The COVID-19 pandemic accelerated telemedicine adoption, offering a convenient alternative to in-person care. However, televisits may not fully address health concerns and sometimes require supplementary in-person visits, consuming resources that could have been saved if the initial visit had been in-person. As the pandemic subsides, in-person visits are regaining popularity, prompting providers to reorient resources toward in-person care. Transportation support (or subsidies) for patients, funded by providers or the government, plays a critical role in facilitating in-person visits. In this evolving landscape of telemedicine, we study how an outpatient care provider can optimally balance virtual and in-person services and whether, and how, to engage with transportation subsidies. We connect these two questions by examining how transportation subsidies reshape the provider’s optimal capacity allocation across service channels and, in turn, affect overall patient access. We develop a stylized queueing-game model to represent the operations of a revenue-maximizing provider serving patients who strategically choose between service channels. We find that provider size, measured by total capacity relative to demand, is key. Small and large providers perform best by focusing on one channel without offering subsidies, whereas medium-sized providers benefit from carefully balancing both channels alongside subsidies. Paradoxically, transportation subsidies, which make in-person care more accessible, may reduce overall patient access to care, even when fully funded by the government. This occurs because providers may shift capacity toward a higherreimbursement channel, ultimately serving fewer patients. Differentiating payment rates between in-person and virtual visits can potentially prevent such reductions. Our study highlights the importance of capacity coordination between channels for providers and cautions policymakers that transportation support may unintentionally harm patient access. Properly designed financial incentives can help prevent such negative outcomes.
Customer service has evolved beyond in-person visits and phone calls to include live chat, AI chatbots and social media, among other contact options. Service providers typically refer to these contact modalities as “channels.” Within each channel, customer service agents are tasked with managing and resolving a stream of inbound service requests. Each request involves milestones where the agent must decide whether to keep assisting the customer or to transfer them to a more skilled—and often costlier—provider. To understand how this request resolution process should be managed, we develop a model in which each channel is represented as a gatekeeper system and characterize the structure of the optimal request resolution policy. We then turn to the broader question of the firm’s customer service design, which includes the strategic problem of which channels to deploy, the tactical questions of at what level to staff the live-agent channel and to what extent to train an AI chatbot, and the operational question of how to control the live-agent channel. Examining the interplay between strategic, tactical, and operational decisions through numerical methods, we show, among other insights, that service quality can be improved, rather than diminished, by chatbot implementation.
Karca D Aral, Erasmo Giambona, Ricardo Lopez A
et al.
How does corporate board gender diversity affect supply chain risk management? Board gender diversity can influence supply chain risk management through behavioral and agency-based mechanisms. Female directors’ greater risk aversion, lower overconfidence in the face of uncertainty, and heightened accountability pressures may lead to stronger oversight and risk mitigation efforts in operations, where risks are quantifiable and failures are highly visible. Therefore, we should expect supply chain risk management to increase with increased women representation in corporate boards. We test this prediction leveraging on a reform requiring California firms to increase board gender diversity. We find that the propensity to use purchase obligations, supply contracts designed to mitigate the risk of future price fluctuations and reduce procurement uncertainty, increased for California buyers following the reform. We also find that California buyers increased their geographic supply chain diversification and became more likely to discontinue relationships with high-default risk suppliers after the reform. These changes are larger when female directors are better positioned to influence corporate decisions, contributing to validate board gender diversity as an important channel for operational changes. The takeaway for corporate leaders, policymakers, and shareholders is that board gender diversity can lead to a greater focus on supply chain risk management, which increases profitability.
Eristy Minda Utami, Gusni Gusni, Reva Yuliani
et al.
The growing impact of technology has attracted a greater number of people, especially Generation Z, to financial markets. Enhancing financial literacy and attitudes is essential for promoting informed investing choices. This research analyzes the impact of financial knowledge and social influence on Generation Z's investment aspirations, while accounting for the mediating functions of financial literacy and attitude. This research seeks to elucidate the mediating role of financial literacy and attitude in the interaction among financial knowledge, social influence, and investment intentions. A survey methodology was used with a sample size of 200 students enrolled in capital market courses at Widyatama University. The results demonstrate that financial knowledge and attitudes substantially affect investing intentions among Generation Z. Furthermore, financial acumen and social influence indirectly impact investing intentions via these mediating variables. These findings emphasize the need of thorough financial education to develop superior investing strategies among young individuals. Theoretical implications indicate that augmenting financial awareness and attitudes may significantly enhance investment decision-making among Generation Z.
