Business Analytics

278 Pages·2012·5.25 MB·English

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Kotler Srinivasan Center for Research in Marketing
2nd INTERNATIONAL CONFERENCE ON
BUSINESS ANALYTICS
December 22-23, 2012

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CONTENT
1 A Comparison of Reflective/Formative Second Factor Models with the Schmid Leiman Factor Structure 5
Piyush Sharma, Hong Kong Polytechnic University, Hong Kong
Bharadhwaj Sivakumaran, Great Lakes Institute of Management, Chennai
Geetha Mohan, SSN College of Engineering, Chennai
2 Rethinking marketing and IT relationship 14
Rajesh RadhaKrishnan, IBM Global Technology Services
P. K. Kannan Robert H. Smith School of Business
3 A Study on customers profiling, competitors mapping and usage pattern analysis from various users segments of Anti- 20
Rabies Vaccine with specific reference to brand RAKSHARAB from Indian Immunologicals Limited in Canine Practicing in
India
Dr. Bimal Kumar Choudhuhry & Ms.Sinorita Dash
4 Order-splitting vs. the postponement strategy for a third-party managed global supply chain 55
Damodar Y. Golha: Haworth College of Business,Western Michigan University
Snehamay Banerje: School of Business – Camden, The State University of NJ
5 Forecasting practices in agrochemical industry in India. 60
Rakesh Singh & Vaidy Jayaraman, Great Lakes Institute of Management
6 Impact of Hospital Service Quality Dimensions on Customer Loyalty from the Patients perspective 77
Shankar MM: Mentor – Research and Training, Synthesis Research Solutions
Roopa BL: Faculty – Vivekananda Institute of Technology,Dept of Management studies
7 Promotional Strategies of Apparels in Selected Retail Stores : A Study on Private Labels 100
Dr.KompalliSasi Kumar, Associate Professor-Finance, Siva Sivani IM
Dr.Jacqueline Williams, Professor & HOD, CMR Technical Campus
Mr.Suneel. S, Assistant Professor, CMR Technical Campus
8 Driving Customer Experience Management through Business Analytics 119
Rangin Lahiri, Senior Manager, Cognizant Business Consulting
Indranil Ghosh,Consultant, Cognizant Business Consulting
9 Social Media Analytics: A study of select Indian banks 128
Sireesha Pulipati, Research Scholar, School of Management Studies, University of Hyderabad
10 A Fuzzy Logic Based Model for Analysis of a Research Design for its Suitability to Avoid Non-Sampling Errors in Market 146
Research
Mukesh Kumar Rohil, Birla Institute of Technolgy and Science, Pilani
11 Advertising on Mobile Phones in India: Spread and Areas of Control 149
Syed Muzammiluddin, Assistant Professor in Badruka Institute of Foreign Trade
12 An Operational Approach to Crop Forecasting: The Case of JISL 157
Dr Rakesh Singh
Prof Piyush Shah
13 Factors affecting choice of Global vs. Local Apparel Brands: An Empirical study in Indian Context 164
Aditi Vidyarthi,Ph.D. Scholar in Business,Department of Business Economics and Management
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Satya Bhushan Dash, Associate Professor Indian Institute of Management
14 A Study of Factors at Bank Level Affecting the Non-Performing Assets of Bank 192
Nisha N Nair, Amirtha School of Business
Rakesh Solanki, Amirtha School of Business
Amalendu Jyotishi, Amirtha School of Business
15 Business Analytics- Scenario in India 202
Inder Deep Singh,Great Lakes Institute of Management.
Vidhi Gupta,Great Lakes Institute Of Management
Deepak Mendiratta,Great Lakes Institute Of Management
16 Alternative Goodness of Fit for Continuous Dependent Variable 206
Sandeep Das, Manager, Analytics Genpact Kolkata, India
17 Cash Flow Modeling and Risk Mapping in Public Cloud Computing- An Evolutionary Approach 215
Easwar Krishna Iyer, Great Lakes Institute of Management, Chennai, India
Tapan Panda, Great Lakes Institute of Management, Chennai, India
18 Estimating Cost of Delays due to Over Dimension Cargo (ODC) in Power Projects: A Case Study of Power Grid 227
Corporation of India Ltd.
Prof Vikas Prakash
Mr Ranjith Vaidooriam
19 Green IT, ROI and Sustainability - in the Indian context 257
Atul Goyal, Graduates of Great Lakes Institute of Management, Patriots
Sweta Kanumuri, Graduates of Great Lakes Institute of Management, Patriots
Syed Zohob, Graduates of Great Lakes Institute of Management, Patriots
Arjun Chakraverti, Visiting Faculty, Great Lakes Institute of Management
Purba H. Rao, Visiting Faculty, Great Lakes Institute of Management

