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Artificial intelligence (AI) is not a new field. The term was coined in 1956, but the field has only recently begun having significant effects on the economy. Research in AI [...]

How do firms benefit from integrated enterprise systems (IES), and how does the IES implementation strategy influence the returns from IES? We investigated the implementation strategy that firms should follow to integrate multiple enterprise systems regarding the timing of adoption and the richness of modules. Borrowing theories from software development literature and enterprise system implementation literature, we developed two IES implementation strategies, i.e., the agile strategy (simple, quick, and flexible) and the phased strategy (rich, phased, and pre-determined). We collected a sample of 675 public firms from 1997 to 2004 at the module level, enabling us to distinguish enterprise resource planning (ERP) systems from customer relationship management (CRM) and supply chain management (SCM) systems. Our data contained the timing of purchase and "go-live" events of these system modules, helping us understand firms' detailed implementation decisions. We found that IES returns depend on the choices of ERP and CRM/SCM module foundation, ERP-CRM/SCM sequential connection, and the continued adoption of ERP and CRM/SCM modules. Fewer ERP or CRM/SCM modules at CRM/SCM go-live events, a quicker connection between ERP and CRM/SCM go-live events, and the continued adoption of ERP or CRM/SCM modules all were found to enhance IES returns. Our findings show that the agile strategy leads to more returns from IES than the phased strategy and suggest that firms should integrate multiple enterprise systems by going live with advanced system modules quickly after going live with basic enterprise system modules and by continually adding new modules. [ABSTRACT FROM AUTHOR]

As digitization sweeps across industries and markets, analytics and data-driven decision making (DDD) are becoming increasingly important. The adoption of analytics and DDD has been slower in small-to-medium enterprises (SMEs) compared with large firms, and reliable causal estimates of the impacts of analytics tools for small businesses have been lacking. We derive experiment-based estimates of the impact of an analytics tool on SME outcomes, analyzing the randomized introduction of eBay's Seller Hub (SH), a data-rich seller dashboard. We find that SH adoption is associated with increased DDD and that access to SH increases e-retailers' revenues by 3.6% on average, as more items are transacted and service quality increases, without increases in average prices. Our results suggest that analytics and DDD help SMEs establish a competitive advantage. Managerial practices play an important role in reaping the benefits from the analytics dashboard, as more than a third of the SH impact is driven by active performance monitoring. Digital platforms should therefore embed analytics tools to support SMEs to increase entry and revenues. Furthermore, policies to support small businesses' transition to the data era should address analytics and DDD gaps by ensuring access to both tools like SH and to appropriate managerial training. This paper was accepted by Anindya Ghose, information systems. Funding: This work was supported by the United States-Israel Binational Science Foundation [Grant 2018263]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.02026. [ABSTRACT FROM AUTHOR]

We develop a framework to measure the value of free goods and services available on the internet. The conventional method of measuring consumer surplus based on monetary expenditures is ineffective because these goods' prices are predominantly zero. As the saying goes, time is money, and thus, our method addresses this challenge by quantifying the value of the time that consumers devote to consuming these free goods. We apply our model to data on the allocation of individuals' time among internet, television, leisure, and work, and this enables us to estimate the consumer surplus of free goods. We find that the average incremental welfare gain from the internet for each year between 2002 and 2011 was about $38 billion per year in the United States, which is equivalent to approximately 0.29% of the annual GDP. In contrast, if we had not considered the value of time, then the estimated annual incremental welfare gain would have been only $2.7 billion, which is barely 7% of the estimate derived from our model, which considers the time spent on consuming these goods. Our approach can be readily extended to the valuation of other zero-priced goods and services, such as television. In addition, our results show the importance of not only quantity but also quality (e.g., internet speed) in determining the welfare contributions of free goods. We develop a framework to measure the value of free goods and services available on the internet. The conventional method of measuring consumer surplus based on monetary expenditures is ineffective because these goods' prices are predominantly zero. Our proposed method addresses this challenge by quantifying the economic value of the time that consumers devote to consuming these free goods. Using data on consumers' time and monetary expenditures, we calibrate an economic model of the allocation of individuals' time among internet, television, leisure, and work. We measure the consumer surplus of free goods on the internet as the reduction in gross domestic product (GDP) required to create an equivalent welfare loss to that which would occur if these free goods were no longer available. We find that the average incremental welfare gain from the internet between 2002 and 2011 was about $38 billion per year in the United States, equivalent to approximately 0.29% of the annual GDP. In contrast, if we had not considered the value of time, then the estimated annual incremental welfare gain would have been significantly smaller at about $2.7 billion, only 7% of the estimate derived from our proposed time-based model. Our approach can be readily extended to the valuation of other zero-priced goods and services, such as television. In addition, our results show the importance of not only quantity but also quality (e.g., internet speed) in determining the welfare contributions of free goods. History: Eric Zheng, Senior Editor; Yan Huang, Associate Editor. Funding: This work was supported by the Stanford Digital Economy Laboratory. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2021.0153. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (AI) has been touted as a potential innovation for greater diagnostic accuracy and clinical efficiency in health care, but whether generative AI will deliver on this promise or fall to the "productivity paradox" is examined in this Special Communication. Importance: Since the introduction of ChatGPT in late 2022, generative artificial intelligence (genAI) has elicited enormous enthusiasm and serious concerns. Observations: History has shown that general purpose technologies often fail to deliver their promised benefits for many years ("the productivity paradox of information technology"). Health care has several attributes that make the successful deployment of new technologies even more difficult than in other industries; these have challenged prior efforts to implement AI and electronic health records. However, genAI has unique properties that may shorten the usual lag between implementation and productivity and/or quality gains in health care. Moreover, the health care ecosystem has evolved to make it more receptive to genAI, and many health care organizations are poised to implement the complementary innovations in culture, leadership, workforce, and workflow often needed for digital innovations to flourish. Conclusions and Relevance: The ability of genAI to rapidly improve and the capacity of organizations to implement complementary innovations that allow IT tools to reach their potential are more advanced than in the past; thus, genAI is capable of delivering meaningful improvements in health care more rapidly than was the case with previous technologies. [ABSTRACT FROM AUTHOR]