Adoption Of Robots Research Articles

Humanoid robot adoption and labour productivity: a perspective on ambidextrous product innovation routines

The increasing presence of humanoid robot adoption has generated a change in explorative and exploitative routines. If the explorative routines provoke creativity and critical thinking which are delivered by humans, exploitative routines induce repetitive actions and mimic activities which are executed by humanoids. This has raised the need for a better balance between both routines involving an ambidextrous dynamic process. Here, product innovations play a relevant role in enhancing such balance and labour productivity. If, from the conceptual standpoint, this phenomenon has already been explored, there is still the need to empirically analyse it. We thus offer a meso-analysis of twenty-four countries located in Europe through the lens of the Service Robot Deployment (SRD) Model and the conceptual lens of organizational ambidexterity. By a regression methodology, the results show that humanoid robot adoption is still not affecting labour productivity which, by contrast, is positively and significantly connected with both radically new and marginally modified/unchanged production of innovative routines. Our original contribution, which falls in the field of Human Resources Management and Artificial Intelligence, is that humanoids are not directly impacting labour productivity but indirectly through the generation of both new and marginally modified (or unchanged) routines. This situation persuades senior leaders to achieve a balance between exploitative and explorative product innovation routines. Supplemental data for this article is available online at https://doi.org/10.1080/09585192.2021.1897643 .

The adoption of AI service robots: A comparison between credence and experience service settings

AbstractWould consumers experience the same psychological processes when adopting AI service robots in different service areas? Results of multigroup structural equation modeling (n = 517) indicate that consumers' psychological processes are not the same in different service areas. Specifically, how consumers perceive AI service robots' usefulness is shown to be a significant underlying mechanism affecting consumers' attitudes toward adopting AI service robots in a service setting with a credence attribute (e.g., a hospital), but is not significant for a service setting with an experience attribute (e.g., a café). Furthermore, regardless of the different service settings, both privacy concerns and trust toward AI technology are shown to be significant antecedents, consistent with previous literature. Our results provide empirical insights at the intersection of psychology, marketing, and AI technology on how consumers adapt to using service robots across different service areas.

Spicing up hospitality service encounters: the case of Pepper™

PurposeThis paper aims to explore the implications of integrating humanoid service robots into hospitality service encounters by evaluating two service prototypes using Softbank Robotics’ popular service robot Pepper™: to provide information (akin to a receptionist) and to facilitate order-taking (akin to a server). Drawing both studies together, the paper puts forward novel, theory-informed yet context-rooted design principles for humanoid robot adoption in hospitality service encounters.Design/methodology/approachAdopting a multiple method qualitative approach, two service prototypes are evaluated with hospitality and tourism experts (N = 30, Prototype 1) and frontline hospitality employees (N = 18, Prototype 2) using participant observation, in situ feedback, semi-structured interviews and photo-elicitation.FindingsThe adoption of humanoid service robots in hospitality is influenced by the following four layers of determinants: contextual, social, interactional and psychological factors, as well as extrinsic and intrinsic drivers of adoption. These empirical findings both confirm and extend previous conceptualizations of human-robot interaction (HRI) in hospitality service.Research limitations/implicationsDespite using photo-elicitation to evoke insight regarding the use of different types of service robots in hospitality, the paper mostly focuses on anthropomorphized service robots such as Pepper™.Practical implicationsAdopting humanoid service robots will transform hospitality operations, whereby the most routine, unpleasant tasks such as taking repeat orders or dealing with complaints may be delegated to service robots or human-robot teams.Social implicationsWorking with and receiving service from Pepper™ changes the service encounter from direct practical, technical considerations to more nuanced social and psychological implications, particularly around feelings of self-esteem, social pressure and social judgment.Originality/valueThis paper presents one of the first empirical studies on HRI in hospitality service encounters using Softbank Robotics’ Pepper™. In doing so, the paper presents a novel framework for service robot adoption rooted in first-hand user interaction as opposed to previous, theory-driven conceptualizations of behavior or empirical studies exploring behavioral intention.

Influence of Social Distance Expressed by Driving Support Agent's Utterance on Psychological Acceptability.

