Read the article below and Write a 250 to 300-word response. In your reply you must make a recommendation of a peer reviewed journal article that provides additional information on the topic. In your response, you should give a summary of the article in your own words and discuss why it is relevant to the article below. It must be different than articles reference in the article below. Include an APA formatted in text citation and at the bottom of the reply.
Hughes-Cromwick and Coronado’s (2019) article discusses the importance of economic data in shaping business decisions. The study highlights that access to reliable and up-to-date economic data is crucial for businesses to make informed choices, understand market trends, assess risks, and identify opportunities (Hughes-Cromwick & Coronado, 2019). The article also explores how economic data can aid in strategic planning, investment decisions, and resource allocation. By utilizing economic indicators and forecasts, businesses can better adapt to changing economic conditions and mitigate potential adverse impacts. Highlighted is that economic data provides valuable insights for policymakers, as it helps them design effective economic policies and foster a conducive business environment.
Current research on economic data has focused on both the business perspective to improve business actives. Gottfried et al. (2021) found that using text mining of open government data (OGD) can increase business intelligence. Business intelligence involves using internal and external sources to collect, analyze, and process information through software and fact-based systems to assist in decision making and strategy formulation with the end goal of a competitive advantage. Specifically, Gottfried et al. (2021) demonstrated through text mining across industries increases knowledge of resource availability for production and market opportunities. Guan et al. (2022) highlighted the urgency the State Council of the Communist Party of China Central Committee stressing the importance of data production factor (DPF) in convicting industry, business models, and digital economy. Like Gottfried et al. (2021), Guan et al. (2022) utilized text mining to show that using open data resulted in a significant improvement in earnings per share. Where Guan et al. (2022) examined support by China in the use of open data, Huber et al.’s (2022) research found that countries that do not maintain an understanding of the implementation of rules supporting open data, contractual relationships and market exchanges did not see the same benefits of open data on entrepreneurship.
Current research on open data usage has also been conducted from the viewpoint of the consumer. Jones and Tonetti (2020) examined economic data in terms of property rights, who should own or regulate the data for its use, and the implications that may come to light. The study showed that while regulations can protect consumer privacy, it also limits the amount of data shared. Limiting data available can negatively impact in that data used can help diagnostics and lifesaving procedures (Jones & Tonetti, 2020). Jones and Tonetti (2020) also researched the idea of firms owning data which ultimately leads to the creation of barriers to entry in the market. Finally, Jones and Tonetti (2020) revealed that consumer choice of what data is collected and shared acts as a balancing mediator for data concerns in terms of consumer privacy and access to open data. Lee et al. (2023) proposed utilizing economic data of grocery shopping habits and trends as a way assess consumer creditworthiness to provide alternative lending options to low-income consumers. The study utilized tradition creditworthiness assessment and grocery data as a comparison (Lee et al., 2023). Lee et al. (2023) found that traditional means of assessment predicted lower-income consumers to be risker credit users while grocery data showed that lower-income consumers were less risky than higher-income consumers.
Future research opportunities are suggested by Gottfried et al. (2021) to advance open-source intelligence through studying different types of techniques and integrating more dynamic data sources and combining topic models for predictive marketing models. Future research should also examine how sub-types of institutional quality affects specific entrepreneurial outcomes through the empirical study of country-level characteristics with experimental methodology (Huber et al., 2022) when using open data. Future studies are also recommended on open government data among different countries and their likelihood to use OGD (Gottfried et al., 2021; Guan et al., 2022). Guan et al. (2022) suggests that their research be expanded to consider other performance measurements than earnings per share to further capture the benefits of using DPFs. Jones and Tonetti (2020) suggest that researchers examine the quantity of data available for use in various industries in terms of productivity advantage and the implications of consumer limited data. While generating possible alternative credit and leading determinants of creditworthiness, Lee et al. (2023) recommends research be conducted to evaluate the ethical decision making behind using grocery data as an approval to creditworthiness.
Gottfried, A., Hartmann, C., & Yates, D. (2021). Mining open government data for business intelligence using data visualization: A two-industry case study. Journal of Theoretical and Applied Electronic Commerce Research, 16(4), 1042-1065. https://doi.org/10.3390/jtaer16040059.
Guan, R., Fan, R., Ren, Y., Lu, F., & Wang, H. (2022). The casual effect of data production factor adoption on company performance: Empirical evidence from Chinese listed companies with PSM-DID. Frontiers in Environmental Science, 10, 1-11. https://doi.org/10.3389/fenvs.2022.939243.
Huber, F., Ponce, A., Rentocchini, F., & Wainwright, T. (2022). The wealth of (open data) nations? open government data, country-level institutions and entrepreneurial activity. Industry and Innovation, 29(8), 992-1023. https://doi.org/10.1080/13662716.2022.2109455.
Hughes-Cromwick, E., & Coronado, J. (2019). The value of US government data to US business decisions. The Journal of Economic Perspectives, 33(1), 131-146. https://doi.org/10.1257/jep.33.1.131.
Jones, C., & Tonetti, C. (2020). Nonrivalry and the economics of data. American Economic Review, 110(9), 2819-2858. https://doi.org/10.1257/aer.20191330.
Lee, J., Yang, J., & Anderson, E. (2023). Using grocery data for credit decisions. Behavioral & Experimental Finance. Advance online publication. https://doi.org/10.2139/ssrn.3868547.