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A Comparison of Reflective/Formative Second Factor Models
with the Schmid Leiman Factor Structure

Piyush Sharma, Hong Kong Polytechnic University, Hong Kong
Bharadhwaj Sivakumaran, Great Lakes Institute of Management, Chennai
Geetha Mohan, SSN College of Engineering, Chennai

INTRODUCTION

A common issue in structural equation modeling is the use of second order factor models (e.g.
Agarwal et al. 2009). A second order factor is one that has no indicator variables. In a second order
factor model, typically, there is correlation amongst some first-order factors and this correlation is
attributed to the fact that these are driven by something above, a “super factor” or what is usually
termed a second order factor (Kline 2005) that is theoretically superior (Rindskopf and Rose 1988).
Within the genre of second order factor models, one has a choice of using reflective or formative
second order factor models (e.g., Bennett and Ali-choudhury 2009). However, in reality, most work
uses standard reflective first order models as indicated in Figure 1.

Figure 1 - Formative vs. Reflective First Order Models

Formative
Social Factor Reflective
Social Factor

E E E
1 2 3
E1 E2 E3

Jarvis et al. (2003) discuss the possibility of formative second order factors, yet few have actually
explored it empirically. In fact, although a viable alternative to the traditional reflective second
order factor model, a formative second order model has its own share of problems (Howell, Breivik,
and Wilcox 2007). For example, some argue that formative measurement uses conceptions of
constructs, measures, and causality that are difficult to defend, the presumed viability of formative
measurement is a fallacy, and the objectives of formative measurement may also be achieved
using alternative models with reflective measures” (Edwards 2011). Interestingly, the Schmid-
Leiman Factor Structure (SLS) may offer a better solution in many cases (Wolff and Preising
2005). Hence, this paper tries to:
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a) Empirically compare and contrast reflective vs. formative second order models.

b) Demonstrate the use of the SLS approach empirically as a viable alternative to the
formative second order model structure.

Specifically, we compare reflective vs. formative second order factor models vs. Schmid-Leiman
Factor Structure with data from a mall survey in India that tested the impact of store environment
on impulse buying.

MODEL DEVELOPMENT

Model 1 - Reflective Second Order Factor Model

In line with Baker et al. (2002), we define store environment as consisting of ambient (e.g. lighting,
scent and music), design (layout, assortment) and social factors (presence and effectiveness of
salespersons). Thus, store environment is a second order factor. Drawing upon extant research in
psychology and retailing, we came up with a model. Figure 2 offers the standard reflective second
order factor model that is the “default” option where the first order factors, social, ambient and
design factors are reflective of the second order factor, store environment.

Figure 2 - Reflective Model - Store Environment and Impulse Buying

+
Shopping Positive
Ambient Enjoyment Affect
factors
+
Design Store +
Environment
Factors
+
+
Social Impulse + Imp ulsive Impulse
Factors Buying Urge Buying
Tendency
- -
Negative
Affect

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Model 2 - Formative Second Order Factor Model

According to Jarvis et al. (2003, pp.203), it is conceptually preferable to use reflective indicators if
the direction of causality “flows from the construct to the measures” and formative indicators if the
direction is in the opposite direction “from the measures to the construct”. In the case of store
environment, the perceptions of ambient, social and design factors drive overall perceptions of
store environment rather than the other way round.

Specifically, shoppers may evaluate a store’s ambient factors (e.g. if the music is nice in the store),
social factors (e.g. the store employees are friendly) and design factors (e.g. the layout is good).
Based on these perceptions, they may form an overall impression of the store’s environment. It is
unlikely that a shopper would first overall form a positive impression of the store and then because
of this, conclude that its music was nice. Hence, in this case a formative second order model
structure (Figure 3) may be appropriate.

