Valence or Volume? Maximizing Online Review Influence Across Consumers, Products, and Marketing

Evidence shows that products with online reviews have a higher chance to stay in the consideration set of consumers than products with no online reviews do. Online reviews, as consumer-generated content, affect consumers’ purchase decision-making process. Most of the studies in this area have looked at valence and volume of online reviews. Generally, valence and volume of online reviews are considered to positively influence sales; however, the findings in the literature are inconclusive. While some studies have reported a positive relationship between valence/volume and sales, others have failed to find any significant relationship. Using both lab experiments and real-world data, this dissertation addresses the conflicting findings from previous studies by introducing the role of the individual, the product, and firm-generated promotional message. In the first essay of the dissertation, I attempt to explain the inconsistencies in the literature by examining the moderating effect of regulatory focus on the relative role of valence versus volume of online reviews in consumer purchase decisions. Regulatory focus theory suggests that people tend to have either a promotion or a prevention orientation in approaching their desired goals. The current research argues that depending on consumers’ regulatory orientation, the effect of either review valence or review volume on consumers’ likelihood to purchase the product will become more salient. Moreover, specific products also activate a certain regulatory orientation. Therefore, depending on the products’ regulatory orientation, valence or volume of online reviews (i.e. valence and volume) will become more or less influential across different product categories. The second essay of the dissertation investigates the use of firm-generated promotional message to maximize online review volume versus valence effects. Specifically, it examines how a common online retail-marketing tactic, scarcity appeal, can be used to accentuate the effect of online review volume and valence on consumers’ purchase decisions. I argue that the mere presence of a scarcity appeal and the specific type of scarcity appeal used can influence the extent to which consumers weigh valence versus volume information. The integrative approach developed in this research advocates the simultaneous consideration of firm marketing tactics and consumer-generated content. It argues that firm-level actions can interact with online review components (i.e. volume and valence) to affect sales

E-Portfolios for Team-Based and Project-Learning Based Marketing Classes

This e-portfolio describes the use of two EBTs including team-based learning and project-based learning in multiple quarters in an upper level marketing class. The document deals with the motivations of incorporating these practices and how students benefited from them

Female Power Portrayals in Ads, Underlying Dimensions

Stereotypical female portrayals are still common in advertisements, though the role of women in society has progressed. In recent years, it appears there has been a move toward portraying women in power positions in ads. This research focuses on this new trend of female portrayals in ads. Power is defined as the capability to change others’/self behavior or thoughts. Building on theories of social power and feminine power, a new typology for different types of female power in ads is proposed. This typology includes five types of female power in ads including sexual power, athletic power, expert power, family power, and empowerment. To determine the viability of this classification system, participants are asked to complete a sorting task. A set of current pre-rated print ads are given to participants for them to sort into ads that demonstrate a similar type of female power in the same category. Multidimensional scaling (MDS) and hierarchical clustering of the results from sorting is used to extract the underlying dimensions of female power in ads and provide empirical evidence for the proposed typology. Future research might study interrelations between female power dimensions in ads and also study the consequences of exposure to female power portrayals

Group efficiency analysis in decision processes: a data envelopment analysis approach

Data envelopment analysis (DEA) is a powerful mathematical programming methodology for evaluating the relative efficiency of decision-making units (DMUs) with multiple outputs and multiple inputs. In the classic DEA, it has been implicitly assumed that all DMUs perform in a unique technology set and the traditional DEA cannot measure the relative performances of DMUs with dissimilar classes. In other words, if we have different groups of DMUs, the traditional DEA models cannot be applied to evaluate such cases. In this paper, it has been assumed that the DMUs do business in different groups. We are interested to evaluate the members of the groups. The main aim of this paper is proposing a DEA-based methodology to estimate the technical efficiency of DMUs along with different groups with different technologies. The proposed method is illustrated by an empirical example on banking industry

Stochastic performance measurement in two-stage network processes: A data envelopment analysis approach

summary:In classic data envelopment analysis models, two-stage network structures are studied in cases in which the input/output data set are deterministic. In many real applications, however, we face uncertainty. This paper proposes a two-stage network DEA model when the input/output data are stochastic. A stochastic two-stage network DEA model is formulated based on the chance-constrained programming. Linearization techniques and the assumption of single underlying factor of the data are used to construct the equivalent deterministic linear programming model. The relationship between the stochastic efficiency of each stage and stochastic centralized efficiency of the whole process, at different confidence levels, is discussed. To illustrate the real applicability of the proposed approach, a real case on 16 commercial banks in China is given