Production management. Operations management, Management. Industrial management
Osama M. Tarabih, Mauricio E. Arias, Hung Q. Nguyen
et al.
Abstract Harmful algal blooms have large impacts on aquatic ecosystem and human health. Nutrient enrichment, in combination with warm water temperatures, high sunlight availability, and low water turbulence, have been proven to be major factors driving algal blooms. In this study, lake eutrophication processes, including phytoplankton production and nutrient cycling, were simulated and coupled with a reservoir operations model to optimize multi‐criteria lake operation goals. The main objective of this study was thus to design reservoir operations that would minimize phosphorus (P), nitrate‐nitrogen (NOx), and phytoplankton loads to downstream water bodies, while meeting other societal water resource demands in eutrophic lakes. We used an open‐source, multi‐objective evolutionary algorithm framework with four optimization objectives (minimizing P, NOx, and phytoplankton loads and water demand deficits), assessing each constituent separately and in combination. In addition, different optimization scenarios associated with each objective were investigated. To effectively demonstrate our findings, we implemented our approach in Lake Okeechobee, the largest subtropical lake in the US. We identified multiple opportunities to reduce downstream loads while minimizing impacts on water demand deficits. Notably, considering combined load objectives yielded substantial reductions in summertime P, NOx, and phytoplankton exports by up to 73%, 82%, and 73%, respectively, with minimal increases in water demand deficits. This supports the idea that alternative operational strategies could provide an effective and economical reservoir management strategy for balancing downstream water quality and societal water resource needs.
The main objective of this study is to present the practical application of the Theory of Constraints (TOC) and its Thinking Process Tools (TPT) to identify, analyze, and eliminate constraints in the production department of a machinery company specializing in the production of bearing cages. A case study was used as the research method. Interviews, direct observations, and performance indicator analysis were used to identify the most critical constraints in the production department of the studied company. The research included the application of five TOC-TPT tools: Goal Tree (GT), Current Reality Tree (CRT), Evaporating Cloud (EC), Future Reality Tree (FRT), and Prerequisite Tree (PRT). These tools were used to identify organizational goals, diagnose the root causes of key constraints, resolve internal conflicts, design future solutions, and outline the path for implementing solutions. The study focused on an in-depth analysis of the key constraint in the form of the lack of work instructions at each stage of production. The analysis revealed that the root causes of this limitation were insufficient investment in training and management development. Determining a dedicated budget and resources for a structured, ongoing training and development program that aligns with the company’s strategic goals and develops both technical and interpersonal skills at all levels of staff was identified as a critical solution to this problem. The advantages and limitations of using TOC-TPT were also analyzed in response to comments from the working group participating in the study. The results showed the effectiveness of TOC-TPT in solving complex operational problems in the study company and the technical and organizational problems associated with the use of TOC thinking tools. The study indicated that strategic commitment of top management, cross-functional teamwork, and targeted training are key to the successful implementation of TOC tools. The results have practical implications for manufacturing companies seeking to improve the effectiveness and efficiency of their production systems through comprehensive constraint management.
Reza Zanjirani Farahani, Nasrin Asgari, Luk N. Van Wassenhove
Textile waste is one of the most pollutant items globally, being strongly affected by fast fashion (FF) products. Public pressure has made many FF firms voluntarily collect a small fraction of their preowned items and export them to developing countries for reuse. However, some developing countries are launching import bans on second‐hand clothes. In addition, FF firms may soon be forced by extended producer responsibility legislation to collect more preowned items for reuse and recycling. To date, they do not have sufficient capacity to deal with this. Charities have been the key collectors and recyclers of unwanted clothes. Therefore, charities could help FF firms increase their capacity in this reverse supply chain (SC). However, we hardly witness such a collaboration for two main reasons: (i) charities prefer to sell high‐quality preowned items in the primary market to generate the highest possible revenue and FF firms may fear cannibalization, (ii) many charities believe that FF firms generate quantities of low‐quality items that require collection and sorting while being difficult to sell in the primary market. Charities also face competition from many small for‐profit organizations selling FF preowned items. While charities have the support of volunteers, they tend to be less efficient. This work urges Operations Management (OM) researchers to suggest innovative business models to help (i) FF firms and charities collaborate to solve the abovementioned issues, and (ii) charities to improve their traditional practices for competitiveness. This study is primarily a position paper highlighting some challenges and introducing interesting research problems. Although the paper is not a research paper, it follows a qualitative research method to collect and analyze the required supporting documents to justify arguments and statements. We collected primary and secondary data from the textile reverse SC members to familiarize the OM community with this context. The current changes in the textile reverse SC offer many great opportunities for impactful OM research.
