scholarly journals Untangling the complexity of market competition in consumer goods—A complex Hilbert PCA analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245531
Author(s):  
Makoto Mizuno ◽  
Hideaki Aoyama ◽  
Yoshi Fujiwara
Keyword(s):  
Consumer Goods ◽  
Consumer Choice ◽  
Quantitative Research ◽  
Market Competition ◽  
Principal Component ◽  
Hodge Decomposition ◽  
Multiple Products ◽  
Managerial Implications ◽  
Novel Method ◽  
Competitive Products

Today’s consumer goods markets are rapidly evolving with significant growth in the number of information media as well as the number of competitive products. In this environment, obtaining a quantitative grasp of heterogeneous interactions of firms and customers, which have attracted interest of management scientists and economists, requires the analysis of extremely high-dimensional data. Existing approaches in quantitative research could not handle such data without any reliable prior knowledge nor strong assumptions. Alternatively, we propose a novel method called complex Hilbert principal component analysis (CHPCA) and construct a synchronization network using Hodge decomposition. CHPCA enables us to extract significant comovements with a time lead/delay in the data, and Hodge decomposition is useful for identifying the time-structure of correlations. We apply this method to the Japanese beer market data and reveal comovement of variables related to the consumer choice process across multiple products. Furthermore, we find remarkable customer heterogeneity by calculating the coordinates of each customer in the space derived from the results of CHPCA. Lastly, we discuss the policy and managerial implications, limitations, and further development of the proposed method.

scholarly journals Subcoalition Cluster Analysis: A New Method for Measuring Political Conflict in Organizations

2018 ◽  
Author(s):  
Scott Ganz ◽  
Daniel Schiff
Keyword(s):  
Decision Making ◽  
Cluster Analysis ◽  
Quantitative Research ◽  
Principal Component ◽  
Political Conflict ◽  
Organizational Decision Making ◽  
Individual Preferences ◽  
Behavioral Theories ◽  
Novel Method

Behavioral theories of organizational decision making emphasize that organizations are political coalitions. Despite considerable recent qualitative research in management and organizational theory on the role of politics in decision making and managing organizational change, quantitative research in this area has stalled. The reason for the lack of progress is not theoretical, but rather methodological; researchers lack empirical tools for understanding basic processes of coalition formation, evolution, and conflict in organizations. We introduce a novel method for modeling politics in organizations that builds on the model of intra-organizational conflict in March (1962), which we call “subcoalition cluster analysis” (sCCA). The main contribution of sCCA is that it identifies subcoalitions with consistent preferences that are in conflict without placing additional restrictions on the structure of individual preferences. We apply sCCA to two cases, Wikipedia and the Baseball Writers’ Association of America and show how leadership would benefit from conceiving of their membership as competing subcoalitions instead of individuals with idiosyncratic preference disagreement. Finally, we compare the performance of sCCA to Principal Component Analysis (PCA) and k-means clustering and demonstrate that sCCA does a better job identifying latent structure in the data when the organization consists of more subcoalitions, when individual preferences are not perfectly aligned with those of their subcoalition, and when observations are missing.

scholarly journals Hyperspectral Image Classification with Localized Graph Convolutional Filtering

2021 ◽  
Vol 13 (3) ◽  
pp. 526
Author(s):  
Shengliang Pu ◽  
Yuanfeng Wu ◽  
Xu Sun ◽  
Xiaotong Sun
Keyword(s):  
Hyperspectral Image ◽  
Principal Component ◽  
Representation Learning ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Spectral Graph Theory ◽  
Feature Reduction ◽  
Graph Representation ◽  
Novel Method ◽  
Local Graph

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

Effects of parental cultural capital on purchase intention of cognac

2019 ◽  
Vol 31 (3) ◽  
pp. 344-361 ◽  
Author(s):  
Yujie Wei ◽  
Blaise Bergiel ◽  
Lingfang Song
Keyword(s):  
Cultural Capital ◽  
Consumer Choice ◽  
Purchase Intention ◽  
Parental Influence ◽  
Value Orientations ◽  
Content Type ◽  
Consumer Purchase Intention ◽  
The Usa ◽  
Managerial Implications ◽  
Willingness To Purchase

