Impact of Emerging Digital Technologies on Firms’ Financial Performance, Inventory Efficiency, and Greenhouse Gas Emissions: An Event Study

Abstract

This study investigates the performance consequences of adopting emerging digital technologies such as artificial intelligence, machine learning, big data analytics, and cloud computing, with attention to financial, operational, and environmental dimensions. Using an event study of 134 adoption announcements by publicly traded U.S. firms from 2009 to 2019, we compare adopters with matched control firms identified through propensity score matching. The empirical evidence shows that adoption is followed by gains in profitability and market valuation, reflected in improvements in return on assets, return on equity, and Tobin’s Q, alongside higher inventory turnover. At the same time, adopting firms exhibit a measurable decline in greenhouse gas emissions when compared with matched control firms. Taken together, these results suggest that digital transformation can align economic performance with environmental improvement, rather than forcing firms to choose between the two. The findings therefore provide practical guidance for managers and policymakers seeking to evaluate digital investments through the lens of long-term sustainability.

1. Introduction

Emerging digital technologies comprise a set of interconnected information systems that include artificial intelligence, machine learning, big data analytics, and cloud computing. These technologies are widely regarded as economically consequential. McKinsey Global Institute [1], for example, estimates that their combined annual economic impact could reach between $14 and $33 trillion by 2025. This expectation is reflected in the strategic investments made by leading technology firms such as OpenAI (San Francisco, CA, USA), Microsoft (Redmond, WA, USA), Salesforce (San Francisco, CA, USA), Oracle (Austin, TX, USA), Google (Mountain View, CA, USA), Amazon (Seattle, WA, USA), SAP (Walldorf, Germany), and IBM (Armonk, NY, USA), all of which have expanded their portfolios of AI, analytics, and cloud-based solutions to support improvements in organizational efficiency and performance.

Across industries, firms have increasingly embedded these technologies within operations and supply chain management activities. Walmart’s announcement in 2023 to open its fifth highly automated fulfillment center by 2026 illustrates how digital technologies are being leveraged to enhance order accuracy and processing speed [2]. Amazon similarly relies on AI and machine learning to support demand forecasting, inventory optimization, and routing decisions [3]. In the automotive sector, Tesla applies advanced AI-based systems to strengthen the capabilities of its Autopilot and Full Self-Driving platforms [4]. Beyond retail and manufacturing, energy firms have also adopted these tools. ExxonMobil’s collaboration with Microsoft integrates cloud computing and machine learning to improve operational profitability while simultaneously reducing methane emissions in its Permian Basin operations [5].

Despite growing evidence of performance benefits, the adoption of emerging digital technologies remains associated with substantial costs, risks, and managerial challenges. Implementation expenses are frequently underestimated, and realized business value is often uncertain, particularly when these technologies must be integrated into complex and tightly coupled OSCM processes [6]. Importantly, successful adoption extends beyond the procurement of hardware and software. It requires sustained investment in technological infrastructure as well as deliberate efforts to cultivate organizational capabilities and cultural readiness that support long-term digital transformation [7].

This study lies at the intersection of two related streams of empirical research in operations and supply chain management. One stream investigates the performance implications of investments in established enterprise systems, including enterprise resource planning, supply chain management, customer relationship management, and supplier relationship management platforms [8,9,10,11,12]. A second stream examines the deployment of emerging digital technologies across a range of OSCM functions, such as predictive analytics, procurement, inventory control, customer service, service-level management, and process improvement [13,14,15,16,17,18,19,20].

Building on these studies, this study advances research on digital transformation and firm performance in several distinct ways. Firstly, it extends event-study analyses of Industry 4.0 investments, particularly Bai et al. [21], by concentrating explicitly on emerging digital technologies, including artificial intelligence, machine learning, big data analytics, and cloud computing. Whereas prior work considers a broad combination of physical and digital Industry 4.0 initiatives, our focused examination enables a more precise assessment of the value generated by data-intensive technologies that increasingly inform managerial decision-making. Secondly, the study expands the scope of performance outcomes considered. Existing research has largely emphasized stock market responses and financial indicators, with limited attention to operational and environmental consequences. Our findings demonstrate that the adoption of emerging digital technologies is associated with improvements in inventory efficiency and reductions in greenhouse gas emissions, thereby directly connecting digital transformation to both supply chain performance and environmental sustainability. Finally, this study contributes evidence from the U.S. context, complementing prior findings drawn primarily from Chinese firms operating within government-driven industrial policy environments. By examining a market-oriented institutional setting, we enhance the external validity of prior results and provide insight into technology adoption decisions shaped largely by competitive and economic considerations.

To the best of our knowledge, prior OSCM research has not examined the effects of investments in emerging digital technologies on a firm’s long-term financial, operational, and environmental performance. Therefore, this study contributes to both streams of OSCM research by addressing the following three main research questions:

• RQ1: What is the effect of investing in emerging digital technologies on a firm’s long-term financial performance?
• RQ2: What is the effect of investing in emerging digital technologies on a firm’s long-term operational performance?
• RQ3: What is the effect of investing in emerging digital technologies on a firm’s long-term environmental performance?

The resource-based view (RBV) posits that sustained competitive advantage arises when firms control resources that are valuable, rare, difficult to imitate, and not easily replaced [22]. From this perspective, emerging digital technologies, including artificial intelligence, machine learning, big data analytics, and cloud computing, can function as strategic assets by strengthening managerial decision-making, improving operational efficiency, and supporting environmental performance. However, access to these technologies alone does not guarantee superior outcomes. Realizing their potential requires the development of dynamic capabilities, through which firms identify new technological opportunities, commit resources to their adoption, and adapt organizational processes to embed digital tools into day-to-day operations [23].

Although these technologies offer substantial promise, firms often approach their adoption cautiously, reflecting ongoing uncertainty related to implementation costs, operational risks, and environmental implications. Grounded in the combined insights of RBV and dynamic capabilities theory, this study examines how the adoption of emerging digital technologies influences financial performance, inventory efficiency, and greenhouse gas emissions among publicly traded firms in the United States.

From a sustainability perspective, emerging digital technologies can be examined through the triple bottom line (TBL) framework, which calls for the joint consideration of economic performance, operational and social outcomes, and environmental impact [24,25]. Viewed through this lens, sustainable firm performance is not confined to short-term financial results but also reflects how effectively firms manage operational processes and limit their environmental footprint. Technologies such as artificial intelligence, machine learning, big data analytics, and cloud computing have therefore gained prominence as strategic enablers of TBL-oriented initiatives. By improving information visibility, strengthening the quality of managerial decisions, and supporting more efficient use of resources, these technologies shape sustainability outcomes across organizational functions and interconnected supply chain activities [26,27,28].

Within the triple bottom line perspective, economic performance reflects the extent to which firms translate digital investments into measurable financial gains. Prior research suggests that productivity improvements, enhanced forecasting accuracy, and more effective asset utilization, all supported by data-driven decision making, play a central role in this process [29,30]. Operational performance is shaped by how effectively firms deploy digital technologies to improve inventory control, align internal processes, and coordinate activities with supply chain partners, thereby strengthening efficiency, responsiveness, and reliability [31,32]. Environmental performance is similarly influenced by firms’ ability to use advanced analytics and digital infrastructure to monitor, measure, and manage emissions. Through improved demand and supply alignment, reductions in excess inventory, and more efficient transportation and production decisions, emerging digital technologies can support meaningful decreases in greenhouse gas emissions and material waste [21,33,34].

Although strong conceptual links have been established between digital transformation and the triple bottom line, empirical evidence that simultaneously examines financial, operational, and environmental outcomes remains relatively scarce, particularly with respect to emerging digital technologies. Existing studies tend to emphasize economic performance or focus on narrowly defined operational improvements, often giving limited attention to environmental consequences or positioning sustainability as a secondary consideration [35,36]. In response to this gap, the present study evaluates the effects of investments in emerging digital technologies across all three dimensions of the triple bottom line, providing a more integrated assessment of digital transformation within the broader context of sustainable development.

