Social Media and Materialism in Consumer Spending Behavior--Model

2018 ◽  
Author(s):  
Amonrat Thoumrungroje
Connections ◽  
2017 ◽  
pp. 281-316
Author(s):  
James E. Katz

2019 ◽  
Author(s):  
Ali M. Shah ◽  
Syed Zeeshan Zahoor ◽  
Ishtiaq Hussain Qureshi

2017 ◽  
Vol 11 (3) ◽  
pp. 456-475 ◽  
Author(s):  
Daphna Shwartz-Asher ◽  
Soon Ae Chun ◽  
Nabil R. Adam

Purpose A social media user behavior model is presented as a function of different user types, i.e. light and heavy users. The users’ behaviors are analyzed in terms of knowledge creation, framing and targeting. Design/methodological approach Data consisting of 160,000 tweets by nearly 40,000 twitter users in the city of Newark (NJ, USA) were collected during the year 2014. An analysis was conducted to examine the hypothesis that different user types exhibit distinct behaviors driven from different motivations. Findings There are three important findings of this study. First, light users reuse existing content more often, while heavy and automated users create original content more often. Light users also use more sentiments than the heavy and automated users. Second, automated users frame more than heavy users, who frame more than light users. Third, light users tend to target a specific audience, while heavy and automated users broadcast to a general audience. Research implications Decision-makers can use this study to improve communication with their customers (the public) and allocate resources more effectively for better public services. For example, they can better identify subsets of users and then share and track specialized content to these subsets more effectively. Originality/value Despite the broad interest, there is insufficient research on many aspects of social media use, and very limited empirical research examining the relevance and impact of social media within the public sector. The social media user behavior model was established as a framework that can provide explanations for different social media knowledge behaviors exhibited by various subsets of users, in an e-government context.


2020 ◽  
pp. 016001762094281
Author(s):  
Laura Medwid ◽  
Elizabeth A. Mack

Rising infrastructure costs for water providers and the rising cost of water for households pose several challenges for water providers, policy makers, and the research community. Consumers may utilize several strategies for coping with rising water costs including reduced water use or spending reductions on other household goods and services. To provide a first glance at the link between rising water bills and consumer spending, this study analyzes data from a household survey in the United States to understand how consumers may change spending behavior given various water bill increase scenarios. Results of this analysis provide insights into the demographics of households likely to be affected, the industries that could be affected, and at what bill increase levels these trends are most pronounced. While additional research on this topic is needed, these results suggest a stronger emphasis on long-term water management planning and allocation of resources to building and maintaining water infrastructure may be required. For utilities, this means a consideration of nonrevenue sources of funds to pay for rising water costs and strategies for making water more affordable for customers without deferring infrastructure improvements.


2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Ava Chae ◽  
Janet Hanson

The COVID-19 pandemic is no longer simply a “health crisis” but seems to have far-reaching impacts and implications as well. The pandemic has led to an unprecedented decline in consumer confidence (demand shock) as people quickly changed their spending behavior to focus on basic needs. In response, the government has taken actions to boost consumer confidence and spending through stimulus packages and other aid. This research uses a public database of private sector data with current information on consumer spending and employment and implements Abraham Maslow’s psychological theory, the hierarchy of needs, and Keynesian economics to explain consumer spending behaviors and the U.S. government’s response to the pandemic. Three hypotheses are developed and tested using regression analysis. First, the findings of the regression discontinuity show significantly higher spending in industries fulfilling basic needs than in nonessential sectors. Second, a causal relationship was found between aggregate demand, more specifically consumer spending, and employment, revealing the distinctness of the pandemic-caused recession driven by coronavirus fear. The final regression discontinuity was tested to observe the effect of stimulus checks (CARES Act) on spending and economic recovery, revealing positive impacts of boosting aggregate demand. This study provides evidence that while high-income households and individuals were the least impacted by the pandemic regarding employment, they showed the most dramatic changes in spending behavior. The study provides discussions and implications as to how to mitigate the effects of the COVID-19 recession in the U.S.


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