Purpose The purpose of this paper is to examine the possibility that individual differences in consumer choice of cognac are at least partially influenced by parental cultural capital. Also examined are ten value orientations factors (e.g. hedonism and self-direction) and attitudes toward France, cognac’s country-of-origin that may affect the degree of this intergenerational influence. Design/methodology/approach The survey research measures parents’ cultural capital, value orientations and attitude toward France and purchase intention using recognized scales. Data were collected from the faculty and students of a major university located in the southeast of the USA. The sample size was 234. Findings The results confirm that parental cultural capital, consumer value orientations and attitudes toward France have significant impacts on the consumer’s willingness to purchase cognac. Adult children of high cultural capital parents are more likely to buy cognac. Practical implications The findings of this paper provide meaningful insights into intergenerational influences on consumer purchase intention of cognac and socialization theory. The paper provides several managerial implications for segmentation, targeting and positioning of cognac in the US market. Originality/value As the first of its kind, this paper introduces the parents’ cultural capital into the consumer research regarding cognac. The longer-term effects that parents can have on grown children’s consumer behavior are confirmed, suggesting that parental influence persists well into adulthood and has impact on their brand preference.

ORGANISING FOR INNOVATION IN PROFESSIONAL SERVICES FIRMS: ECONOMETRIC EVIDENCE FROM THE UK

2021 ◽  
Author(s):  
JANE BOURKE ◽  
STEPHEN ROPER ◽  
JAMES H LOVE
Keyword(s):  
Information Sharing ◽  
Principal Component ◽  
Professional Services ◽  
Professional Service ◽  
Service Firms ◽  
Work Practices ◽  
Professional Service Firms ◽  
Econometric Evidence ◽  
Managerial Implications ◽  
The Uk

Undertaking innovation involves a range of different activities from ideation to the commercialisation of innovations. Each activity may have very different resources and organisational requirements, however, most prior studies treat innovation as a single un-differentiated activity. Here, using new survey data for professional service firms (PSFs) in the UK, we are able to examine separately how a range of organisational work practices influence success in ideation and commercialisation. In particular, we use principal component analysis (PCA) to identify and compare the benefits of four groups of organisational work practices relating to strategy & information sharing, recruitment & training, work flexibility & discretion and culture & leadership. Strong contrasts emerge between those work practices that are important for success in ideation and commercialisation. Work practices linked to culture & leadership are important for ideation activities, while strategy &information sharing practices are more strongly associated with commercialisation success. The results suggest clear managerial implications depending on the priority

Remaking Medicare: The Voucher Myth

1998 ◽  
Vol 28 (1) ◽  
pp. 29-46
Author(s):  
Jonathan Oberlander
Keyword(s):  
Consumer Choice ◽  
Baby Boom ◽  
Market Competition ◽  
The Elderly ◽  
Medicare Beneficiaries ◽  
Short Term ◽  
Medicare Program ◽  
The Stability ◽  
Voucher System

There is growing enthusiasm for transforming Medicare into a voucher system. Advocates claim vouchers would increase the health care choices available to Medicare beneficiaries, reduce the regulatory burden on the federal government, and promote the benefits of fair market competition. In addition, some analysts contend vouchers are the only feasible solution to Medicare's short-term financing problems and the long-term “crisis” of the retirement of the baby-boom generation. The author argues against these claims. Vouchers would not work as advertised by proponents because of the limitations of risk-adjustment methods and unrealistic assumptions about consumer choice. Moreover, the elderly and disabled Medicare population is ill-suited to cope in a competitive insurance system. Implementation of vouchers would therefore pose a threat to both the health of beneficiaries and the stability of the Medicare program. The implications of this analysis for Medicare reform are discussed.