Using event study methods, our research is based on a sample of 134 emerging digital technology adoptions announced by publicly traded firms between 2009 and 2019. Our analysis shows that investments in emerging digital technologies improve a firm’s financial and operational performance, as measured by return on assets (ROA), return on equity (ROE), Tobin’s Q, and inventory turnover, especially after the implementation phase. Additionally, an analysis of a sub-sample of 87 emerging digital technology announcements reveals that these investments reduce firms’ greenhouse gas (GHG) emissions. Our results on the long-term performance benefits of emerging digital technologies are important to managers hesitant to adopt emerging digital technologies, by alleviating their concerns regarding the costs, risks, and challenges associated with emerging digital technology adoptions.

The rest of the paper is organized as follows. Section 2 discusses related literature and hypotheses development. Section 3 details our event study methodology for empirical analysis. Section 4 presents the results of this study. Section 5 provides additional within-industry analysis. Finally, in Section 6, we discuss the insights gained from this research, its limitations, and potential directions for future research.

2. Related Literature and Hypothesis Development

From a resource-based view, information technologies are widely recognized as strategic assets that can enhance firm profitability, market valuation, and operational performance [37]. Emerging digital technologies build on this foundation by supporting advanced analytics, automation, and scalable data processing, thereby strengthening financial outcomes, and improving inventory efficiency [30]. As these technologies become increasingly accessible, however, competitive advantage is less likely to arise from ownership alone. Instead, performance differences are shaped by how effectively firms deploy and embed digital tools within their organizational processes.

Dynamic capabilities theory offers insight into why firms with comparable technological resources often realize markedly different outcomes. Organizations that are more adept at identifying technological opportunities, committing resources to innovation, and reconfiguring internal routines are better positioned to translate artificial intelligence, machine learning, big data analytics, and cloud computing into sustained operational and strategic gains [38]. Prior research further suggests that these capabilities support not only financial performance but also environmental outcomes, as they enable more adaptive, data-driven decision making and more efficient use of resources [39].

This study is generally related to two streams of empirical research in OSCM literature. The first stream examines the performance effects of investments in traditional enterprise technologies and systems, while the second stream focuses on the applications of emerging digital technologies in various OSCM domains. Conventionally, information technologies and systems have contributed to OSCM by providing ad hoc information for making better strategic and operational decisions. The most iconic research stream is the use of information sharing across different layers of the supply chain [11,40,41]. In the domain of traditional enterprise technologies and systems, early OSCM research explores the benefits and costs of ERP systems with mixed results [42]. For example, Poston and Grabski [43] find no or little evidence that ERP adoption improves profitability or operational performance. In contrast, Hitt et al. [8], Hendricks et al. [10], and Cao et al. [44] provide strong evidence that ERP investments enhance firm performance, which may be attributed to the efficient use of enterprise information [45]. Subsequent research shifts to CRM systems [10,46], SRM systems [12,47,48], and SCM systems [9,10]. For instance, Dehning et al. [9] find that investment in SCM systems increases a firm’s gross margin and inventory turnover, but not ROA, while Hendricks et al. [10] find that investment in SCM systems improves ROA. This study contributes to this domain of research by examining the effects of emerging digital technologies on a firm’s future financial, operational, and environmental performance.

In the domain of applications of emerging digital technologies, a growing body of empirical research has focused on the applications of these technologies and systems in various OSCM domains, such as predictive analytics and demand forecasting [15,49,50,51]; sourcing and procurement [18,52]; customer service [17]; productivity improvement [19], profitability and risks [53]; and environmental benefits and risks [15,20]. These technologies improve firms’ operational and financial performance by strengthening supply chain resilience and adaptability to disruptions [54]. We refer to Kuo & Kusiak [55], Choi et al. [56], Ajmal et al. [57], and Chen et al. [58] for detailed literature reviews on applications of emerging digital technologies in OSCM. This study contributes to this domain of research by investigating the overall performance effects of investments in emerging digital technologies.

2.1. Emerging Digital Technologies and Financial Performance

The business value of emerging digital technologies stems from their capacity to integrate, structure, and analyze data drawn from multiple sources, including customers, supply chain partners, and competitors, and to convert this information into actionable insights that inform managerial decision making [59]. Through their operational deployment, these technologies support revenue growth and cost reduction, thereby contributing to improved financial performance.

Technologies such as artificial intelligence, machine learning, big data analytics, and cloud computing enable firms to identify market trends and shifts in customer preferences with greater precision. These capabilities enhance demand forecasting accuracy and improve the quality of information used in pricing and sales decisions. Empirical evidence illustrates these effects. Ferreira et al. [49] show that the application of AI and machine learning to demand forecasting and pricing at the online retailer Rue La La resulted in a revenue increase of 9.7 percent. Glaeser et al. [60] report even larger gains, demonstrating revenue improvements of up to 36 percent for a retailer operating a buy online, pick up in store model. Beyond revenue effects, Ilk et al. [17] find that an intelligent customer routing system based on big data analytics and machine learning significantly enhances service performance in live chat contact centers. Cloud computing platforms further reinforce these outcomes by providing firms with access to real time data and scalable analytical capabilities. Major providers such as Amazon, Oracle, Salesforce, and Microsoft enable organizations to generate timely business insights that can translate into higher sales revenue [61].

Emerging digital technologies also support cost reduction by enabling managers to identify and address operational inefficiencies. When effectively deployed, these technologies enhance inventory management, facilitate more efficient allocation of resources, and improve service responsiveness across logistics and supply chain operations [62]. Prior studies provide evidence of these mechanisms in practice. Spring et al. [19] show that AI-based systems contribute to process improvements and the expansion of service offerings in professional service contexts. Similarly, Cui et al. [18] find that procurement costs decline when firms simultaneously adopt automation and intelligent features within AI-enabled systems. Cloud computing further contributes to cost efficiency by offering scalable and flexible computing and storage capacity, thereby reducing the need for costly investments in on-premise infrastructure [58].

Beyond individual applications, big data analytics in supply chain management has been shown to strengthen both operational and financial performance by supporting cost reduction, improving the quality of managerial decisions, and enabling more efficient, data-driven logistics strategies [63]. These benefits are reinforced when digital technologies are aligned with firm strategy, as improved strategic fit and resource utilization enhance overall financial outcomes [64]. In addition, artificial intelligence contributes to financial performance by strengthening supply chain resilience through improved environmental sensing, enhanced transaction security, and greater capacity for adaptive reconfiguration [65].

Therefore, we posit that in the long run, investments in emerging digital technologies will positively influence the financial performance of firms. We consider three alternative financial performance metrics: ROA and ROE as profitability measures following Hendricks et al. [10] and Tobin’s Q as a measure of firm market value following Modi and Mishra [66].

H1a.

Adoption of emerging digital technologies is positively associated with a firm’s ROA.

H1b.

Adoption of emerging digital technologies is positively associated with a firm’s ROE.

H1c.

Adoption of emerging digital technologies is positively associated with a firm’s Tobin’s Q.