In this study, we discuss the psychological acceptability of an utterance strategy used by the Driving Support Agent (DSA). Previous literature regarding DSA suggests that the adoption of a small robot as a form will increase acceptability. However, the agent’s utterance has been reported as a problem faced by the user. Therefore, in this study, we designed the agent’s utterance using politeness strategy as described by Brown and Levinson’s famous sociolinguistics and pragmatics theory and analyzed its acceptability through a participant-based experiment. In this experiment, we used positive and negative politeness strategies (PPS and NPS, respectively). In general, PPS is utilized to reflect the desire to be liked/recognized by others, whereas NPS is utilized to reflect the need for not wanting to be disturbed by others. Based on our results, PPS was rated high compared to NPS (n = 197). Therefore, many participants highly evaluated PPS. However, there was a group of participants who appreciated NPS. There were also participants who evaluated the two strategies equally. The number of participants in these three groups was observed at 4:1:1. This result contributes as an index on the utterance design of the DSA.

Adoption of AI-empowered industrial robots in auto component manufacturing companies

The usage of AI-empowered Industrial Robots (InRos) is booming in the Auto Component Manufacturing Companies (ACMCs) across the globe. Based on a model leveraging the Technology, Organisation, and Environment (TOE) framework, this work examines the adoption of InRos in ACMCs in the context of an emerging economy. This research scrutinises the adoption intention and potential use of InRos in ACMCs through a survey of 460 senior managers and owners of ACMCs in India. The findings indicate that perceived compatibility, external pressure, perceived benefits and support from vendors are critical predictors of InRos adoption intention. Interestingly, the study also reveals that IT infrastructure and government support do not influence InRos adoption intention. Furthermore, the analysis suggests that perceived cost issues negatively moderate the relationship between the adoption intention and potential use of InRos in ACMCs. This study offers a theoretical contribution as it deploys the traditional TOE framework and discovers counter-intuitively that IT resources are not a major driver of technology adoption: as such, it suggests that a more comprehensive framework than the traditional RBV should be adopted. The work provides managerial recommendations for managers, shedding light on the antecedents of adoption intention and potential use of InRos at ACMCs in a country where the adoption of InRos is in a nascent stage.

Robots and Firms

Abstract We study the microeconomic implications of robot adoption using a rich panel data set of Spanish manufacturing firms over a 27-year period (1990–2016). We provide causal evidence on two central questions: (1) Which firm characteristics prompt firms to adopt robots? (2) What is the impact of robots on adopting firms relative to non-adopting firms? To address these questions, we look at our data through the lens of recent attempts in the literature to formalise the implications of robot technology. As for the first question, we establish robust evidence for positive selection, i.e., ex ante better performing firms (measured through output and labour productivity) are more likely to adopt robots. On the other hand, conditional on size, ex ante more skill-intensive firms are less likely to do so. As for the second question, we find that robot adoption generates substantial output gains in the vicinity of 20–25% within four years, reduces the labour cost share by 5–7% points, and leads to net job creation at a rate of 10%. These results are robust to controlling for non-random selection into robot adoption through a difference-in-differences approach combined with a propensity score reweighting estimator. To further validate these results, we also offer structural estimates of total factor productivity (TFP) where robot technology enters the (endogenous) productivity process of firms. The results demonstrate a positive causal effect of robots on productivity as well as a complementarity between robots and exporting in boosting productivity.

Job Creators or Job Killers? Heterogeneous Effects of Industrial Robots on UK Employment

AbstractThere is concern about robots taking our jobs. This analysis looks at the impact of industrial robot adoption in the UK. Using a novel instrument to deal with endogeneity of robot adoption, estimates suggest that higher robot use is associated with increased employment and some evidence of a positive effect on part‐time pay, contrary to evidence from other countries. However, there is a large amount of heterogeneity across industries. The results show that industrial robots have directly replaced workers in automobile manufacturing. On the other hand, they have had positive effects on other areas of the labour market such as services.

UV-C Mobile Robots with Optimized Path Planning: Algorithm Design and On-Field Measurements to Improve Surface Disinfection Against SARS-CoV-2

Ultraviolet type-C irradiation (UV-C) is an effective no-contact disinfection procedure for surfaces and environments to reduce the spread of severe acute respiratory syndrome coron avirus 2 (SARS-CoV-2), the virus that causes COVID-19. This work evaluates the effect of the adoption of mobile robots for UV-C irradiation, compared to conventional disinfection methods based on static UV-C lamps. On-field evaluation was conducted to measure the energy dose delivered by a robot-based moving source of UV-C radiation at different locations in an indoor environment. The effectively released radiation dose was experimentally measured using distributed UV-C-sensitive detectors, considering all of the environmental factors involved. Moreover, this article proposes a novel trajectory planner consisting of a genetic algorithm (GA) that explores the possible trajectories and disinfection outcomes of a robot moving in a tunable artificial potential field (APF) and is capable of maximizing the delivered UV dose based on ambient geometry. The experimental results show that, compared to a conventional trajectory, an optimized one has better performance in terms of both the coverage of the radiated energy in the environment and the time required to complete the disinfection task.

