10 scale. People have rated ‘8’ for maximum times with frquency of 10. 50% of observation lies below 8.6 and 50% lies above it. 25% of observation lies below 7.6, 25% between 7.6 to 8.6, 25% between 8.6 to 9.65 & rest 25% between 9.6 to 10 The expected deviation can be expected to be 1.9 from mean. The range of rating is 7. The maximum rating has been 10, where as minimum rating has been 3. Skewness of mean from median is 0.44. 37% of sample people have rated 8 33% of sample people have rated 10 Lease rating rating were given as 5 & 7 that is around just 3.7% The grand weighted mean is 7.96, which states that average scores rated to importance of space layout design is 7.9. However the kendals’ W test, say that 77.2% of ranking provided by respondents are inclined to each other & is jutify enough to satisfy the relationship as it is geater than 0.5. 7.Learning Outcome How space layout is related to buying behaviour Varius factors related to space layout Relationship between space layout design and various other factors Concordance in ratings Corelation between various factors Reliability of respondents Descrptive statistics Prepared by : SUNAM PAL CHANDRADEEP Page 49
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Ma Market Research Project Alliance University, Bangalore 2011 8.Conclusion Space layout design is an important parameter that enhances buying behaviour inside a retail mall. Parking space,retail outlets space,dinning space,width of accelerators.closeness to home add value to customer percieved value and thus enhances buying behaviour. Prepared by : SUNAM PAL CHANDRADEEP Page 50
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Ma Market Research Project Alliance University, Bangalore 2011 APPENDIX-1 LIST OF RESPONDENTS 1. NAME 2. AGE 3. GENDER 4. PLACE SUNAM PAL 20-25 MALE BANGALORE hemant kumar 20-25 MALE Not Bangalore Ghulam 20-25 MALE BANGALORE BHAVYA JANARDHAN 20-25 FEMALE BANGALORE aditya narayan patra 20-25 MALE Not Bangalore AVR PHANIKRISHNA M 20-25 MALE BANGALORE Rohan Prasad 20-25 MALE Not Bangalore saumya shukla 20-25 FEMALE BANGALORE RITUPARNA DUTTA 20-25 FEMALE BANGALORE saswat kumar 20-25 MALE Not Bangalore DILIP KUMAT 25-30 MALE BANGALORE sandeep almiya 20-25 MALE BANGALORE Ritesh Kumar Agrawal 20-25 MALE BANGALORE Debjan Bhowmik 20-25 MALE BANGALORE C. Bhattacharya 20-25 MALE BANGALORE Nitesh Tripathi 20-25 MALE Not Bangalore ANIRBAN KAUSHIK 25-30 MALE BANGALORE Vinyith Sisinty 20-25 MALE BANGALORE Ujjawal Kumar 20-25 MALE BANGALORE Rishabh Jain 20-25 MALE BANGALORE Akshay Modi 25-30 MALE BANGALORE Anupriya Verma 20-25 FEMALE BANGALORE PUSHPANJALI KUMARI 20-25 FEMALE BANGALORE IRFAN HABIB 25-30 MALE BANGALORE Subodh 20-25 MALE BANGALORE Vishal Janendra 20-25 MALE BANGALORE Kiran Jacob 20-25 MALE BANGALORE pavithra 20-25 FEMALE BANGALORE Anshuman 20-25 MALE BANGALORE Prepared by : SUNAM PAL CHANDRADEEP Page 51
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Ma Market Research Project Alliance University, Bangalore 2011 APPENDIX-1 RESPONSES Source: https://spreadsheets.google.com/spreadsheet/viewform?formkey=dHNSS3B 3SlpIaVJoeXJmLWFmLURkT2c6MQ 10. You would prefer a shopping mall because - It has space for parking Strongly Agree 10 36% Agree 15 54% Neither agree nor disagree 3 11% Disagree 0 0% Strongly Disagree 0 0% 10. You would prefer a shopping mall because - It has sufficient space to walk & roam around Strongly Agree 15 54% Agree 11 39% Neither agree nor disagree 2 7% Disagree 0 0% Strongly Disagree 0 0% 10. You would prefer a shopping mall because - It has place to sit Strongly Agree 10 36% Agree 14 50% Neither agree nor disagree 1 4% Disagree 1 4% Strongly Disagree 2 7% 10. You would prefer a shopping mall because - Service help desk is available Strongly Agree 6 21% Agree 12 43% Neither agree nor disagree 7 25% Disagree 2 7% Strongly Disagree 1 4% Prepared by : SUNAM PAL CHANDRADEEP Page 52
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