Trade-offs analysis of sustainability dimensions using integer-valued data envelopment analysis

Conducting an in-depth exploration of trade-offs between sustainability aspects is a notable matter of taking decisions. Furthermore, there are many real world investigations that trade-offs and sustainability should be dealt with in the presence of desirable and undesirable materials while some of them accept integer amounts. Therefore, this study addresses trade-offs of sustainability dimensions when undesirable and integer-valued measures are presented. For this purpose, approaches based upon data envelopment analysis (DEA) are proposed. To explain, DEA models are introduced to calculate individual and group marginal rates of substitution and also directional marginal rates of substitution when integer and undesirable variables are observed. These procedures are applied to calculate trade-offs between different sustainability dimensions, including economic, environmental and social ones. The applications of ports and industrial parks are provided to clarify the approaches appeared in this study. The results derived from the proposed strategies show the usefulness and validity of them

Identification of QTLs for Yield Related Traits in Indica Type Rice Using SSR and AFLP Markers

This research was carried out to identify quantitative trait loci (QTLs) controlling yield and yield components in rice using 196 F2:4 lines derived from a cross between two rice varieties of indica, Sepidrood and Gharib. Quantitative trait loci analysis using composite interval mapping was carried out by 105 SSR and 111 AFLP markers. Results showed that 8 chromosomes contain 28 regions (QTLs) controlling 11 studied traits. One QTL was mapped for the number of spikelet per panicle on chromosome 12, three QTLs for number of filled grains per panicle on chromosomes 1, 6 and 11, three QTLs for empty spikelets per panicle on chromosomes 2, 3 and 12, five QTLs for plant height on chromosomes 1, 7 (2 QTLs), eight and 11, four QTLs for days to 50% flowering on chromosomes 2, 3 (2 QTLs) and 6, one QTL for panicle length on chromosome 1, two QTLs for 1000 grain weight on chromosomes 1 and 2, three QTLs for number of panicles per plant on chromosomes 1, 3 and 6, one QTL for grain yield on chromosome 3, four QTLs for days to maturity on chromosomes 2, 3 (2 QTLs) and 6 and one QTL for fertility percentage on chromosome 11. The identified QTLs on specific chromosome regions explaining high phenotypic variance can be considered for use in marker-assisted selection (MAS) programs

A Linear Programming Relaxation DEA Model for Selecting a Single Efficient Unit with Variable RTS Technology

The selection-based problem is a type of decision-making issue which involves opting for a single option among a set of available alternatives. In order to address the selection-based problem in data envelopment analysis (DEA), various integrated mixed binary linear programming (MBLP) models have been developed. Recently, an MBLP model has been proposed to select a unit in DEA with variable returns-to-scale technology. This paper suggests utilizing the linear programming relaxation model rather than the MBLP model. The MBLP model is proved here to be equivalent to its linear programming relaxation problem. To the best of the authors’ knowledge, this is the first linear programming model suggested for selecting a single efficient unit in DEA under the VRS (Variable Returns to Scale) assumption. Two theorems and a numerical example are provided to validate the proposed LP model from both theoretical and practical perspectives

Estimating outputs using an inverse non-radial model with non-discretionary measures: An application for restaurants

Few inverse data envelopment analysis (DEA) models have incorporated non-discretionary measures based on radial efficiency values. However, the efficiency may be miscounted in radial approaches when some non-zero slacks appear. Furthermore, there is scant research on inverse DEA to estimate performance measures in the restaurant industry. Accordingly, this research proposes models based on non-radial DEA to analyze the efficiency and output changes of some Iranian restaurants while also presenting non-discretionary measures. Actually, in the company of non-discretionary factors, a non-radial DEA approach and its inverse problem are introduced to assess the performance and estimate the outputs for the modifications of inputs, respectively, while the inefficiency levels are maintained (and when they are preserved or decreased). The inefficiency of each discretionary input and output is specified using the presented non-radial DEA approach, and output targets are determined through inverse non-radial DEA with non-discretionary inputs. The results show containing non-discretionary data leads to more rational determinations through non-radial DEA-founded problems. This research presents analytic insights into the resources of inefficiency and output targets of entities with non-discretionary data, such as restaurants.