Viability of Robots in Improving Autistic Student’s Engagement and Happiness When Learning

The adoption of robotics in other industries is increasing exponentially at a rapid pace due to increased interest in the field. Advancements in robotic technology allow them to be more capable in teaching a variety of topics while costing much less than even just a decade ago. This means that robots are more ready than ever before to be deployed globally in the educational space. The aim of paper is to check the viability of robots in improving autistic students’ engagement and happiness, rather than just their performance. 6 autistic students aged 7 – 12 took part in this experiment (4 male and 2 female). Several different structured scenarios and tasks were used to evaluate the student’s happiness and engagement on a scale of five. It was found that the children showed an improvement of 144% across the board, when comparing the happiness score and an increase of 61.4% for the students’ engagement score. It was also found that the children were showing much more emotion and were much more responsive during the tasks.

The adoption of self-driving delivery robots in last mile logistics

Covid-19, the global pandemic, has taught us the importance of contactless delivery service and robotic automation. Using self-driving delivery robots can provide flexibility for on-time deliveries and help better protect both driver and customers by minimizing contact. To this end, this paper introduces a new vehicle routing problem with time windows and delivery robots (VRPTWDR). With the help of delivery robots, considerable operational time savings can be achieved by dispatching robots to serve nearby customers while a driver is also serving a customer. We provide a mathematical model for the VRPTWDR and investigate the challenges and benefits of using delivery robots as assistants for city logistics. A two-stage matheurisitic algorithm is developed to solve medium scale VRPTWDR instances. Finally, results of computational experiments demonstrate the value of self-driving delivery robots in urban areas by highlighting operational limitations on route planning.

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention.

In order to effectively prevent sports injuries caused by collisions in basketball training, realize efficient shooting, and reduce collisions, the machine learning algorithm was applied to intelligent robot for path planning in this study. First of all, combined with the basketball motion trajectory model, the sport recognition in basketball training was analyzed. Second, the mathematical model of the basketball motion trajectory of the shooting motion was established, and the factors affecting the shooting were analyzed. Thirdly, on this basis, the machine learning-based improved Q-Learning algorithm was proposed, the path planning of the moving robot was realized, and the obstacle avoidance behavior was accomplished effectively. In the path planning, the principle of fuzzy controller was applied, and the obstacle ultrasonic signals acquired around the robot were taken as input to effectively avoid obstacles. Finally, the robot was able to approach the target point while avoiding obstacles. The results of simulation experiment show that the obstacle avoidance path obtained by the improved Q-Learning algorithm is flatter, indicating that the algorithm is more suitable for the obstacle avoidance of the robot. Besides, it only takes about 250 s for the robot to find the obstacle avoidance path to the target state for the first time, which is far lower than the 700 s of the previous original algorithm. As a result, the fuzzy controller applied to the basketball robot can effectively avoid the obstacles in the robot movement process, and the motion trajectory curve obtained is relatively smooth. Therefore, the proposed machine learning algorithm has favorable obstacle avoidance effect when it is applied to path planning in basketball training, and can effectively prevent sports injuries in basketball activities.

Robotics technology and firm-level employment adjustment in Japan

Unlike studies that analyze the impact of robotics technology on overall employment at the industry or firm level, this study investigated cross-division employment adjustment within a firm in an industry with greater diffusion and penetration of robotics technology. By examining changes in the composition of employment, we measured job creation and destruction at the division level and explored whether robotics technology, as a leading example of automation, not only displaces workers but also introduces new jobs in favor of labor. We made use of unique, division-level employment data for Japan’s manufacturing firms, together with industry-level data on the installation of industrial robots. We found that industry-level adoption of robots positively affects the firm-level job creation rate and the job destruction rate. Because the magnitude of the impact is larger for job destruction, robot adoption has an overall negative impact on firms’ net employment growth. Our findings suggest that the labor displacement effect of robotics technology and the emergence of new jobs due to technological change coexist even at the firm level.

Robotic colorectal surgery in Australia: evolution over a decade.