Estimating Store Brand Shelf Space: A New Framework Using Neural Networks and Partial Least Squares

2009 ◽  
Vol 51 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Monica Gomez ◽  
Shintaro Okazaki
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Statistical Treatment ◽  
Principal Component ◽  
Future Research ◽  
Store Brand ◽  
Shelf Space ◽  
Managerial Implications ◽  
Space Data

Despite abundant research that examines the effects of store brands on retail decision making, little attention has been paid to the predictive model of store brand shelf space. This paper intends to fill this research gap by proposing and testing a theoretical model of store brand shelf space. From the literature review, 11 independent variables were identified (i.e. store format, reputation, brand assortment, depth of assortment, in-store promotions, leading national brands’ rivalry, retailers’ rivalry, manufacturers’ concentration, store brand market share, advertising, and innovation) and analysed as potential predictors of the dependent variable (i.e. store brand shelf space). Data were collected for 29 product categories in 55 retail stores. In designing the statistical treatment, a three-phase procedure was adopted: (1) interdependence analysis via principal component analysis; (2) dependence analysis via neural network simulation; and (3) structural equation modelling via partial least squares. The findings corroborate our proposed model, in that all hypothesised relationships and directions are supported. On this basis, we draw theoretical as well as managerial implications. In closing, we acknowledge the limitations of this study and suggest future research directions.

scholarly journals A Novel Method of Immunomodulation of Endothelial cells Using Streptococcus Pyogenes and its Lysate

2020 ◽  
Author(s):  
Mark Christopher Arokiaraj ◽  
Jarad Wilson
Keyword(s):  
Cell Culture ◽  
Endothelial Cells ◽  
Coronary Artery ◽  
Streptococcus Pyogenes ◽  
Immune Modulation ◽  
Principal Component ◽  
Second Phase ◽  
Novel Method ◽  
Artery Disease ◽  
Pleiotropic Functions

AbstractBackgroundCoronary artery diseases and autoimmune disorders are common in clinical practice. In this study, a novel method of immune-modulation to modify the endothelial function was studied to modulate the features of the endothelial cells, and thereby to reduce coronary artery disease and other disorders modulated by endothelium.MethodsHUVEC cells were seeded in the cell culture, and streptococcus pyogenes were added to the cell culture, and the supernatant was studied for the secreted proteins. In the second phase, the bacterial lysate was synthesized, and the lysate was added to cell culture; and the proteins in the supernatant were studied at various time intervals.ResultsWhen streptococcus pyogenes alone was added to culture, E Cadherin, Angiostatin, EpCAM and PDGF-AB were some of the biomarkers elevated significantly. HCC1, IGFBP2 and TIMP were some of the biomarkers which showed a reduction. When the lysate was added, the cell-culture was maintained for a longer time, and it showed the synthesis of immune regulatory cytokines. Heatmap analysis showed a significant number of proteins/cytokines concerning the immune/pathways, and toll-like receptors superfamily were modified. BLC, IL 17, BMP 7, PARC, Contactin2, IL 10 Rb, NAP 2 (CXCL 7), Eotaxin 2 were maximally increased. By principal component analysis, the results observed were significant.ConclusionThere is potential for a novel method of immunomodulation of the endothelial cells, which have pleiotropic functions, using streptococcus pyogenes and its lysates.

scholarly journals A data-driven approach to measuring epidemiological susceptibility risk around the world

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alessandro Bitetto ◽  
Paola Cerchiello ◽  
Charilaos Mertzanis
Keyword(s):  
Factor Model ◽  
Principal Component ◽  
Data Driven ◽  
Dynamic Factor ◽  
Country Level ◽  
Epidemiological Risk ◽  
Data Driven Approach ◽  
Managerial Implications ◽  
Total Variability ◽  
Pca Method

AbstractEpidemic outbreaks are extreme events that become more frequent and severe, associated with large social and real costs. It is therefore important to assess whether countries are prepared to manage epidemiological risks. We use a fully data-driven approach to measure epidemiological susceptibility risk at the country level using time-varying information. We apply both principal component analysis (PCA) and dynamic factor model (DFM) to deal with the presence of strong cross-section dependence in the data. We conduct extensive in-sample model evaluations of 168 countries covering 17 indicators for the 2010–2019 period. The results show that the robust PCA method accounts for about 90% of total variability, whilst the DFM accounts for about 76% of the total variability. Our index could therefore provide the basis for developing risk assessments of epidemiological risk contagion. It could be also used by organizations to assess likely real consequences of epidemics with useful managerial implications.