Sushil Gupta, Hossein Rikhtehgar Berenji, Manish Shukla
et al.
We review and analyze the farming (upstream agribusiness supply chain) research literature since 1965 to identify farming research opportunities for operations management (OM) researchers. A majority of reviewed papers in our corpus, until the turn of the 21st century, primarily focus on improving operational efficiency and effectiveness of farming using optimization techniques. However, during the last two decades, farmers’ welfare and the interests of other stakeholders have drawn OM researchers’ attention. This expanded focus on farming research has become possible due to the proliferation of mobile communication devices and the Internet as well as advancements in information technology platforms and social media. Our review also shows that there is a paucity of OM literature that leverages increased data availability from the emergence of precision agriculture and blockchain to address major challenges for the farming sector emanating from climate change, natural disasters, food security, and sustainable and equitable agriculture, among others. Big data, in conjunction with opportunities for field‐based experimentation, artificial intelligence and machine learning, and integration of predictive and prescriptive analytics, can be leveraged by OM scholars engaged in farming research. We zero in on specific questions, issues, and opportunities for research in farming.
This study investigates the determinants of salary for operations management scholars. Is it as some opine that the only thing that matters is the publication count in “A” journals? How do the full range of activities: research, teaching, service, and administration, affect salary? The present research seeks to shed light on these questions and is predicated on a data set that consists of the research, teaching, and service outcomes, along with salary, for a full census of operations management faculty at 22 public universities (227 faculty) for base, 9-month salary and a subset of 15 universities (150 faculty) for total annual compensation. The results demonstrate partial support for the hypotheses that only “A” publications are financially rewarded, with publications in Production and Operations Management having the highest correlation with salary. The salary reward for publishing in “A” journals is unaffected by number of coauthors. Publications in other academic journals, top-tier practitioner journals, and top journals in sister-fields, and measures of impact, such as citations, H-index, and research awards provide no additional explanatory value of salary. Likewise, service and teaching awards do not add explanatory value. Female Full Professors are paid roughly $23,000 less in base salary than their male counterparts. Non-publishing factors that significantly contribute to salary include changing university affiliations (only for Associate and Full Professors), taking on administrative duties, as well as certain qualitative measures such as achieving Fellow status at the Production and Operations Management Society.
Background: The present study was designed to investigate the use of sound symbolic mappings in fictitious brand names. Sound symbolic mappings refer to the existence of a nonarbitrary relationship between individual sounds and associations of different attributes and concepts. Given that sounds have inherent meaning in them, brand names in line with the established symbolic effects could communicate tailored messages that are congruent with consumer expectations of a product. Purpose: As perceived congruency between a product and its label is highly desirable in marketing, the study sets out to test the noted effects in brand names. It was aimed to confirm the strength of sound symbolic effects through greater preference for names with a symbolic fit with the product. Study design/methodology/approach: Two product categories were chosen, and a set of fictitious brand names were created. The names differed only on the sounds purported to convey targeted symbolic associations of salient product characteristics. The participants were presented with a forced choice task consisting of paired name samples and a stated product category for each pair. Finding/conclusions: The results confirmed the presence of sound symbolic effects as participants favoured the names with embedded symbolism. This leads to a conclusion that sound symbolism may be used to affect consumer choices based on brand name preference. Limitations/future research: Even though the study included only two products and used forced choice tasks limited on name pairs, the findings clearly imply the potential of sound symbolic mappings in creating efficient brand names. Broadening the research to other products and the service sector would surely deliver intriguing results.