2.2. Emerging Digital Technologies and Operational Performance

Concerning operational performance, prior empirical OSCM research has shown that traditional enterprise systems such as ERP can improve lead time and on-time delivery performance [67,68]. For example, Shah and Shin [69] examine the association among information system (IS), inventory, and profitability across various industry sectors. They observed a positive association between IS and profitability, which varied across sectors. Further, inventory was found to function as a mediator in the relationship between IS and profitability, and that mediation effect was also industry specific. Mishra et al. [12] also demonstrate a positive correlation between a company’s IT capability and its inventory efficiency. The adoption of emerging digital technologies can significantly enhance inventory performance by improving demand forecasting accuracy, optimizing replenishment processes, and reducing inventory costs. Artificial intelligence applications in supply chain management enhance firms’ operational performance by improving sensing, learning, and decision-making capabilities that drive efficiency and responsiveness [70]. AI-enabled technologies can also dynamically control and adjust inventory levels, facilitating responsive inventory replenishment decisions [71]. ML-based approaches, such as cross-item learning, have demonstrated improved demand forecast accuracy across volatile demand environments [51], while cloud-based supply chain frameworks leverage ML to optimize inventory allocation, achieving forecast accuracy improvements of up to 16% [72]. Integrating machine learning for advanced demand forecasting, supplier selection, and order allocation enhances firms’ operational and financial performance by improving forecasting accuracy, optimizing supplier decisions, and reducing costs under demand uncertainty [73]. Cloud computing can also enhance varying levels of supply chain collaboration and, in turn, significantly improve service levels, particularly through business intelligence-enabled collaborative planning, forecasting, and replenishment [14]. Furthermore, AI applications in manufacturing can improve supplier selection, reduce inventory levels, and optimize production scheduling [74] along with reducing costs and lead times and improving quality, service levels, and sustainability [75]. Advances in BDA enable firms to make informed inventory decisions, providing a competitive edge [13,76]. Moreover, Qi et al. [77] conduct a field experiment with JD.com and demonstrate that ML algorithms can increase inventory turnover substantially compared with JD’s current practice. Implementing big data strategies has shown to enhance firms’ operational and strategic performance by unlocking business value through improved insights and decision-making [78]. Furthermore, big data analytics improve firms’ operational performance by enhancing logistics planning, resource utilization, and decision-making on manufacturing shopfloors [79]. AI has enhanced firms’ equipment maintenance, reducing downtime and increasing the availability of production systems [80]. Hence, we expect emerging digital technology adoption to have a positive relationship with inventory performance, as measured by inventory turnover.

H2.

Adoption of emerging digital technologies is positively associated with a firm’s inventory turnover.

2.3. Emerging Digital Technologies and Environmental Performance

Sustainability has moved to the forefront of managerial decision making as firms confront growing concerns about environmental impact and increased scrutiny from stakeholders who place a high value on environmental responsibility, including customers, employees, supply chain partners, and investors [81,82,83]. Addressing environmental obligations requires firms to make deliberate strategic choices across a range of operational activities, with the goal of reducing waste, improving energy efficiency, and minimizing the inefficient use of resources that contribute to higher emission levels [84]. In this setting, emerging digital technologies provide critical capabilities for sustainable operations by allowing firms to track, control, and reduce emissions through improved information and analytical support [85,86] more effectively.

A substantial body of OSCM research has investigated how environmental initiative announcements influence firm value, yet the empirical evidence remains inconclusive [42,81,87,88,89,90]. Early work by Klassen and McLaughlin [81] documents a positive stock market response of approximately 0.63 percent following announcements of environmental awards, suggesting that certain forms of environmental recognition are valued by investors. Subsequent studies, however, report more nuanced and sometimes contradictory effects. Gilley et al. [87] show that announcements related to environmentally friendly process improvements generate a negative average market reaction of −0.45 percent, while environmentally oriented product announcements fail to elicit a statistically significant response. In contrast, Melnyk et al. [88] find that the adoption of formal environmental management systems is associated with improvements in multiple dimensions of operational performance.

More recent evidence further highlights the context-dependent nature of market reactions. Jacobs et al. [82] observe that philanthropic contributions to environmental causes are met with positive abnormal returns, whereas voluntary emission reduction initiatives are associated with negative market responses, though efforts explicitly targeting greenhouse gas emissions tend to be viewed more favorably than reductions aimed at other pollutants. Complementing these findings, Arora et al. [90] report that investor reactions to the appointment of corporate sustainability executives are not significantly different from zero on average but vary meaningfully across firms and industries. Taken together, this literature suggests that the market’s valuation of environmental initiatives depends not only on the type of action undertaken but also on how such actions are perceived in terms of strategic intent and expected economic impact.

Recent theoretical and empirical work suggests that the performance implications of emerging digital technologies are contingent on organizational priorities, particularly the extent to which firms embed sustainability within supply chain policies. Seddigh et al. [91] argue that digital technologies exert stronger effects when sustainability objectives are explicitly incorporated into managerial decision making, positioning technology as an enabler rather than a standalone solution. Consistent with this view, emerging digital technologies support the monitoring, coordination, and enforcement of sustainable practices across organizational boundaries [92]. Their capacity for real-time data processing and system-level optimization allows firms to redesign business processes, improve energy efficiency, and reduce carbon emissions [93].

Empirical evidence across industries reinforces these mechanisms. Using survey data from 205 manufacturing firms, Jeble et al. [15] show that big data analytics is positively associated with reductions in greenhouse gas emissions. Similarly, machine learning applications have been found to lower operational costs, improve resource utilization, and facilitate the adoption of cleaner production technologies [94]. Cloud-based manufacturing systems further extend these benefits by enabling equipment sharing and scalable production capacity, which enhances operational efficiency while reducing environmental burdens [95]. In transportation and logistics contexts, cloud technologies support route optimization, real-time performance monitoring, and eco-driving initiatives, resulting in lower fuel consumption and reduced emissions [96]. Beyond firm-level applications, digital platforms also enable collaborative measurement and governance of emissions across supply chain partners, lowering the cost of environmental coordination [97]. More recently, AI-driven decision support systems have been shown to strengthen supply chain adaptability, promote circular economy practices, and support green servitization strategies by enhancing alertness and responsiveness to environmental risks [98,99].

At the same time, integrating sustainability objectives into supply chain decision making presents nontrivial challenges, particularly with respect to data availability, consistency, and comparability across organizational boundaries [100]. These challenges are compounded by growing concerns that emerging digital technologies themselves impose environmental costs. The rapid expansion of AI, machine learning, big data analytics, and cloud computing has been associated with substantial energy demand and rising carbon emissions, raising questions about their net contribution to sustainable development. Recent panel-based evidence suggests that the diffusion of emerging digital technologies may increase carbon emissions, thereby potentially exacerbating climate change pressures [101]. Corbett [102] similarly cautions that while data-intensive technologies can support sustainability-oriented decision making, their deployment may also generate unintended environmental consequences. Taken together, this literature suggests that investments in emerging digital technologies are likely to influence firms’ environmental performance, as reflected in greenhouse gas emissions, but offers no clear prediction regarding the direction of this effect. We therefore adopt an agnostic empirical stance and refer readers to Dieste et al. [20] for a comprehensive review of the documented negative environmental effects associated with digital technologies.

Emerging digital technologies affect firm-level GHG emissions through multiple, and often countervailing, pathways. On the one hand, technologies such as AI, machine learning, big data analytics, and cloud computing can lower emissions by enhancing operational efficiency and enabling more precise allocation of resources. Prior studies show that advanced analytics improve demand forecasting, production planning, and logistics coordination, which in turn reduce energy consumption and material waste across supply chain activities [27,56]. Predictive maintenance systems and real-time monitoring further contribute by minimizing unnecessary equipment usage and improving energy efficiency. Cloud computing may also reduce the carbon intensity of data processing by shifting workloads to professionally managed data centers that benefit from economies of scale, energy-efficient cooling systems, and increasing reliance on renewable energy sources [103,104]. On the other hand, these benefits are not guaranteed. When cloud data centers rely on carbon-intensive electricity grids, digitalization may simply reallocate emissions rather than eliminate them [104]. Given the coexistence of efficiency-enhancing and energy-intensive mechanisms, the overall environmental impact of emerging digital technology adoption remains an empirical question. Our results indicate that, for the firms in our sample, the efficiency gains associated with digital technologies outweigh their energy demands, leading to a net reduction in GHG emissions following adoption.