Is sustainability a motive to buy? An exploratory study in the context of mobile applications channel among young Indian consumers

foresight ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 177-199 ◽  
Author(s):  
Asha K.S. Nair ◽  
Som Sekhar Bhattacharyya
Keyword(s):  
Qualitative Research ◽  
Factor Analysis ◽  
Mobile Applications ◽  
Quantitative Research ◽  
Questionnaire Development ◽  
Principal Component ◽  
Food Delivery ◽  
Content Type ◽  
Mobile Payments ◽  
Digital Payments

PurposeConsumers shopping motives may differ across products/services categories, retail formats and channels. In the context of m-Apps-based commerce, this study aims to explore different shopping motives of consumers in three different categories of app, namely, food delivery, ride sourcing and digital payments. Using motivation literature, the study extends the theory of consumer motives by including sustainability as a key motive to buy in the context of m-App channel. Further, the authors undertake a comparative analysis of the identified motives across the three mobile applications (m-Apps).Design methodology/approachThe research methodology involved two stages (qualitative research followed by quantitative research). In qualitative research, personal interview was conducted to extract items for survey questionnaire development. Subsequently, quantitative analysis was carried out. The data were subjected to exploratory factor analysis and confirmatory factor analysis (CFA). The study sample comprised 201 young Indian managers.FindingsUsing principal component analysis and CFA, the study validates the existence of different motivations in the three categories of m-Apps considered. Transaction-oriented and sustainability-oriented motivation is found to be a major motive to use m-Apps for food delivery, ride sourcing and mobile payments. Additionally, in digital wallet applications for mobile payments, consumers exhibit innovation-oriented motivation. Value-oriented motivation was identified as a motive in food delivery apps.Research limitations/implicationsThe scale developed and the comparative study done extended the theoretical conversation on young consumer motives in the context of m-Apps channel and extended it by including sustainability motive, which needs further in-depth study.Originality/valueThis is one of the first studies to explore sustainability motives in the context of m-Apps channel.

Chemical Structure-Odor Correlation in a Series of Synthetic n-Nonen-1-ols

1995 ◽  
Vol 50 (11-12) ◽  
pp. 757-765 ◽  
Author(s):  
Yasunobu Sakoda ◽  
Kenji Matsui ◽  
Tadahiko Kajiwara ◽  
Akikazu Hatanaka
Keyword(s):  
Principal Component Analysis ◽  
Double Bond ◽  
Spatial Structure ◽  
Chemical Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Acetylenic Compound ◽  
Analysis Method ◽  
Entire Series ◽  
Novel Method

In order to elucidate chemical structure-odor correlation in the all isomers of n-nonen-1- ols, an entire series of these alcohols were synthesized stereo-selectively in high purity. For unequivocal syntheses of them, geometrically selective hydrogenation of the respective acetylenic compound was adopted. The synthesized alcohols were converted to their 3,5-dinitrobenzoate derivatives with 3,5-dinitrobenzoyl chloride, and then purified by repeated recrystallization. Chemical structure-odor correlations in all the isomers of n-nonen-1-ols were elucidated by introducing a novel method to evaluate odor characteristics and by treating the obtained data statistically with the principal component analysis method (Cramer et al., 1988). The odor profiles of the tested compounds were attributable largely to the positions of the carbon- double bond. The geometries of compounds had only a little effect. With the principal component analysis, the odor profiles of the series of compounds were successfully integrated into the first and the second principal components. The first component (PC-1) consisted of combined characteristics of fruity, fresh, sweet, herbal and oily-fatty, in which herbal and oily-fatty were conversely correlated each other to the position of double-bond of the tested compounds. Of these, only (6Z)-nonen-1-ol deviated markedly from the correlation, indicative of some special interaction between the spatial structure of this compound and the sensory machinery of human.

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