A Cross-Cultural Perspective on Motives and Patterns of Brand Recommendation in Social Media

2019 ◽  
pp. 388-406
Author(s):  
Castulus Kolo ◽  
Stefan Widenhorn ◽  
Anna-Lena Borgstedt ◽  
David Eicher
Keyword(s):  
Social Media ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Cultural Differences ◽  
Formal Education ◽  
Principal Component ◽  
Cross Cultural ◽  
Substantial Influence ◽  
Cultural Perspective ◽  
The Us

This article describes how today, social media enable users to comment on brands in a multitude of ways. Although it is undoubted that this can have a substantial influence on the way brands impact on consumers, comparatively little is known about what motivates consumers to recommend brands in social media and whether there are cultural differences therein. This article aims to determine the factors leading to either positive or negative communication about brands on Facebook, YouTube, Twitter, and brand-related blogs based on a representative sample from Germany and the US, each with 1,000 adults. Complementary to an analysis of factors determining a general inclination to recommend, a principal component analysis of the diverse motives to do so exhibits patterns being largely consistent in a cross-cultural perspective, however, with differences in specific practices concerning gender, age, and formal education. A cluster analysis as well as taking a look at “influencers” provide a basis for developing differentiated strategies of brand communication and management respectively.

A Cross-Cultural Perspective on Motives and Patterns of Brand Recommendation in Social Media

2018 ◽  
Vol 8 (2) ◽  
pp. 27-44 ◽  
Author(s):  
Castulus Kolo ◽  
Stefan Widenhorn ◽  
Anna-Lena Borgstedt ◽  
David Eicher
Keyword(s):  
Social Media ◽  
Principal Component Analysis ◽  
Cultural Differences ◽  
Formal Education ◽  
Principal Component ◽  
Cross Cultural ◽  
Substantial Influence ◽  
Cultural Perspective ◽  
The Us ◽  
Do So

This article describes how today, social media enable users to comment on brands in a multitude of ways. Although it is undoubted that this can have a substantial influence on the way brands impact on consumers, comparatively little is known about what motivates consumers to recommend brands in social media and whether there are cultural differences therein. This article aims to determine the factors leading to either positive or negative communication about brands on Facebook, YouTube, Twitter, and brand-related blogs based on a representative sample from Germany and the US, each with 1,000 adults. Complementary to an analysis of factors determining a general inclination to recommend, a principal component analysis of the diverse motives to do so exhibits patterns being largely consistent in a cross-cultural perspective, however, with differences in specific practices concerning gender, age, and formal education. A cluster analysis as well as taking a look at “influencers” provide a basis for developing differentiated strategies of brand communication and management respectively.

The Tipping Point

2020 ◽  
Vol 12 (1) ◽  
pp. 23-39
Author(s):  
Soo Kwang Oh ◽  
Seoyeon Hong ◽  
Hee Sun Park
Keyword(s):  
Social Media ◽  
Focus Group ◽  
Cultural Differences ◽  
Exploratory Study ◽  
Cross Cultural ◽  
Cultural Perspective ◽  
Theoretical Frameworks ◽  
Media Usage ◽  
Current Shift ◽  
Perceived Relationships

While previous researchers have addressed motivations to join and continue using social media, this paper focuses on why users quit certain social media and change their favorite platforms, such as the current shift from Facebook to Twitter to Instagram and Snapchat. Furthermore, this exploratory study seeks to build an understanding of social media usage and motivations for switching from a cross-cultural perspective by comparing findings from Korean and U.S. users. Findings from 19 focus group sessions (n = 118) highlight influences regarding modes of usage, user control, commitment, addiction, privacy, perceived relationships, self-construals, and social/cultural trends. Findings are further analyzed and compared in light of relevant theoretical frameworks and cultural differences.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Quality evaluation of tugaksheeree samples by ATR-FTIR spectroscopy using multicomponent analysis

2020 ◽  
Vol 10 ◽  
Author(s):  
Nikunj D. Patel ◽  
Niranjan S. Kanaki
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Quality Evaluation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Attenuated Total Reflectance ◽  
Chemometric Methods ◽  
Total Reflectance ◽  
Fingerprint Region ◽  
Ftir Technique

Background: Numerous Ayurvedic formulations contains tugaksheeree as key ingredient. Tugaksheereeis the starch gained from the rhizomes of two plants, Curcuma angustifoliaRoxb. (Zingiberaceae) and Marantaarundinacea (MA) Linn. (Marantaceae). Objective: The primary concerns in quality assessment of Tugaksheeree occur due to adulteration or substitution. Method: In current study, Fourier transform infrared (FTIR) technique with attenuated total reflectance (ATR) facility was used to evaluate tugaksheeree samples. Total 10 different samples were studied and transmittance mode was kept to record the spectra devoid of pellets of KBR. Further treatment was given with multi component tools by considering fingerprint region of the spectra. Multivariate analysis was performed by various chemometric methods. Result: Multi component methods like Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA)were used to discriminate the tugaksheeree samples using Minitab software. Conclusion: This method can be used as a tool to differentiate samples of tugaksheeree from its adulterants and substitutes.

scholarly journals COVID-19 Pandemic and Adaptive Shopping Patterns: An Insight from Indonesian Consumers

2021 ◽  
pp. 097215092110135
Author(s):  
Arif Hartono ◽  
Asma'i Ishak ◽  
Agus Abdurrahman ◽  
Budi Astuti ◽  
Endy Gunanto Marsasi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Demographic Characteristics ◽  
Depth Information ◽  
Factor Scores ◽  
Consumer Spending ◽  
Consumer Responses ◽  
Buying Behaviour ◽  
Manova Analysis

Although existing studies on consumers typology are extensively conducted, insights on consumers typology in adapting their shopping attitude and behaviour during the COVID-19 pandemic remain unexplored. Current studies on consumer responses to the COVID-19 pandemic tend to focus on the following themes: panic buying behaviour, consumer spending and consumer consumption. This study explores a typology of adaptive shopping patterns in response to the COVID-19 pandemic. The study involved a survey of 465 Indonesian consumers. Principal component analysis is used to identify the variables related to adaptive shopping patterns. Cluster analysis of the factor scores obtained on the adaptive shopping attitude and behaviour revealed the typology of Indonesian shoppers’ adaptive patterns. Multivariate Analysis of Variance (MANOVA) analysis is used to profile the identified clusters based on attitude, behaviour and demographic characteristics. Results revealed five adaptive shopping patterns with substantial differences among them. This study provides in-depth information about the profile of Indonesian shoppers’ adaptive patterns that would help retailers in understanding consumers and choosing their target group. The major contribution of this study is providing segmentation on shopping adaptive patterns in the context of the COVID-19 pandemic which presents interesting differences compared with previous studies. This study reveals new insights on shoppers’ adaptive attitude and behaviour as consumers coped with the pandemic.

Fake news detection and social media trust: a cross-cultural perspective

2021 ◽  
pp. 1-20
Author(s):  
Amal Dabbous ◽  
Karine Aoun Barakat ◽  
Beatriz de Quero Navarro
Keyword(s):  
Social Media ◽  
Cross Cultural ◽  
Fake News ◽  
Cultural Perspective ◽  
Media Trust
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