Recent work in environmental planning and management highlights the role of technological innovation as a critical driver of sustainability improvements. Using city-level data from China, Du et al. [105] examine ecological efficiency within low-carbon development initiatives and show that green technology innovation is associated with measurable gains in environmental performance. Their analysis illustrates how targeted investments in environmentally oriented technologies can lower resource intensity and emissions without constraining economic activity. These findings resonate with insights from operations and supply chain research, which emphasize that the sustainability benefits of technology adoption depend on how innovation is embedded within organizational and institutional contexts. By incorporating the perspective offered by Du et al. [105], we extend our theoretical framing beyond firm-level adoption to situate the environmental consequences of emerging digital technologies within a broader landscape of green innovation and low-carbon development.

Viewed in aggregate, recent scholarship suggests that the environmental footprint of emerging digital technologies is shaped less by their absolute energy requirements than by how they are embedded within operational and supply chain systems. Capabilities such as advanced analytics, real-time monitoring, and cross-functional optimization allow firms to curb material waste, improve energy efficiency, and exercise finer control over carbon-intensive processes throughout the supply chain. Concerns regarding the energy intensity of digital infrastructure remain warranted, particularly in contexts dependent on carbon-heavy electricity sources. Nevertheless, a growing body of evidence indicates that when sustainability objectives are integrated into managerial decision making, efficiency-driven gains frequently outweigh these additional energy costs. On this basis, we advance the expectation that investments in emerging digital technologies are associated with net improvements in firms’ environmental performance.

H3.

Adoption of emerging digital technologies is associated with improvements in a firm’s environmental performance, as reflected in reductions in greenhouse gas emissions.

3. Methodology

This section describes event study methods employed to select the sample and control firms for empirical analysis. It also details the methodology used to calculate the performance metrics and the criteria for choosing appropriate time periods during which these performance metrics are measured.

3.1. Sample Selection Procedure

Following the approach developed by Hendricks and Singhal [106], we identified announcements of emerging digital technology investments by U.S. publicly traded firms using the Wall Street Journal (WSJ) and Dow Jones News (DJN) as our primary data sources. We conducted independent searches of both outlets using a set of technology-related keywords, including “big data,” “data analytics,” “machine learning,” “artificial intelligence,” and “cloud.” To minimize the risk of omitted observations, these searches were supplemented by pairing the keywords with the names of publicly listed U.S. firms.

Given the rapid pace of technological change in digital technologies, we limited our search to a ten-year window in order to capture relatively recent adoption decisions. We further restricted the sample to announcements made no later than 2019 to ensure the availability of at least two years of post-announcement data, which is essential for assessing longer-term financial and operational outcomes. As a result, the final observation window for examining post-adoption performance spans the period from 2009 to 2019. To be included in the final sample, each announcement was required to satisfy the following criteria: (a) The announcement must provide information about a company either utilizing services provided by emerging digital technology providers or launching an in-house emerging digital technology initiative. For instance, “Harnessing in-memory technology from SAS, XL Group plc’s global insurance and reinsurance operations will dig deep into enhanced data analysis with SAS Visual Analytics (version not disclosed by the vendor). XL Group will use SAS Visual Analytics to help quickly visualize business data from internal and external sources to explore new ways of analyzing risk.” This is an example of a company (“XL Group”; NYSE: XL) availing services from an emerging digital technology provider. (b) Since the focus of this paper is on understanding how adopting emerging digital technologies impacts firm performance, announcements related to emerging digital technology providers themselves were excluded from our sample. (c) In cases where multiple announcements concerning emerging digital technology adoption were made by the same company, only the earliest dated announcement was included in our sample.

This process yielded 354,876 announcements by firms spanning the years 2009 to 2019. Each announcement was carefully reviewed to confirm its relevance to emerging digital technology adoption, which narrowed the pool to 519 announcements. Furthermore, to gather financial information on these companies, we used the Compustat database, which only contains financial data for publicly listed companies. Consequently, our final sample consists of 134 announcements by companies that had made emerging digital technology adoption announcements and both sample and control firms had available financial information in Compustat for the years 2009 to 2019.

Environmental performance was measured using greenhouse gas (GHG) emissions disclosed in firms’ sustainability and ESG reports for both sample and control firms. Because emissions reporting remains voluntary in the United States, the availability of such data is uneven across firms and inconsistent over time. Of the 134 emerging digital technology announcements in the full sample, only 87 could be paired with control firms that reported emissions in both the event year and the following year. Extending the analysis to require emissions data over multiple post-event years would lead to a sharp reduction in usable observations, severely limiting statistical power and compromising the reliability of the results.

Beyond data availability, the absence of standardized emissions reporting poses additional challenges for longitudinal analysis. Firms vary widely in reporting units, scope definitions, and measurement methodologies, and these practices often change across reporting periods. Differences in the treatment of Scope 1, Scope 2, and Scope 3 emissions are particularly pronounced and can hinder meaningful comparisons over time. Expanding the emissions analysis to longer post-adoption windows would therefore introduce substantial comparability concerns that could obscure the effects of emerging digital technology adoption. To balance data coverage, consistency, and interpretability, we focus on changes in total GHG emissions from the event year to one year after adoption. Where necessary, Scope 1, Scope 2, and Scope 3 emissions were aggregated, and all values were converted to million metric tons (MMT) to ensure uniformity. We view this shorter post-event window as a conservative test of environmental impact, as it prioritizes data integrity and comparability over a longer but noisier time horizon.

3.2. Choosing the Period over Which to Measure Performance Effects

Implementing emerging digital technologies typically involves two phases: (a) a contract vendor phase, during which a firm finalizes a contract agreement with a provider, and the emerging digital technology is customized, developed, and installed, with data merged from separate systems and (b) a complete operational use phase, which includes user training and system adaption and adjustment [53]. The entire process is estimated to take between 6 and 18 months, with an average duration of about one year [107]. Therefore, long-term performance effects are measured by analyzing changes in return on assets (ROA), return on equity (ROE), Tobin’s Q, and inventory turnover over a three-year period (one year from the announcement date for the implementation phase, and two years afterward as the post-implementation period). Due to limited data availability, as emissions data is not easily available for most public companies, the long-term effects for environmental performance will be measured using a two-year period, comparing emissions in the event year with emissions one year after the event.

3.3. Propensity Score Matching

We selected control firms using propensity score matching, following the methodological guidance of Ho et al. [108]. This approach is well established in the OSCM literature and has been widely applied to construct comparable control samples in empirical studies [109,110,111]. Although prior research has employed a range of matching strategies, including one-to-many matches and alternative algorithms such as radius or caliper matching, OSCM studies most commonly rely on one-to-one matching using a nearest-neighbor procedure [109,110,111].

Each firm that adopts a digital technology is matched to a single non-adopting firm using nearest-neighbor propensity score matching. The set of potential controls is limited in advance to firms operating in the same two-digit SIC industry and fiscal year following Corbett et al. [112]. This restriction reflects the view that industry conditions and timing account for a large share of baseline differences across firms. Observations with missing data on the matching variables, as well as those without a suitable industry-year counterpart, are excluded before matching.

The propensity score is estimated using firm size, market equity, and the book-to-market ratio. These variables describe differences in scale and market position that are likely to shape both adoption behavior and subsequent outcomes. Matching is implemented without an explicit caliper. This design choice is further supported by Austin (2010) [113], who shows through Monte Carlo simulations that matching one or two control firms per treated firm using nearest-neighbor methods minimizes mean squared error.

Post-matching analysis shows that sample and matched control firms have comparable means and standard mean differences within tolerable limits for firm size, market equity, and the book-to-market ratio. The standard mean difference for firm size, market equity, and book-to-market ratio are −0.0993, −0.0086, and 0.1178, respectively, suggesting that the matching approach yielded a reasonable comparison group for the analysis that follows.