Despite reports of increasing adoption of robotics in colorectal surgery worldwide, data regarding its uptake in Australasia are lacking. This study examines the trends of robotic colorectal surgery in Australia during the last 10 years. Data from patients undergoing robotic colorectal surgery with the da Vinci robotic platform between 2010 and 2019 were obtained. Overall, numbers of specific colorectal procedures across Australia were obtained from the Medicare Benefit Schedule data over the same period. Pearson's correlation analysis was used to determine the statistical trends of overall and specific robotic colorectal procedures over time. A total of 6110 robotic general surgery procedures were performed across Australia during the study period. Of these, 3522 (57.6%) were robotic colorectal procedures. An increasing trend of overall robotic colorectal procedures was seen over 10 years (Pearson's coefficient of 0.875; P= 0.001). While this applied to both the public and private sectors, 90.7% of the procedures were undertaken in the private sector. Restorative rectal resections, rectopexies, and right hemicolectomies accounted for 82.6% of the robotic colorectal procedures performed during this period with an increasing trend seen over time for each intervention. Moreover, a robotic approach was utilized in 12.5%, 41.0% and 9.0% of all restorative rectal resections, rectopexies and right hemicolectomies undertaken in Australia during 2019, respectively. Robotic colorectal surgery has increased dramatically in Australia over the last 10 years, especially in the private sector. Penetration of robotic colorectal surgery in the public healthcare system will require focussed cost-benefit evaluations and governmental investment.

The effect of artificial intelligence on carbon intensity: Evidence from China's industrial sector

Artificial Intelligence (AI) is becoming the engine of a new round of technological revolution and industrial transformation; as such, it has attracted much attention of scholars in recent years. Surprisingly, scarce studies have shed lights on the effects of AI on the environment, especially with respect to carbon intensity. Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we use Chinese industrial sector data from 2005 to 2016 to investigate how AI affects carbon intensity. The empirical results show that AI, as measured separately by the adoption of robotics by industry and the number of academic AI-related papers, significantly reduces carbon intensity. The results remain robust after addressing endogenous issues. We find that there are both stages and industrial heterogeneity in the effects of AI on carbon intensity. AI had a more decrease effect on carbon intensity during the 12th Five-Year Plan than the 11th. Compared with capital-intensive industries, AI tends to have a more decrease effect on carbon intensity in the labor-intensive and tech-intensive industries. To enlarge the effects of AI on reducing carbon intensity, the government should promote the development and application of AI and implement differentiated policies in line with the industry characteristics.

Challenges and solutions for application and wider adoption of wearable robots.

The science and technology of wearable robots are steadily advancing, and the use of such robots in our everyday life appears to be within reach. Nevertheless, widespread adoption of wearable robots should not be taken for granted, especially since many recent attempts to bring them to real-life applications resulted in mixed outcomes. The aim of this article is to address the current challenges that are limiting the application and wider adoption of wearable robots that are typically worn over the human body. We categorized the challenges into mechanical layout, actuation, sensing, body interface, control, human-robot interfacing and coadaptation, and benchmarking. For each category, we discuss specific challenges and the rationale for why solving them is important, followed by an overview of relevant recent works. We conclude with an opinion that summarizes possible solutions that could contribute to the wider adoption of wearable robots.

Pandemics and Automation: Will the Lost Jobs Come Back?

COVID-19 has exacerbated concerns about the rise of the robots and other automation technologies. This paper analyzes empirically the impact of past major pandemics on robot adoption and inequality. First, we find that pandemic events accelerate robot adoption, especially when the health impact is severe and is associated with a significant economic downturn. Second, while robots may raise productivity, they could also increase inequality by displacing low-skilled workers. We find that following a pandemic, the increase in inequality over the medium term is larger for economies with higher robot density and where new robot adoption has increased more. Our results suggest that the concerns about the rise of the robots amid the COVID-19 pandemic seem justified.

The Impact of Robot Adoption on Global Sourcing

This paper studies the impact of robot adoption on firms’ global sourcing activities. Using a rich panel dataset of Spanish manufacturing firms, we show that robot adopting firms increased their intermediate input purchases from foreign and domestic suppliers between 2006 and 2016. The effects of robots differ across sourcing strategies: the highest in foreign outsourcing and the lowest in foreign vertical integration. We find that robot adopters fragment their production further by reducing the concentration of purchases from suppliers and the increase in intermediate input purchases is related to quality upgrading to a certain extent. Marginal treatment effects estimates suggest that responses to adoption are heterogeneous: higher probability of adoption intensifies the effects on outsourcing and weakens the effects on vertical integration. In contrast to rising concerns over reshoring, our findings suggest that robots have yet promoted trade in intermediate inputs.

