Exploring Opportunities for Artificial Emotional Intelligence in Service Production Systems

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Exploring Opportunities for Artificial Intelligence in Service IFAC PapersOnLine 52-13 (2019) Emotional 1145–1149 Exploring Opportunities for Artificial Emotional Intelligence in Service Exploring Artificial Emotional Intelligence Production Systems Exploring Opportunities Opportunities for for Artificial Emotional Intelligence in in Service Service Production Systems Exploring Opportunities for Artificial Emotional Intelligence in Service Production Systems Production Systems Exploring Opportunities Artificial Emotional Intelligence in Service Marlenefor Amorim*, Yuval Cohen**, João Reis***, Systems Marlene Production Amorim*, Yuval Cohen**, João Reis***, MárioYuval Rodrigues**** Systems Marlene Amorim*, Cohen**, Marlene Production Amorim*, Cohen**, João João Reis***, Reis***, MárioYuval Rodrigues**** 

MárioYuval Rodrigues**** Marlene Amorim*, Cohen**, João Reis***,  Mário Rodrigues**** *DEGEIT & GOVCOPP, Universidade de Aveiro, Campus Universitário Santiago, 3810-193, Aveiro, Portugal (Tel: 351 Marlene Amorim*, Yuval Cohen**,de João Reis***, Mário Rodrigues**** *DEGEIT & GOVCOPP, Universidade de 234370200; Aveiro, Campus Universitário deua.pt). Santiago, 3810-193, Aveiro, Portugal (Tel: 351e-mail: mamorim@ Mário Rodrigues****  *DEGEIT & GOVCOPP, GOVCOPP, Universidade Universidade de de 234370200; Aveiro, Campus Campus Universitário deua.pt). Santiago, 3810-193, 3810-193, Aveiro, Aveiro, Portugal Portugal (Tel: (Tel: 351351*DEGEIT & Aveiro, Universitário de Santiago, e-mail: mamorim@  of Engineering, Tel Aviv Israel (e-mail: [email protected]) ** Department of Industrial Engineering, Afeka College 234370200; e-mail: mamorim@ ua.pt). *DEGEIT & GOVCOPP, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal (Tel: 351234370200; e-mail: ua.pt). ** Department of Industrial Afeka College of mamorim@ Engineering, Tel Academy, Aviv IsraelLisbon, (e-mail: [email protected]) *** Department of Engineering, MilitarydeScience and CINAMIL/CISD, Military Portugal (e*DEGEIT & GOVCOPP, Universidade Aveiro, Campus Universitário deua.pt). Santiago, 3810-193, Aveiro, Portugal (Tel: 351** Department Department of Industrial Industrial Engineering, Afeka College of mamorim@ Engineering, Tel Academy, Aviv Israel Israel (e-mail: [email protected]) 234370200; e-mail: ** of Afeka College of Engineering, Tel Aviv (e-mail: [email protected]) *** Department of Engineering, Military Science and CINAMIL/CISD, Military Lisbon, Portugal (email:[email protected]) 234370200; e-mail: ua.pt). *** Department Department of Engineering, Military Science Science and CINAMIL/CISD, Military Academy, Lisbon, Portugal (e** Department of Industrial Afeka College of mamorim@ Engineering, Tel Academy, Aviv IsraelLisbon, (e-mail: [email protected]) *** of Military and CINAMIL/CISD, Military Portugal (email:[email protected]) ** Department of Industrial Engineering, Afeka College of Engineering, Tel Academy, Aviv IsraelLisbon, (e-mail: [email protected]) mail:[email protected]) *** Department of Military Science and CINAMIL/CISD, Military Portugal (email:[email protected]) *** Department and CINAMIL/CISD, Military Academy, Lisbon, Portugal (e****IEETAof&Military ESTGA,Science Universidade de Aveiro, Aveiro, Portugal (e-mail: [email protected]) mail:[email protected]) ****IEETA & ESTGA, Universidade de Aveiro, Aveiro, Portugal (e-mail: [email protected]) mail:[email protected]) ****IEETA ****IEETA & & ESTGA, ESTGA, Universidade Universidade de de Aveiro, Aveiro, Aveiro, Aveiro, Portugal Portugal (e-mail: (e-mail: [email protected]) [email protected]) ESTGA, de Aveiro, Portugal [email protected]) Abstract:****IEETA This paper&offers an Universidade exploratory view about Aveiro, opportunities for(e-mail: the integration of Artificial ****IEETA ESTGA, de Aveiro, Aveiro, Portugal (e-mail: [email protected]) Abstract: This paper&offers aninUniversidade exploratory view about opportunities for the integration of Artificial Emotional Intelligence (AEI) service production systems. Service systems are conceptualized as Abstract: This paper an view opportunities for the integration of Abstract: This paper offers offers aninexploratory exploratory view about about opportunities for the integration of Artificial Artificial Emotional Intelligence (AEI) serviceco-production production systems. Service systems are conceptualized as increasingly digitalized and networked environments where employees, customers and Emotional Intelligence (AEI) service production systems. Service systems are conceptualized as Abstract: This paper offers anin exploratory view about opportunities for the integration of Artificial Emotional Intelligence (AEI) in service production systems. Service systems are conceptualized asa increasingly digitalized and networked co-production environments where employees, customers and technology engage in offers rich interactions to deploy outputs and create value. The studycustomers builds onand Abstract: This paper an exploratory view about opportunities for the integration of Artificial increasingly digitalized and networked co-production environments where employees, Emotional Intelligence (AEI) in service production systems. Service systems are conceptualized asa increasingly digitalized and networked co-production environments where employees, customers and technology engage in rich interactions to deploy outputs and create value. The study builds on systematic literature review to in identify three dimensions of and application of AEIThe for study improving service