Production management. Operations management, Personnel management. Employment management
SignificanceAgricultural environment is dynamic and variable, with numerous factors affecting the growth of animals and plants and complex interactions. There are numerous factors that affect the growth of all kinds of animals and plants. There is a close but complex correlation between these factors such as air temperature, air humidity, illumination, soil temperature, soil humidity, diseases, pests, weeds and etc. Thus, farmers need agricultural knowledge to solve production problems. With the rapid development of internet technology, a vast amount of agricultural information and knowledge is available on the internet. However, due to the lack of effective organization, the utilization rate of these agricultural information knowledge is relatively low.How to analyze and generate production knowledge or decision cases from scattered and disordered information is a big challenge all over the world. Agricultural knowledge intelligent service technology is a good way to resolve the agricultural data problems such as low rank, low correlation, and poor interpretability of reasoning. It is also the key technology to improving the comprehensive prediction and decision-making analysis capabilities of the entire agricultural production process. It can eliminate the information barriers between agricultural knowledge, farmers, and consumers, and is more conducive to improve the production and quality of agricultural products, provide effective information services.ProgressThe definition, scope, and technical application of agricultural knowledge intelligence services are introduced in this paper. The demand for agricultural knowledge services are analyzed combining with artificial intelligence technology. Agricultural knowledge intelligent service technologies such as perceptual recognition, knowledge coupling, and inference decision-making are conducted. The characteristics of agricultural knowledge services are analyzed and summarized from multiple perspectives such as industrial demand, industrial upgrading, and technological development. The development history of agricultural knowledge services is introduced. Current problems and future trends are also discussed in the agricultural knowledge services field. Key issues in agricultural knowledge intelligence services such as animal and plant state recognition in complex and uncertain environments, multimodal data association knowledge extraction, and collaborative reasoning in multiple agricultural application scenarios have been discussed. Combining practical experience and theoretical research, a set of intelligent agricultural situation analysis service framework that covers the entire life cycle of agricultural animals and plants and combines knowledge cases is proposed. An agricultural situation perception framework has been built based on satellite air ground multi-channel perception platform and Internet real-time data. Multimodal knowledge coupling, multimodal knowledge graph construction and natural language processing technology have been used to converge and manage agricultural big data. Through knowledge reasoning decision-making, agricultural information mining and early warning have been carried out to provide users with multi-scenario agricultural knowledge services. Intelligent agricultural knowledge services have been designed such as multimodal fusion feature extraction, cross domain knowledge unified representation and graph construction, and complex and uncertain agricultural reasoning and decision-making. An agricultural knowledge intelligent service platform composed of cloud computing support environment, big data processing framework, knowledge organization management tools, and knowledge service application scenarios has been built. Rapid assembly and configuration management of agricultural knowledge services could be provide by the platform. The application threshold of artificial intelligence technology in agricultural knowledge services could be reduced. In this case, problems of agricultural users can be solved. A novel method for agricultural situation analysis and production decision-making is proposed. A full chain of intelligent knowledge application scenario is constructed. The scenarios include planning, management, harvest and operations during the agricultural before, during and after the whole process.Conclusions and ProspectsThe technology trend of agricultural knowledge intelligent service is summarized in five aspects. (1) Multi-scale sparse feature discovery and spatiotemporal situation recognition of agricultural conditions. The application effects of small sample migration discovery and target tracking in uncertain agricultural information acquisition and situation recognition are discussed. (2) The construction and self-evolution of agricultural cross media knowledge graph, which uses robust knowledge base and knowledge graph to analyze and gather high-level semantic information of cross media content. (3) In response to the difficulties in tracing the origin of complex agricultural conditions and the low accuracy of comprehensive prediction, multi granularity correlation and multi-mode collaborative inversion prediction of complex agricultural conditions is discussed. (4) The large language model (LLM) in the agricultural field based on generative artificial intelligence. ChatGPT and other LLMs can accurately mine agricultural data and automatically generate questions through large-scale computing power, solving the problems of user intention understanding and precise service under conditions of dispersed agricultural data, multi-source heterogeneity, high noise, low information density, and strong uncertainty. In addition, the agricultural LLM can also significantly improve the accuracy of intelligent algorithms such as identification, prediction and decision-making by combining strong algorithms with Big data and super computing power. These could bring important opportunities for large-scale intelligent agricultural production. (5) The construction of knowledge intelligence service platforms and new paradigm of knowledge service, integrating and innovating a self-evolving agricultural knowledge intelligence service cloud platform. Agricultural knowledge intelligent service technology will enhance the control ability of the whole agricultural production chain. It plays a technical support role in achieving the transformation of agricultural production from "observing the sky and working" to "knowing the sky and working". The intelligent agricultural application model of "knowledge empowerment" provides strong support for improving the quality and efficiency of the agricultural industry, as well as for the modernization transformation and upgrading.