3.4. Methodology for Estimating the Long-Term Performance Effects

To calculate long-term performance effects (including ROA, ROE, Tobin’s Q, inventory turnover, and GHG emissions), we first calculate the difference between the actual performance (financial, operational, and environmental) of the sample firm and its expected performance, referred to as abnormal performance [114]. The expected performance of the sample firm is determined by the actual performance of the matched control firm. Specifically, the expected performance of firm i in period t + 1 is calculated as:

E[DT Adopter_i,t+1] = DT Adopter_i,t + (Non Adopter_i,t+1 − Non Adopter_i,t)

(1)

where DT Adopter is the performance of the sample firm that has invested in emerging digital technologies and Non Adopter is the performance of its matched control firm, one that has not invested in emerging digital technologies. The abnormal performance is then calculated as

AP_i,t+1 = DT Adopter_i,t+1 − E[DT Adopter_i,t+1]

(2)

which is simply the performance difference between emerging digital technology adopters and non-adopters in period t + 1.

For single-year analyses, abnormal performance is measured directly using Equation (2). For multi-year post-implementation windows, abnormal performance is constructed as a cumulative change rather than an average of annual abnormal values. In particular, for performance evaluated over Years 1–3, we calculate abnormal performance as the difference between abnormal performance in Year 3 and abnormal performance in Year 1. This cumulative measure reflects the net long-term impact of emerging digital technology adoption over the post-implementation period and avoids masking persistent effects through annual averaging.

This approach is consistent with prior event-study research that emphasizes long-horizon performance changes following major organizational investments. Accordingly, all reported multi-year abnormal performance measures represent cumulative performance differentials between adopters and non-adopters over the specified post-implementation horizon.

To mitigate the influence of extreme observations, we trimmed the data at the 2.5 percent level in both tails, consistent with established event study practices in OSCM research [106,114,115]. Prior methodological studies caution against exclusive reliance on parametric t-tests in event study settings, noting their sensitivity to distributional assumptions that are often violated in firm-level performance data [112,116,117,118]. In light of these concerns, we emphasize non-parametric statistical tests when evaluating abnormal performance. Specifically, we employ the Wilcoxon signed-rank test, which is appropriate when the underlying distribution is approximately symmetric, and the binomial sign test, which is more robust to skewed outcomes [119]. To facilitate comparison with prior work and provide a complete statistical picture, we also report results from conventional parametric t-tests alongside the non-parametric p-values.

Our hypotheses will evaluate whether abnormal changes in return on assets (ROA), return on equity (ROE), Tobin’s Q, inventory turnover, and greenhouse gas (GHG) emissions deviate significantly from zero, with detailed results reported in the following section. ROA and ROE capture complementary aspects of firm profitability and are widely used in operations and information systems research as broad indicators of financial performance [46,120,121,122]. Tobin’s Q adds a market-based perspective and has been frequently employed to assess the value implications of information systems investments [123] and operational capabilities [66]. Together, these measures provide a richer depiction of financial outcomes than any single indicator alone.

4. Empirical Results

This section begins with descriptive statistics that highlight the timing of emerging digital technology announcements, industry classifications, and variations in mean returns. The main results for long-term financial, operational, and environmental performance are then presented.

4.1. Data Description

Table 1. Emerging digital technology adoption announcements over time.

Event Year	No. of Obs.
2009	5
2010	8
2011	7
2012	7
2013	8
2014	16
2015	9
2016	21
2017	16
2018	17
2019	20
Total	134

shows that the adoption of emerging digital technologies has become more frequent in recent years, with about 55% of them occurring in 2016 or later.

We also observe significant interest in emerging digital technologies across a wide range of industries in

Table 2. Emerging digital technology adoption announcements by industry.

Segment	Segment Description	No. of Obs.
73	Business Services	21
35	Industrial Machinery & Equipment	13
36	Electronic & Other Electric Equipment	9
48	Communications	9
63	Insurance Carriers	8
38	Instruments & Related Products	7
13	Oil and Gas Extraction	6
28	Chemical & Allied Products	6
49	Electric, Gas, & Sanitary Services	6
20	Food & Kindred Products	4
37	Transportation Equipment	4
50	Wholesale Trade—Durable Goods	4
53	General Merchandise Stores	4
45	Transportation by Air	3
51	Wholesale Trade-non-durable Goods	3
10	Metal, Mining	2
26	Paper & Allied Products	2
29	Petroleum & Coal Products	2
60	Depository Institutions	2
64	Insurance Agents, Brokers, & Service	2
16	Heavy Construction Other Than Building Construction Contractors	1
21	Tobacco Products	1
27	Printing & Publishing	1
33	Primary Metal Industries	1
34	Fabricated Metal Products	1
44	Water Transportation	1
55	Automative Dealers & Service Stations	1
56	Apparel & Accessory Stores	1
57	Furniture & Home furnishings Stores	1
59	Miscellaneous Retail	1
61	No depository Institutions	1
65	Real Estate	1
75	Auto Repair, Services, & Parking	1
78	Motion Pictures	1
82	Educational Services	1
87	Engineering & Management Services	1
99	No classifiable Establishments	1
	Grand Total	134

. A substantial portion of the sample firm announcements came from the business services industry (16%), followed by industrial machinery (10%), Communications (7%), and electronic and equipment-based industries (7%).

Interestingly,

Table 3. Performance of sample and control firms per year.

Sample Firms	Mean	Median	S.D
ROA	0.0338	−0.0017	0.89484
ROE	−0.1754	0	2.05058
Tobin’s Q	0.2437	0	2.84721
Inventory Turnover	−0.0172	0	0.07834
GHG Emission reduction *	−690,531	−7854	3,226,296
Control Firms	Mean	Median	S.D
ROA	−0.0634	0	1.50328
ROE	0.5269	0	3.76615
Tobin’s Q	−0.0279	0	0.188
Inventory Turnover	−0.0055	0	0.11097
GHG emission reduction *	−67,259	−312	749,155

n = 134 (* n = 87).

shows that adopters of emerging digital technologies (sample firms) exhibit higher mean ROA and Tobin’s Q but lower mean ROE and inventory turnover per year than control firms. In addition, emerging digital technology adopters experienced a greater reduction in GHG emissions on average per year than control firms.

Table 4. Abnormal financial and operational performance in periods: Descriptive Statistics (mean and standard deviation).

	Implementation Period (0–1)		Post-Implementation Period (1–3)		Overall (0–3)
Abnormal Performance	Mean	S.D	Mean	S.D	Mean	S.D
ROA	−0.009	0.061	0.031	0.105	0.012	0.104
ROE	0.035	0.393	0.046	0.340	0.080	0.502
Tobin’s Q	0.096	0.897	0.076	0.861	0.090	1.097
Inventory Turnover	−0.359	4.602	−1.412	15.412	−1.514	14.135

n = 134.

shows that sample firms have higher financial performance on average (ROA, ROE, and Tobin’s Q) and lower operational performance (inventory turnover) compared to control firms) in implementation period, post-implementation period, and all periods. It is noteworthy that the mean abnormal returns for most long-term financial performance measures are higher in the post-implementation period when emerging digital technologies are operational and fully integrated into the business, compared to the implementation period, when firms have yet to fully implement emerging digital technologies. However, in terms of operational performance, represented by inventory turnover, the results in Table 4 show a decline in average inventory turnovers for both the post-implementation and the combined periods, compared to the implementation period.

4.2. Long-Term Performance Effects of Investments in Emerging Digital Technologies

Event study research has long cautioned against uncritical reliance on parametric t-tests, particularly when the underlying data violate normality assumptions [10,112,116]. As a result, nonparametric alternatives are frequently recommended. The Wilcoxon signed-rank test is commonly used because of its relatively strong statistical power, but its applicability depends on the presence of symmetric distributions. When the distribution of outcomes is skewed, the binomial sign test offers a more robust inferential approach [119]. In this study, both Shapiro–Wilk and Kolmogorov–Smirnov tests indicate that abnormal ROA, ROE, Tobin’s Q, and inventory turnover deviate from normality across all three event windows. This conclusion is reinforced by visual inspection of boxplots, which reveal substantial asymmetry in most cases. Given these distributional properties, we therefore place greater emphasis on inferences drawn from the binomial sign test.