Emotional Intelligence (AEI) service production systems. Service systems are conceptualized technology engage in rich interactions to deploy outputs create value. builds on increasingly digitalized and networked co-production environments where employees, customers and technology engage inreview rich namely interactions to deploy and create value. builds on asaa systematic literature to identify three dimensions ofand application of customer AEIThe for study improving service efficiency and reliability, to augment, to outputs assist to mimic and/or providers increasingly digitalized and networked co-production environments where employees, customers systematic literature review to identify three dimensions of application of AEI for improving service technology engage in rich interactions to deploy outputs and create value. The study builds onanda systematic literature review to paper identify threea dimensions application of customer AEI dialogue for improving service efficiency reliability, namely tooffers augment, to assistofand to mimic and/or providers production and capabilities. The contribution to promoting the between technology engage inreview rich interactions to deploy and create value. builds onthe efficiency and reliability, namely tooffers augment, to outputs assistofand and to mimic customer and/or providers systematic literature to paper identify three dimensions application of customer AEIThe for study improving service efficiency and reliability, namely to augment, to assist to mimic and/or providers production capabilities. The a contribution to promoting the dialogue between thea knowledge domains of service systems and AEI. systematic literature review to identify three dimensions of application of AEI for improving service production and capabilities. The papertooffers offers contribution to promoting promoting the dialogue dialogue between the efficiency reliability, namely augment, to assist and to mimic customer and/or providers production capabilities. The paper aa contribution to the between the knowledge domains of service systems and AEI. efficiency and reliability, namely tooffers augment, toControl) assist and to bymimic customer and/or providers knowledge domains ofintelligence, service systems and AEI. production capabilities. The paper a contribution to customer promoting theLtd.dialogue between the © 2019, IFAC (International Federation ofand Automatic Hosting Elsevier All rights reserved. knowledge domains of service systems AEI. Keywords: artificial service production systems, interaction, human machine production capabilities. The systems paper offers a contribution to customer promotinginteraction, the dialogue between the Keywords: artificial service production systems, human machine knowledge domains ofintelligence, service and AEI. interaction, digital services. Keywords: artificial service production knowledge ofintelligence, service systems and AEI. Keywords: artificial intelligence, service production systems, systems, customer customer interaction, interaction, human human machine machine interaction, domains digital services. interaction, digital services. Keywords: artificial intelligence, service production systems, customer interaction, human machine interaction, digital services.   Keywords: artificial intelligence, service production systems, customer of interaction, human machine interaction, digital services. of experimentation human computer interactions, and  1. INTRODUCTION of experimentation of human computer interactions, interaction, digital services. particularly in investigations aiming for the developmentand of 1. INTRODUCTION of experimentation experimentation of human human computer interactions, and  of of computer interactions, and particularly in investigations aiming for the development of 1. INTRODUCTION systems for effective and efficient integration of providers’ 1. INTRODUCTION Services account for a major share of economic value in particularly in investigations aiming for the theinteractions, development of of experimentation of human computer and particularly in investigations aiming for development systems for effective and efficient integration of providers’ Services account for a major shareandof Akaka, economic valueThe in resources, i.e. employees, technology and the customers. of 1. INTRODUCTION A modern societies (Vargo, Maglio 2008). of experimentation ofand human computer interactions, and systems for effective and efficient integration of providers’ particularly in investigations aiming for the development of Services account for a major share of economic value in systems for effective efficient integration of providers’ resources, i.e. employees, technology and the customers. A 1. INTRODUCTION Services account for a major share of economic value in modern societies (Vargo, Maglio and Akaka, 2008). The prominent example has been the adoption of online and importance of the service industries for value creation andThe for particularly in investigations aiming for thethe development of resources,fori.e. i.e. employees, technology and the customers. A systems effective and efficient integration of providers’ modern societies (Vargo, Maglio and Akaka, 2008). resources, employees, technology and customers. A prominent example has been the adoption of online and Services account for a major share of economic value in modern societies (Vargo,industries Maglio andvalue Akaka, 2008). importance of in the economies service for creation andThe for mobile delivery channels, and their integration in cross employment is share estimated, and expanding, systems fori.e. effective andbeen efficient integration