Background: Present the relevance of the study and highlights the key points of literature overview. Purpose: As of May 25, 2018, General Data Protection Regulation (GDPR) has become mandatory for all organizations, public or private, that handle personal data of European citizens, regardless of their physical location. Higher education institutions (HEIs), namely public universities, are no exception to this requirement and, as in many other organizations, many HEIs begin the process of implementing the GDPR without meeting the minimum conditions necessary for implementation. The purpose of this study, therefore, is to present a model to determine the level of readiness of HEIs regarding the implementation of the GDPR. Study design/methodology/approach: With the objective of designing a new artefact as a readiness model for the implementation of the GDPR, this study follows Design Science Research as an approach to be used to build the readiness model, based on a set of 16 critical success factors (CSFs) previously determined. Findings/conclusions: A readiness model was designed, based on a set of 16 CSFs related to the implementation of GDPR in HEIs. Limitations/future research: This is a new area of study that needs further development, namely through the practical application of the model, allowing the improvement of the measurement levels of the different CSFs. Practical implications: The determined readiness model allows HEIs to realize a priori if they have the necessary conditions for the implementation of the GDPR, giving useful indications of the organizational dimensions and the CSFs that compose them where better performance is necessary to ensure a successful implementation. Originality/Value: As far as we know, this is the first model of readiness based on CSFs related to the implementation of GDPR in HEIs, being therefore a first contribution to the development of this area.
Production management. Operations management, Personnel management. Employment management
Abstract Since the beginning of the decade of the oceans (2021–2030), many countries failed to even develop minimal fisheries management. In some countries, where legislation is appropriately applied to the logistics of marine fishing operations, some measures of containment and fisheries management are being implemented. However, other countries have no effective plans for the sustainable development of the sector. In this paper, we have compiled information on the historical discrepancies in fisheries management in the extensive Brazilian waters and used it to illustrate the institutional neglect of this important blue-economy sector. Since the 1930s, when the first management agencies were registered, fisheries management has been handled by at least 10 different federal agencies. This discontinuity has resulted in a country that is ignorant, overall, of the quantity fished, where fishing occurs, and the status of its fish stocks. The only available long-term fishing production data is held by small state departments or marine protected areas and does not come close to reflecting the total catch of the country, with its continental dimensions and its mix of artisanal and industrial fisheries. For this reason, the present work has compiled information on the years of neglect in the governance of the fishing sector. In addition, we suggest the creation of a pivotal management model, based on three main pillars, for sustainable fisheries development.
Muhammad Yousuf Khan Marri, Rabia Jamshaid, Ramaisa Aqdas
The feeling of job boredom can impede employees’ performances but it can be improved through engaging them in job crafting activities. It is important to understand the concept of job boredom because it can lead to many negative consequences at the work place. The study attempted to investigate the impact of perceived organizational support, servant leadership, creative self-efficacy, and conscientiousness on job boredom through the mediating effect of job crafting. Data has been collected from 450 employees of Punjab and Sindh working in banking sector of Pakistan through questionnaires. The data is analyzed with the help of SPSS 22 and Smart PLS 3. The findings reveal that there is significant and positive impact of perceived organizational support, servant leadership, creative self-efficacy, and conscientiousness on job crafting. Additionally, job crafting has significant and negative impact on job boredom. However, job crafting also significantly mediate between perceived organizational support, servant leadership, creative self-efficacy, conscientiousness, and job boredom. Moreover, the study also suggests that future researchers can explore other outcomes of job crafting through which job boredom can be mitigated.
