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Current literature argues that citizen engagement platforms must be used to gather citizens’ feedback to provide improved quality of services to citizens. However, limited studies consider the challenges faced by practitioners at the local level during the incorporation of those feedback for continuous service improvement. As a result, these services fail to fulfil the need of citizens. The purpose of this study is to structure the relationship between citizens’ feedback and continuous service improvement to meet the need of citizens.

Design science research methodology has been adapted under which a case study approach has been followed to investigate one of the citizens’ engagement platforms in Ireland.

The results from this study highlighted that practitioners faced challenges (e.g. capacity, risk and constraints) in terms of fulfilling the needs of citizens and there is a lack of structured approach to continuously provide improved services to them.

This study provides a structured approach in the form of a process model to showcase how citizens’ feedback can be incorporated for continuously providing improved services to the citizens.

This research provides a prescriptive view to assist municipalities during the incorporation of citizens’ feedback for continuous service improvement while addressing the challenges they face during this process.

This paper proposes a process model based on the guidelines of the open group architecture framework enterprise architecture and the collaboration with practitioners that would assist local authorities in continuously providing improved services to the citizens.

This paper aims to examine the presence of impulsive and compulsive buying among consumers. It studies the various factors that affect and moderate the impulsiveness and compulsiveness of buying.

Literature review resulted in four constructs – social media influence, social media preferences, hedonic motivation and shop in COVID-19. On conducting factor analysis in statistical package for the social sciences, the variables were divided under the influence of social media, social commerce, electronic word of mouth (EWOM) of social commerce, hedonic happiness, hedonic fun and shopping in times of COVID-19. Structural equation modeling is conducted in AMOS (statistical software) for a diagrammatic representation of the relationship between the variables. Regression analysis is used to re-affirm the above relationship. Testing of hypotheses is done with the help of the chi-square test.

All six latent variables are significantly related to impulsive and compulsive buying. However, the regression analysis shows social media influence as the strongest predictor for impulse buying and hedonic happiness for compulsive buying. Also, the presence of the pandemic COVID-19 leads to impulsive buying as well as compulsive buying in the apparel and accessory segment.

Marketers should capitalize on spontaneous buying in both forms – impulsive buying and compulsive buying. Social media influencers, as well as more consumer engagement on social media, can promote impulsive buying. However, compulsive buyers will be more attracted towards great in-store experiences or hedonically driven advertisements, as they do not just shop for buying the product; they shop for the experience of shopping.

This study uncovers the difference in factors that affect impulsive and compulsive buying. Though both behaviours seem points of the same scale, they are inherently different and can be predicted with social media influence and hedonic happiness.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.