Overall, our results in

Table 5. Abnormal financial and operational performance in periods: Parametric and non-parametric test results.

	Implementation Period Year 0–1			Post-Implementation Period Years 1–3			Combined Periods Years 0–3
Abnormal Performance	Mean t-Statistic	Median Wilcoxon Signed-Rank Test	Percent Positive Binomial Sign Test	Mean t-Statistic	Median Wilcoxon Signed-Rank Test	Percent Positive Binomial Sign Test	Mean t-Statistic	Median Wilcoxon Signed-Rank Test	Percent Positive Binomial Sign Test
ROA	−1.644	0.00 **	0.53	3.40 ***	0.00 ***	0.74 ***	1.286	0.00	0.63 ***
Tobin’s Q	1.24	0.00	0.53	1.018	0.00	0.63 ***	0.949	0.00	0.55
ROE	1.046	0.00	0.62 ***	1.552	0.00	0.67 ***	1.843 *	0.00	0.67 **
Inventory turnover	−0.903	0.00	0.70 ***	−1.06	0.00	0.70 ***	−1.240	0.00	0.69 ***

n = 134, Period: 2009–2019. For t-statistics and Wilcoxon signed-rank test: *** p ≤ 0.01, ** p ≤ 0.05, * p ≤ 0.1 (two-tailed); For binomial sign test: *** Significantly different from zero (50% in the case of percent positive) at the 1% level for a one-tailed test; ** Significantly different from zero (50% in the case of percent positive) at the 5% level for a one-tailed test; * Significantly different from zero (50% in the case of percent positive) at the 10% level for a one-tailed test.

indicate no significant difference in performance between sample and control firms during the implementation period of emerging digital technologies, with a few exceptions. The t-statistic for all performance measures, the Wilcoxon signed-rank test for Tobin’s Q, ROE, and inventory turnover, and the binomial sign test for ROA and Tobin’s Q all show no significant difference in performance between sample and control firms during the implementation phase.

However, in the post-implementation period, sample firms significantly outperform control firms in all long-term financial and operational performance metrics, based on the binomial sign test. For instance, the mean abnormal ROA during the post-implementation period is 3.4 (p ≤ 0.01), with the median abnormal ROA also significantly different from zero (p ≤ 0.01). Furthermore, nearly 74% of sample firms outperformed their propensity score matched control firms, a proportion significantly greater than 50%. Similar findings are observed in Table 5 for the combined period, with the binomial sign test showing significant results for all metrics except Tobin’s Q, which does not exhibit statistically significant differences.

Regarding firm market value, Tobin’s Q, the mean abnormal return during the post-implementation period is 1.018, which is not significantly different from zero (p ≥ 0.1). Similarly, the median abnormal Tobin’s Q is also not significantly different from zero (p ≥ 0.1). However, approximately 63% of the sample firms perform better on Tobin’s Q than the control firms, significantly greater than 50%. For the combined period, there is no significant difference in Tobin’s Q between sample and control firms.

In terms of ROE, the mean abnormal ROE is 1.552, which is not significantly different from zero (p ≥ 0.1) during the post-implementation period. The median abnormal ROE is also not significantly different from zero (p ≥ 0.1). However, about 66% of the sample firms outperform control firms on ROE, significantly exceeding the 50% threshold. For the combined period, both the parametric t-test and non-parametric binomial sign test indicate a significant difference between sample and control firms. The mean abnormal ROE is 1.843 (p ≤ 0.1), with nearly 67% of sample firms outperforming control firms on ROE, significantly different from 50%.

The ROE findings require a nuanced reading. Although neither the mean nor the median abnormal ROE reaches statistical significance in the post-implementation period, the binomial sign test reveals that roughly two-thirds of adopting firms exhibit positive abnormal ROE. This pattern underscores the sensitivity of ROE to firm-specific equity movements. In line with prior event-study research that highlights the limitations of parametric inference under skewed or heavy-tailed outcomes [116,119], we therefore emphasize evidence from non-parametric tests. The prevalence of positive abnormal ROE observations points to a broadly shared improvement in shareholder returns following emerging digital technology adoption, even though the magnitude of these gains differs substantially across firms.

Regarding inventory turnover, we find that nearly 70% of the sample firms outperform control firms, a proportion significantly different from 50%, in the post-implementation period. However, the mean abnormal inventory turnover is −1.06, which is not significantly different from zero (p ≥ 0.1), and the median abnormal inventory turnover also shows no significant difference from zero (p ≥ 0.1). Similar findings are observed for inventory turnover in the combined period across all three tests.

The inventory turnover findings also require a more nuanced reading. Despite the absence of statistically significant mean or median abnormal turnover, the binomial sign test shows that close to 70 percent of firms adopting emerging digital technologies exhibit improvements relative to their matched controls in the post-implementation period. This apparent inconsistency reflects the pronounced heterogeneity of inventory structures and operating conditions across firms. Consequently, gains in inventory efficiency are common but uneven, producing a wide dispersion of outcomes that attenuates average-based statistical tests. In this setting, the binomial sign test provides a more informative signal by capturing the systematic direction of change across firms, even when the magnitude of those changes differs substantially.

Overall, our results in Table 5 suggest that firms investing in emerging digital technologies achieve significantly better financial and operational performance compared to benchmark firms once these emerging digital technologies are fully operational and integrated into their business processes. Emerging digital technology adopters exhibit significantly higher ROA, ROE, Tobin’s Q, and inventory turnover in the post-implementation period than benchmark firms, supporting Hypotheses H1a–H1c and H2.

Table 6. Abnormal environmental performance in periods.

	Implementation Period Year 0–1
	Mean t-Statistic	Median Wilcoxon Signed-Rank Test	Percent Positive Binomial Sign Test
Abnormal GHG emissions	−1.727 *	−8636 **	0.61 **

n = 87, Period: 2009–2021; For t-statistics and Wilcoxon signed-rank test: ** p ≤ 0.05, * p ≤ 0.1 (two-tailed); For binomial sign test: ** Significantly different from zero (50% in the case of percent positive) at the 5% level for a one-tailed test; * Significantly different from zero (50% in the case of percent positive) at the 10% level for a one-tailed test.

shows that the mean abnormal GHG emissions in the year following emerging digital technology adoption is −1.727 (p ≤ 0.1), indicating that, on average, emerging digital technology adopters have lower GHG emissions compared to control firms. The median abnormal GHG emissions of −8636 is also significantly different from zero (p ≤ 0.05). Furthermore, nearly 61% of sample firms outperform their propensity score matched control firms in reducing GHG emissions, a proportion significantly greater than 50%, supporting H3.

5. Within-Industry Analysis

In addition to our main analysis and results, we further classify our sample companies into their respective industry sectors based on their four digits SIC code [124]. Of the 134 emerging digital technology adoption announcements made by firms from 2009 to 2019, a vast majority of them were from the manufacturing sector (n = 48) and service sector (n = 53), as shown in

Table 7. Industry breakdown of emerging digital technology announcements.

Industry Classification	No. of Obs.
Construction	1
Manufacturing	48
Oil and Mining	10
Other	2
Retail	15
Services	53
Transportation	5
Total	134

. Approximately 36% of the announcement pertain to the manufacturing sector, while 40% are from the services sector. Due to limited sample sizes in the other industries, our within-industry analysis will focus on the manufacturing and services sectors.

Focusing on the manufacturing and service sectors is important, as emerging digital technologies offer valuable insights for both service and manufacturing companies, though their focus and applications may differ [56]. Manufacturing companies often rely on data related to production, supply chain management, inventory, and quality control. Emerging digital technologies assist these manufacturing companies in optimizing processes, enhancing efficiency, reducing waste, and improving product quality through data-driven decision-making. On the other hand, service companies utilize emerging digital technologies to understand customer behavior, preferences, and satisfaction levels to enhance customer experience, personalize offerings, and identify growth opportunities.

