Self-Aware Contextual Behavior Analysis for Service Quality Assurance Over Social Networks

Social media allows people to share their ideologue through an efficient channel of communication. The social dialogues carry sentiment in expression regarding a particular social profile, trend, or topic. In our research, we have collected real-time user comments and feedbacks from Twitter portals of two food delivery services. This is followed by the extraction of the most prevalent contexts using natural language analytics. Further, our proposed algorithmic framework is used to generate a signed social network to analyze the product-centric behavioral sentiment. Analysis of sentiment with the fine-grained level about contexts gave a broader view to evaluate and perform contextual predictions. Customer behavior is analyzed, and the outcome is received in terms of positive and negative contexts. The results from our social behavioral model predicted the positive and negative contextual sentiments of customers, which can be further used to help in deciding future strategies and assuring service quality for better customer satisfaction.

Self-Aware Contextual Behavior Analysis for Service Quality Assurance over Social Networks

Social media allows people to share their ideologue through an efficient channel of communication. The social dialogues carry sentiment in expression regarding a particular social profile, trend, or topic. In our research, we have collected real-time user comments and feedbacks from Twitter portals of two food delivery services. This is followed by the extraction of the most prevalent contexts using natural language analytics. Further, our proposed algorithmic framework is used to generate a signed social network to analyze the product-centric behavioral sentiment. Analysis of sentiment with the fine-grained level about contexts gave a broader view to evaluate and perform contextual predictions. Customer behavior is analyzed, and the outcome is received in terms of positive and negative contexts. The results from our social behavioral model predicted the positive and negative contextual sentiments of customers, which can be further used to help in deciding future strategies and assuring service quality for better customer satisfaction.

Online Vegan Food Delivery System

The system proposed here signifies Vegan food delivery process. This system will allow restaurants to quickly and easily manage an online menu which customers can browse and use to place orders with just a few clicks. The system then relays these orders to restaurant’s employees through an easy to navigate graphical interface for efficient processing. Keywords: Vegan food delivery, customers, vegan vibes, food order etc.

Channel integration affects usage intention in food delivery platform services: the mediating effect of perceived value

PurposeStructural equation modeling was conducted, and a sample with 577 consumers was investigated.Design/methodology/approachBased on the stimulus–organism–response (SOR) model, this study aims to explore how channel integration affects usage intention through perceived value in food delivery platform (FDP) services. Moreover, the author also examines the moderating effects of personal innovativeness and experience on the relationships in the model.FindingsThe study found that channel integration affects usage intention through perceived usefulness, perceived enjoyment and perceived price. Moreover, the moderating effects of personal innovativeness and experience are both significant in the model.Research limitations/implicationsThis study found that perceived usefulness, perceived enjoyment and perceived price are three major values influencing the relationship between channel integration and usage intention in FDP services. Moreover, for consumers with high personal innovativeness, perceived usefulness, perceived enjoyment, social image and perceived risk affecting usage intention will be weaker than for consumers with low personal innovativeness. However, for highly experienced consumers, perceived usefulness, perceived enjoyment and perceived price affecting usage intention will be stronger than for less experienced consumers.Practical implicationsThis study suggests that practitioners should develop value-driven innovative services and activities by integrating various channels for customers. Moreover, they should segment consumers on the basis of different levels of personal innovativeness and experience to provide different strategies for increasing the intention to use the service.Originality/valueThis study advances the extant knowledge of the SOR model in the context of online-to-offline commerce.

Pengaruh Citra Merek, Kepercayaan Merek, dan Keamanan Privasi Terhadap Keputusan Pengguna Gojek

Gojek is an Indonesian technology company engaged in services through motorcycle taxi services. Gojek itself has more than 20 services offered and is a solution to everyday challenges, ranging from transportation, food delivery, shopping, delivery of goods, payments, massages, to cleaning houses and vehicles. Researchers conducted research on Gojek application users in Surabaya. This research is a research with quantitative method. The sampling technique used is purposive sampling and uses questions on a questionnaire distributed to 75 respondents who are Gojek application users who are at least 17 years old and have used the Gojek application in Surabaya at least 2 times. Thisresearch was conducted using validity test, reliability test, descriptive test, classical assumption test (normality test, multicollinearity test, heteroscedasticity test), multiple linear regression test, determinant coefficient, F test, and t test. The t-test in this study states that the brand image variable (X1) on purchasing decisions (Y) has a t-count value of 1.624 < t-table 1.993 with a significant level of 0.109 > 0.050 which means that brand image has no effect on purchasing decisions of Gojek application users, the trust variable brand (X2) on purchasing decisions (Y) has a t value of 3.209 > t table 1.993 with a significant level of 0.002 < 0.050 which means that brand trust affects the purchasing decisions of Gojek application users, and the privacy security variable (X3) on purchasing decisions (Y) has a t value of 4.018 > t table of 1.993 with a significant level of 0.000 < 0.050 which means that privacy security affects the purchasing decisions of Gojek application users. The results of this study conclude that brand trust and privacy security have a significant effect on the decisions of Gojek users in Surabaya, while brand image does not affect the decisions of Gojek users in Surabaya.

Living through the COVID-19 Pandemic: Impact and Lessons on Dietary Behavior and Physical Well-Being

The aim of this review is to highlight the spectrum on which human behavior has been affected by blanket restriction measures and on a wider scale, the COVID-19 pandemic. Some of the human behaviors that have been impacted by the COVID-19 lockdown are dietary behavior and nutrition, food options and food delivery usage, physical activity and sedentary behaviors. This is important in planning effective public health strategies with minimal detriment to all subsets of society as well as improving the distribution of government aid to populations that are more severely affected. Our main purpose is to present the literature from a rapidly growing pool of scientific research to hopefully enable a better and more comprehensive understanding of the effects of this pandemic and the lessons learnt from the accompanying restrictions, as well as policy recommendations that can be made in national pandemic responses in the future.

Modeling the public attitude towards organic foods: a big data and text mining approach

This study aims to identify the topics that users post on Twitter about organic foods and to analyze the emotion-based sentiment of those tweets. The study addresses a call for an application of big data and text mining in different fields of research, as well as proposes more objective research methods in studies on food consumption. There is a growing interest in understanding consumer choices for foods which are caused by the predominant contribution of the food industry to climate change. So far, customer attitudes towards organic food have been studied mostly with self-reported methods, such as questionnaires and interviews, which have many limitations. Therefore, in the present study, we used big data and text mining techniques as more objective methods to analyze the public attitude about organic foods. A total of 43,724 Twitter posts were extracted with streaming Application Programming Interface (API). Latent Dirichlet Allocation (LDA) algorithm was applied for topic modeling. A test of topic significance was performed to evaluate the quality of the topics. Public sentiment was analyzed based on the NRC emotion lexicon by utilizing Syuzhet package. Topic modeling results showed that people discuss on variety of themes related to organic foods such as plant-based diet, saving the planet, organic farming and standardization, authenticity, and food delivery, etc. Sentiment analysis results suggest that people view organic foods positively, though there are also people who are skeptical about the claims that organic foods are natural and free from chemicals and pesticides. The study contributes to the field of consumer behavior by implementing research methods grounded in text mining and big data. The study contributes also to the advancement of research in the field of sustainable food consumption by providing a fresh perspective on public attitude toward organic foods, filling the gaps in existing literature and research.