Business, Production management. Operations management
The high thermal and mechanical inertia of the oceans results in slow changes in sea surface temperatures (SSTs). Changes in SSTs, in turn, can impact atmospheric circulation including water vapor transport, precipitation, and temperatures throughout the world. The Pacific Decadal Oscillation (PDO), the tropical Atlantic SST gradient variability, and the West Pacific Warm Pool are patterns of natural climate variability that tend to persist over decadal time periods. There are current efforts to produce decadal climate predictions, but there is limited understanding if this information can be used in water resources management. Understanding the current state of decadal climate variability (DCV) phenomena and the probability of persisting in that state may be useful information for water managers. This information could improve forecasts that aid operations and short-term planning for reservoir management, domestic and industrial water supplies, flood risk management, energy production, recreation, inland navigation, and irrigation. If conditions indicate a higher likelihood of drought, reservoir managers could reduce flood storage space and increase storage for conservation purposes. Improved forecasts for irrigation could result in greater efficiencies by shifting crops and rotational crop patterns. The potential benefits of using a forecast must be balanced against the risk of damages if the forecast is wrong. Seasonal forecasts using DCV information could also be used to inform drought triggers. If DCV indices indicate that the climate has a higher probability of dry conditions, drought contingency plans could be triggered earlier. Understanding of DCV phenomena could also improve long-range water resources planning. DCV can manifest itself in relatively short-term hydrologic records as linear trends that complicate hydrologic frequency analysis, which has traditionally assumed that hydrologic records are stationary. HIGHLIGHTS
Knowledge of the current phase of decadal climate variability (DCV) indices and the probability of persisting in that state may be useful information for water managers for seasonal forecasts.;
DCV information could inform the allocation of flood storage and conservation storage spaces in reservoirs, but the potential use of DCV information depends on the risk if the forecasts are wrong.;
Analysis of DCV phenomena could help explain trends and breakpoints in nonstationary flood frequency analysis.;
River, lake, and water-supply engineering (General)
In recent years, the drive to contain health care costs has increased scrutiny of the traditional mode of delivering primary care where a patient is treated only by his primary care physician. In particular, greater reliance on non‐physician providers has been suggested as a lower‐cost alternative to the traditional set‐up. In this study, we consider a homogeneous patient panel treated by a solo primary care physician and develop a new model of patient health dynamics in which the health state for each patient on the physician’s panel follows Markovian transitions between “healthy,” “intermediate,” and “sick” states. In contrast to most currently used models, we treat patient demand for office visits as endogenous and managed by a physician via selection of a revisit frequency consistent with patient preferences. We model these preferences for the frequency of office visits using patients’ perception of their health status as well as the disutility associated with falling sick. At the center of our analysis are the interconnected decisions that a physician makes regarding the size of her patient panel and the patient revisit frequency. Our results quantify the overall impact of non‐physician providers on physician’s choices, physician’s expected daily compensation, and patients’ health. We characterize care settings, defined in terms of care effectiveness, characteristics of patient panel, as well as physician’s compensation scheme, that result in both parties, physician and patients, being better off as well as settings where at least one of the parties is worse off compared to the traditional approach.
Pablo Rovira, Tim McAllister, Steven M. Lakin
et al.
Metagenomic investigations have the potential to provide unprecedented insights into microbial ecologies, such as those relating to antimicrobial resistance (AMR). We characterized the microbial resistome in livestock operations raising cattle conventionally (CONV) or without antibiotic exposures (RWA) using shotgun metagenomics. Samples of feces, wastewater from catchment basins, and soil where wastewater was applied were collected from CONV and RWA feedlot and dairy farms. After DNA extraction and sequencing, shotgun metagenomic reads were aligned to reference databases for identification of bacteria (Kraken) and antibiotic resistance genes (ARGs) accessions (MEGARes). Differences in microbial resistomes were found across farms with different production practices (CONV vs. RWA), types of cattle (beef vs. dairy), and types of sample (feces vs. wastewater vs. soil). Feces had the greatest number of ARGs per sample (mean = 118 and 79 in CONV and RWA, respectively), with tetracycline efflux pumps, macrolide phosphotransferases, and aminoglycoside nucleotidyltransferases mechanisms of resistance more abundant in CONV than in RWA feces. Tetracycline and macrolide–lincosamide–streptogramin classes of resistance were more abundant in feedlot cattle than in dairy cow feces, whereas the β-lactam class was more abundant in dairy cow feces. Lack of congruence between ARGs and microbial communities (procrustes analysis) suggested that other factors (e.g., location of farms, cattle source, management practices, diet, horizontal ARGs transfer, and co-selection of resistance), in addition to antimicrobial use, could have impacted resistome profiles. For that reason, we could not establish a cause–effect relationship between antimicrobial use and AMR, although ARGs in feces and effluents were associated with drug classes used to treat animals according to farms’ records (tetracyclines and macrolides in feedlots, β-lactams in dairies), whereas ARGs in soil were dominated by multidrug resistance. Characterization of the “resistance potential” of animal-derived and environmental samples is the first step toward incorporating metagenomic approaches into AMR surveillance in agricultural systems. Further research is needed to assess the public-health risk associated with different microbial resistomes.