Additionally, emerging digital technologies help service companies optimize resource allocation, manage service delivery, and track key performance indicators. Elbashir et al. [125] examine the relationship between emerging digital technologies and organizational performance and find significant differences in the relationship between service and non-service sectors. A systematic literature review of emerging digital technology adoption, utilization, and success by Ain et al. [126] highlights that most studies focus on various service sectors, such as government services, insurance, banking, and professional services.

When comparing the mean abnormal performance metrics between manufacturing and service sectors, it is evident that manufacturing firms generally exhibit better financial and operational performance during the emerging digital technology implementation period. However, during the post-implementation period, manufacturing firms experience greater financial benefits in terms of ROA and ROE, while service firms see more significant gains in market value (Tobin’s Q), as shown in

Table 8. Abnormal financial and operational performance of manufacturing firms.

Manufacturing
	Implementation Period (0–1)			Post-Implementation Period (1–3)			Overall (0–3)
	Mean t-Statistic	Median Wilcoxon Signed-Rank Test	Percent Positive Binomial Sign Test	Mean t-Statistic	Median Wilcoxon Signed-Rank Test	Percent Positive Binomial Sign Test	Mean t-Statistic	Median Wilcoxon Signed-Rank test	Percent Positive Binomial Sign Test
ROA	−0.02	0.00	0.50	0.05 **	0.00 **	0.71 ***	0.02	0.00	0.60
ROE	0.09	0.00	0.58	0.12 *	0.00	0.58	0.18 *	0.00	0.56
Tobin’s Q	0.53 ***	0.00 **	0.63	−0.07	0.00	0.60	0.38 **	0.00	0.58
Inventory Turnover	−0.04	0.00	0.65 *	−2.65	0.00	0.50	−2.45	0.00	0.56

Manufacturing: n = 48, Period: 2009–2019; For t-statistics and Wilcoxon signed-rank test: *** p ≤ 0.01, ** p ≤ 0.05, * p ≤ 0.1 (two-tailed); For binomial sign test: *** Significantly different from zero (50% in the case of percent positive) at the 1% level for a one-tailed test; ** Significantly different from zero (50% in the case of percent positive) at the 5% level for a one-tailed test; * Significantly different from zero (50% in the case of percent positive) at the 10% level for a one-tailed test.

and

Table 9. Abnormal financial and operational performance of service firms.

Services
	Implementation Period (0–1)			Post-Implementation Period (1–3)			Overall (0–3)
	Mean t-Statistic	Median Wilcoxon Signed-Rank Test	Percent Positive Binomial Sign Test	Mean t-Statistic	Median Wilcoxon Signed-Rank test	Percent Positive Binomial Sign Test	Mean t-Statistic	Median Wilcoxon Signed-Rank Test	Percent Positive Binomial Sign Test
ROA	−0.01	0.00	0.51	0.02 **	0.00 **	0.77 ***	0.01	0.00	0.60
ROE	−0.04	0.00	0.60	−0.01	0.00	0.70 ***	−0.02	0.00	0.62 *
Tobin’s Q	−0.14 *	0.00 **	0.51	0.17	0.00	0.70 ***	−0.10	0.00	0.55
Inventory Turnover	−0.94	0.00	0.79 ***	0.38	0.00	0.87 ***	−0.27	0.00	0.85 ***

Services: n = 53, Period: 2009–2019; For t-statistics and Wilcoxon signed-rank test: *** p ≤ 0.01, ** p ≤ 0.05, * p ≤ 0.1 (two-tailed); For binomial sign test: *** Significantly different from zero (50% in the case of percent positive) at the 1% level for a one-tailed test; ** Significantly different from zero (50% in the case of percent positive) at the 5% level for a one-tailed test; * Significantly different from zero (50% in the case of percent positive) at the 10% level for a one-tailed test.

. Regarding the statistical significance of these within industry analyses, the binomial sign test reveals that service firms achieve higher financial benefits, with at least 70% of sample firms in service sectors outperforming their benchmark firms against ROA, ROE, and Tobin’s Q. In contrast, 71% of manufacturing firms outperform their benchmark firms in ROA, but there is no statistically significant difference between sample and control firms in the manufacturing sector for ROE, Tobin’s Q, and inventory turnover during the post-implementation period.

In service environments, inventory turnover does not primarily reflect the movement of physical goods. Instead, it captures how efficiently firms manage work-in-process services, transactional queues, and embedded capacity buffers. From this perspective, changes in turnover signal shifts in flow efficiency, including shorter cycle times, more effective use of available capacity, and fewer processing delays. Digital technologies shape these outcomes by improving the visibility of demand and enabling tighter coordination across service activities, which allows firms to adjust operations more quickly and reduce operational slack [127,128,129,130].

Interestingly, no significant differences are found in abnormal environmental performance regarding GHG emissions in both manufacturing and service sectors for all periods. This outcome may be attributed to the small sample size (n = 30 for manufacturing and n = 33 for services).

6. Discussion, Limitations & Conclusions

This study advances understanding of the performance implications of emerging digital technologies by jointly examining their financial, operational, and environmental effects at the firm level. In contemporary business settings, operations managers face the complex task of assembling and governing information technology portfolios that are not only technically coherent but also aligned with strategic objectives [131]. Although prior OSCM research has generated substantial insight into the performance consequences of established enterprise systems such as ERP, SCM, CRM, and SRM, the effects of newer, data-intensive digital technologies remain comparatively underexplored. By focusing on investments in artificial intelligence, machine learning, big data analytics, and cloud computing, this study contributes early empirical evidence on how emerging digital technologies shape firm performance.

Our findings indicate that performance gains materialize most clearly in the post-implementation period once these technologies have been fully embedded within organizational processes. Specifically, we observe significant improvements in return on assets, return on equity, Tobin’s Q, and inventory turnover, suggesting that the benefits of emerging digital technologies accrue over time rather than immediately following adoption. This pattern is consistent with arguments that such technologies require sustained investments in infrastructure, skills, and organizational adaptation before yielding measurable returns [7]. When performance is assessed over the combined event window, abnormal improvements in ROA, ROE, and inventory turnover remain evident. Results from the binomial sign test further reinforce prior findings in the enterprise systems literature, showing that, like ERP implementations, investments in emerging digital technologies are associated with meaningful gains in profitability, particularly as reflected in ROA [10].

More broadly, these results align with earlier evidence linking information technology investments to improvements in operational outcomes such as inventory efficiency, delivery reliability, and process responsiveness [12,67,68], as well as to enhanced financial performance [8,9]. Taken together, the findings suggest that emerging digital technologies extend the value-creation mechanisms observed in traditional enterprise systems, while operating through more data-intensive and analytically sophisticated pathways.

This study also advances understanding of how emerging digital technologies shape firms’ environmental performance, an area where prior evidence remains inconclusive. Existing research points to competing effects. On the one hand, digital technologies can support more sustainable operations by enabling closer monitoring, measurement, and management of carbon emissions [85]. On the other hand, the deployment and use of technologies such as artificial intelligence, machine learning, big data analytics, and cloud computing are themselves energy intensive and may contribute to higher carbon emissions [101]. Corbett [102] similarly cautions that while information systems can enhance sustainability-oriented decision making, they may also generate unintended environmental costs.

Against this backdrop, our empirical results indicate a net improvement in environmental performance following the implementation of emerging digital technologies. Approximately 61 percent of firms in our sample exhibit meaningful reductions in greenhouse gas emissions relative to matched benchmark firms. These findings challenge the prevailing narrative that advanced information technologies are inherently detrimental to environmental outcomes. Instead, they suggest that when effectively integrated into organizational and supply chain processes, emerging digital technologies can contribute to sustained reductions in firm-level greenhouse gas emissions over the longer term.