Defining the learning curve for robotic pancreaticoduodenectomy at a single center with an established laparoscopic pancreaticoduodenectomy program

Presenter: Isabel DeLaura BS | Duke University Medical Center Background: Robotic pancreaticoduodenectomy (RPD) is a complex operation with a well-studied but inconsistent learning curve of approximately 30-250 cases. The learning curve for laparoscopic pancreaticoduodenectomy (LPD) using a team approach at Duke University has previously been reported and defined as 50 cases to reach proficiency. This study aims to compare the experience building the laparoscopic and robotic pancreaticoduodenectomy programs and to determine whether the transition from LPD to RPD shortened the learning curve for RPD. Methods: LPD and RPD cases performed at Duke University Hospital were retrospectively analyzed. Continuous operative, pathologic, and perioperative metrics from LPD and RPD were compared with independent t-test or Wilcoxon rank sum test, while categorical variables used Fisher’s exact test. Learning curves were defined with respect to OR time using CUSUM analysis. Distinct phases on the CUSUM plots for LPD and RPD were determined at clear transitions in the slope of the CUSUM fitline. Results: Thirty-nine LPD and 69 RPD were included in our analysis. Three LPD and 0 RPD were converted to open. Introduction of LPD was completed first as a hybrid technique including laparoscopic resection and open reconstruction for 23 cases (excluded from this analysis), followed by total LPD. Based on operative time, LPD had an apparent bimodal or inverted learning curve likely accounting for proficiency attained from hybrid LPD and introduction of additional surgeons and trainees later in the experience. The learning curve for RPD had an accelerated early experience phase (case 1-10), a skill consolidation phase (case 11-40), followed by an improvement phase marked by reduction in operative time (case 41-69). Compared to LPD, RPD had a shorter operative time (median 379 vs 406 minutes, p<0.03), improved LN harvest (median 15 vs 19, p<0.02), and similar estimated blood loss (EBL) (median 250ml vs 400ml, p = 0.26) and R0 resection (83% vs 85%, p = 1.0). Additionally, RPD had improved LOS (median 7 days vs 9 days, p<0.002), and lower rates of surgical site infection (10% vs 38% p<0.001), abscess (7% vs 23%, p<0.04), and readmission (13% vs 33%, p<0.03). Conclusion: Adoption of LPD can be safely performed by surgeons with experience in complex laparoscopic procedures. The transition of a competent robotic surgeon from LPD to RPD is facilitated by prior familiarity with MIS approaches to pancreatic resection which result in an abbreviated learning curve. Adoption of robotics can improve surgical quality and perioperative outcomes including operative time, LN harvest, LOS, post-operative infection, and readmission. As exposure to robotics within General Surgery, Hepatopancreatobiliary, and Surgical Oncology training programs increase, surgical quality and patient outcomes will be further improved using RPD over LPD.

Robots and Labor Regulation: A Cross-Country/Cross-Industry Analysis

This work discusses and empirically investigates the relationship between labor regulation and robotization. In particular, the empirical analysis focuses on the relationship between the discipline of workers' dismissal and the adoption of industrial robots in nineteen Western countries over the 2006-2016 period. We find that high levels of statutory employment protection have been negatively associated with robot adoption, suggesting that labor-friendly national legislations, by increasing adjustment costs (such as firing costs), and thus making investment riskier, provide less favorable environments for firms to invest in industrial robots. We also find, however, that the correlation is positively mediated by the sectoral levels of capital intensity, a hint that firms do resort to industrial robots as potential substitutes for workers to reduce employees' bargaining power and to limit their hold-up opportunities, which tend to be larger in sectors characterized by high levels of operating leverage.

Robots and Labor in the Service Sector: Evidence from Nursing Homes

In one of the first studies of service sector robotics using establishment-level data, we study the impact of robots on staffing in Japanese nursing homes, using geographic variation in robot subsidies as an instrumental variable. We find that robot adoption increases employment by augmenting the number of care workers and nurses on flexible employment contracts, and decreases difficulty in staff retention. Robot adoption also reduces the monthly wages of regular nurses, consistent with reduced burden of care. Our findings suggest that the impact of robots may not be detrimental to labor and may remedy challenges posed by rapidly aging populations. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.