Figure 3 - Formative Model - Store Environment and Impulse Buying

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+
Shopping Positive
Ambient Enjoyment Affect
factors
+
Design Store +
Environment
Factors
+
+
Social Impulse + Imp ulsive Impulse
Factors Buying Urge Buying
Tendency
- -
Negative
Affect

Model 3 - Schmid-Leiman Factor (SLS) Structure
When there is a second order factor structure and the concerned constructs have multi-item
measures, the Schmid-Leiman factor structure can be used instead of the second factor structures.
In this structure, the first order factors as well as the factor considered to be the second order
factor in a standard second order factor model are both considered exogenous. The indicator
variables are considered to be driven by both the first order factors and the erstwhile second order
factor, as shown in Figure 4.

Figure 4 – Schmid-Leiman Structure - Store Environment and Impulse Buying

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M
1
Ambient +
Factor Positive
M Shopping Affect
2 Enjoyment +

L
1 +
Ambient +
Factor
L
2

+ +
Store
L3 Environment Urge Impulse

Buying
E
1
Social

Factor +
E
2 -
-
E
3 Impulse

Buying
LO Tendency -
Design 1
Factor Negative
Affect
LO
2

In the standard second order factor structure, for instance, the indicator variable, “LO ” is
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considered driven by the factor “design”. In the new Schmid-Leiman factor structure, the same
indicator variable is considered to be driven by both store environment (the erstwhile second order
factor) and “design factors”. We argue that this is more reflective of reality as well, since when a
shopper thinks about “design”, it is likely that apart from thinking of the store’s design, (s)he will
think about the store overall as well.
METHODOLOGY

We used a single stage mall intercept (in 44 leading outlets in Chennai, India) method to collect
data (Sample size = 733, response rate = 46%). We used established scales for all constructs,
which showed good reliabilities (Table 1). Only clearly unplanned purchases that could not be
classified as reminder items were recorded as impulse purchases (Beatty and Ferrell 1998). The
number of such impulse purchases was counted for each shopper.
TABLE 1
Scale Source/s Alpha Mean SD
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Music (Morin 2005) .885 2.63 1.06
Light (Areni and Kim 1994; Smith 1989; .660 3.53 .68
Summers and Hebert 2001)
Layout (Dickson and Albaum 1977) .628 3.63 .74
Employee (Dickson and Albaum 1977) .838 3.56 .77
Positive Affect (Watson, Clark, and Tellegen 1988) .770 3.25 .72
Negative Affect (Watson et al. 1988) .830 2.32 .80
Urge (Beatty and Ferrell 1998) .684 3.06 .93
IBT (Weun, Jones, and Beatty 1998) .714 3.12 .69
Shopping (Sproles and Kendall 1986) .881 3.17 .86
Enjoyment

To test our model, we followed a 2-step approach using structural equation modeling, first refining
the measurement model before analyzing the structural one (Anderson and Gerbing 1988). We
tested Common Method Variance and found no evidence of this. Having purified the measurement
model, we first analyzed a base model without the second order factor, store environment. We had
direct paths from ambient, social and design factors to the mediators. The fit was poor.

Next, we analyzed the model with the standard reflective factor structure. The structural model
yielded the following:(χ2 = 860.33, df = 372, χ2/df = 2.3, RMSEA = .07, SRMR = .05, CFI = .91).
While the fit improved considerably, it was still below par. We then analyzed the model in Figure 3,
the formative second order model. We found that the fit improved further (χ2 = 388.52, df = 155,
χ2/df = 2.51, RMSEA = .05, SRMR = .05, CFI = .95) with all the fit-indices better than the
recommended cut-off values (RMSEA < .06, SRMR < .08, CFI > .95). Finally, the Schmid-Leiman
Factor Structure provided the best fit (as expected) compared to the models without a second
order factor structure and a reflective second order one (χ2 = 664.96, df = 356, χ2/df = 1.9, RMSEA
= .04, SRMR = 0.03, CFI = 0.95).

TABLE 2
Fit Indices
Model CFI IFI NFI SRMR RMSEA
First Order Factor .81 .81 .76 .19 .06
Reflective Second Order Factor .91 .91 .86 .05 .07
Formative Second Order Factor .94 .95 .91 .05 .05
Schmid-Leiman Factor Structure .95 .95 .94 .03 04

In another study in Singapore and Hong Kong, we demonstrate the use of the SLS approach
where the reflective model is apparently better in the context of consumer impulsiveness.
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