The findings further reveal meaningful industry-level heterogeneity in the performance effects of emerging digital technologies. When comparing manufacturing and service firms, the financial benefits appear substantially stronger in the service sector. More than 70 percent of service firms in the sample outperform their matched benchmarks on return on assets, return on equity, and Tobin’s Q following technology adoption. Manufacturing firms exhibit a more limited pattern of gains. While 71 percent outperform benchmark firms on ROA, no statistically significant differences emerge for ROE, Tobin’s Q, or inventory turnover during the post-implementation period. This divergence is consistent with the nature of many service industries, including financial services, utilities, e-commerce, and data-intensive platform businesses, where emerging digital technologies are tightly coupled with core value-creation activities such as real-time analytics, forecasting, and automated decision support [132].

Differences in inventory outcomes further underscore how industry structure shapes the translation of digital investments into operational performance. Service firms typically carry minimal physical inventory and rely heavily on information processing, scheduling, and demand management to coordinate resource flows. In such contexts, improvements in data visibility and analytical capability can be rapidly converted into higher inventory turnover, aligning with evidence that information-technology-enabled integration enhances responsiveness in information-intensive operations [133]. Manufacturing firms, in contrast, operate with layered inventories that include raw materials, work-in-process, and finished goods, all embedded within production systems characterized by setup times, batch constraints, and supplier lead-time dependencies. Prior research shows that inventory turnover in manufacturing is strongly shaped by structural factors such as capital intensity, product complexity, and process design, which can dampen the marginal impact of improved information alone [134]. Horizontal supply chain complexity, including supplier breadth, product variety, and customer dispersion, further constrains the ability to convert digital capabilities into immediate turnover gains [135]. Together, these structural rigidities help explain why service firms realize more direct operational benefits, while manufacturing firms may require deeper process reconfiguration or supply chain redesign before comparable improvements emerge.

Seen through a Resource-Based perspective, the findings suggest that emerging digital technologies acquire strategic value only after they are absorbed into the fabric of organizational work. The clearer performance effects that emerge after the implementation period has passed echo a long-standing RBV insight. Advantage is not rooted in ownership of resources, but in how firms learn to deploy and recombine them over time in the form of investments in people, data systems, and the reworking of routines that govern daily operations.

A Dynamic Capabilities lens also helps explain how this deployment unfolds. Improvements in profitability, inventory turnover, and environmental performance reflect firms’ ability to identify where digital tools can make a difference, commit to using them in targeted ways, and adjust operational and supply chain practices accordingly. Higher inventory turnover signals tighter coordination and faster response cycles. Lower greenhouse gas emissions point to more disciplined oversight of energy use and material flows. These outcomes do not resemble sudden technology shocks. They are more consistent with gradual experimentation, learning, and adjustment.

The environmental findings also contribute to ongoing debates in the Triple Bottom Line literature. Evidence of net reductions in emissions challenges the view that advanced digital technologies inevitably introduce environmental trade-offs. Instead, the results suggest that when managers explicitly direct digital initiatives toward operational efficiency and control, economic and environmental objectives can move in the same direction. Improvements in transparency and decision quality appear central to this alignment.

Taken together, the results position Dynamic Capabilities as the bridge between resources and outcomes. Emerging digital technologies create potential, but potential alone does not generate value. What ultimately matters is whether firms can adapt their structures, routines, and governance practices so that digital transformation translates into durable financial, operational, and environmental gains.

Our findings show that adopting emerging digital technologies can deliver real benefits across financial, operational, and environmental dimensions. These improvements do not appear instantly; gains in profitability, inventory efficiency, and emissions reduction become most apparent once the technologies are fully embedded into daily operations. To capture these benefits, managers need to invest early in the right data infrastructure, skill development, and process alignment.

While the technologies we studied contribute to lower greenhouse gas emissions overall, we find no significant differences between manufacturing and service firms. This suggests that environmental benefits are not automatic and require intentional integration into business processes. Managers should actively embed emissions tracking, monitoring, and resource efficiency practices into operational routines to ensure that environmental gains are realized consistently alongside improvements in financial and operational performance.

Future research could examine newer generations of emerging digital technologies, including large language models such as ChatGPT (conceptual reference; no specific software version employed in this study), and assess their implications not only for financial and operational performance but also for environmental outcomes. While this study focuses on greenhouse gas emissions as an indicator of environmental impact, subsequent work could broaden the scope to include additional measures such as energy consumption and waste generation, offering a more comprehensive assessment of sustainability effects.

Studies that rely on public announcements necessarily observe the moment when firms formally signal technology investment, not the depth or intensity with which those technologies are ultimately deployed. In practice, firms differ widely in how fully emerging digital tools are woven into everyday operations, and some companies may adopt analytics, cloud infrastructure, or related technologies without issuing public disclosures. Such cases blur the distinction between adopting and non-adopting firms, but they do so in a way that adds noise rather than directional distortion. If control firms quietly deploy similar technologies, the contrast between groups is reduced, making detected effects smaller than the underlying impact. From this perspective, the estimated financial and environmental gains should be viewed as lower-bound estimates rather than overstated results.

At the same time, the research design reduces the likelihood that this issue drives the main findings. Propensity score matching yields a control group that mirrors adopting firms along observable financial and operational characteristics prior to the announcement. The lack of statistically meaningful differences in return on assets and Tobin’s Q at the event date suggests comparable pre-adoption trajectories. Although implementation intensity cannot be directly observed, the timing of the results is informative. Performance and environmental improvements emerge gradually after the announcement, consistent with a process of learning, adjustment, and organizational embedding rather than immediate transformation. This pattern aligns with how complex digital technologies are typically absorbed into planning, coordination, and control systems. Future research could sharpen measurement by combining announcement data with surveys, technology spending disclosures, or indicators of digital maturity. Within the constraints of archival data, however, the approach adopted here offers a cautious and transparent assessment of the economic and environmental implications of emerging digital technology adoption, while recognizing that unobserved adoption among control firms would tend to dampen, not exaggerate, the estimated effects.

One limitation of the analysis is related to the timing of the environmental data used in this study. Greenhouse gas emissions are observed only for the year immediately following the adoption, a constraint that reflects data availability, not the scope of the intended analysis. As such, the results primarily capture short-term environmental responses. Changes in emissions during this initial period may be driven by early efficiency gains, changes in reporting practices, or modest process improvements, whereas meaningful environmental benefits are more likely to occur once the digital technologies become fully embedded in routine operations and supply chain activities.

The narrow time horizon also means smaller insight into longer-term dynamics. Rarely, if ever, would the adoption of advanced digital technologies be instantaneous, and such events typically consist of long periods of learning, experimentation, and organizational adjustment. In this setting, early emissions reductions will not always persist, especially as firms expand digital use, increase computing capacity, or adapt operations in ways that are responsive to new technological demands. These factors mean environmental results need to be interpreted cautiously. Early post-adoption patterns, instead of durable outcomes, are being measured. Future work can resolve this by longitudinally extending the post-implementation period that is analyzed, tracking emissions over multiple years, or incorporating data on firm-level sustainability initiatives into modeling environmental impacts over time.

A further limitation of the study lies in how greenhouse gas emissions are measured. The analysis draws on data reported in firms’ sustainability and ESG disclosures, rather than on emissions figures that have been independently audited. Although these disclosures are now a common data source in sustainability research and have benefited from increasing standardization, they still reflect differences in reporting practices across firms. Emissions estimates may vary because of how firms define reporting boundaries, select estimation methods, or frame their disclosures, which introduces a degree of imprecision, and leaves open the possibility of strategic reporting.

That said, these measurement limitations are unlikely to bias the results in a systematic direction. Any reporting noise or inconsistency would tend to blur differences between adopting firms and their matched controls, making it more difficult to detect meaningful effects. In this sense, the estimated reductions in greenhouse gas emissions should be interpreted cautiously but also conservatively, as they likely understate rather than overstate the underlying environmental impact. Future research could strengthen confidence in these findings by pairing ESG disclosures with externally verified emissions data, regulatory filings, or alternative measures such as satellite-based estimates, allowing for a more refined assessment of both environmental performance and reporting practices.