ofonline providers’ prominent example has the adoption of and resources, employees, technology and the customers. A Services account for a major of economic value in importance of the service industries for value creation and for prominent example has been the adoption of online and mobile delivery channels, and their integration in cross modern societies (Vargo, Maglio and Akaka, 2008). The importance of in the service industries forservice value and creation and for employment economies is estimated, expanding, channel processes, enabling firmstheir to and achieve gainsin incross the beyond 70%, while the diversity of companies, as mobile resources, i.e. employees, technology theofcustomers. A delivery channels, and integration prominent example has been thetheir adoption online and modern societies (Vargo, Maglio andservice Akaka, 2008). employment in economies is estimated, estimated, and expanding, mobile delivery channels, and integration in cross channel processes, enabling firms to achieve gains in the importance of in the service industries for value creation andThe for employment economies is and expanding, beyond 70%, while the diversity of companies, as design ofprocesses, service encounters, to meetto customer requirements, well as service functions within manufacturing grows prominent example has been the adoption of online and channel enabling firms achieve gains in the mobile delivery channels, and their integration in cross importance of the service industries for value creation and for beyondas 70%, 70%, while the diversity diversity of service service companies, as channel enabling firms achieve gains at in the design ofprocesses, service encounters, to meettocustomer requirements, employment inwhile economies is estimated, and expanding, beyond the of companies, as well service functions manufacturing grows notably for access and and service convenience an consistently (Gebauer, Fleischiswithin andestimated, Friedli, 2005). mobile delivery channels, their integration of service encounters, to meet requirements, channel processes, enabling firms tocustomer achieve gainsin at incross the employment inwhile economies and expanding, well as service functions within manufacturing grows design of service encounters, to meet customer requirements, notably for access and service convenience an beyond the diversity of service companies, as design well as 70%, service functions within manufacturing grows consistently (Gebauer, Fleisch and Friedli, 2005). unprecedented pace (Reis, Amorim and Melão, 2018). channel processes, enabling firms to achieve gains in the access and convenience at design of for service encounters, toservice meet requirements, beyond 70%, whilefunctions the diversity of service as notably consistently (Gebauer, Fleisch and 2005). notably for access andAmorim service convenience at an an unprecedented pace (Reis, andcustomer Melão, 2018). well service within manufacturing grows consistently (Gebauer, Fleisch and Friedli, 2005). Serviceasproduction systems can beFriedli, defined as companies, co-production design of service encounters, to meet customer requirements, unprecedented pace (Reis, Amorim and Melão, 2018). notably for access and service convenience at an well as service functions within manufacturing grows Service production systems can be defined as co-production unprecedented pace (Reis, Amorim and Melão, 2018). consistently (Gebauer, Fleisch and work Friedli,and 2005). technologies find a fertile ground in services, because environments wheresystems customers’ knowledge are Digital notably for opportunities access and service convenience at an Service production can be defined as co-production Digital technologies find a Amorim fertile ground ininnovation services, unprecedented pace (Reis, and Melão, 2018).because consistently (Gebauer, Fleisch and Friedli, 2005). Service production systems can be defined as co-production environments where customers’ work and knowledge are they create for continuous in the brought together with the company’s resources, implying that Digital technologies find aa Amorim fertile ground in services, because unprecedented pace (Reis, and Melão, 2018). environments where customers’ work and knowledge are Digital technologies find fertile ground in services, because they create opportunities for continuous innovation in the Service production systems can be defined as co-production environments where customers’ work andproduction knowledge are ways that information - that is a core input for service brought together with the company’s resources, implying customers always play an company’s active role in the of that the create opportunities continuous in the Digital technologies find-a for fertile ground services, Service production systems can be defined asknowledge co-production brought together with the resources, implying that they create opportunities for innovation in and the ways that information that is a coreininnovation input for because service environments where customers’ work andproduction are they brought together with the company’s resources, implying that customers always play an active role in the of the production systems - can be continuous exchanged, transported service outputs (Zeithaml et al., 2006, Pinhanez, 2008, Digital technologies find a fertile ground in services, because ways that information that is a core input for service they create opportunities for continuous innovation in the environments where customers’ work and knowledge are customers always play an active role in the production of the ways that information that is a core input for service production systems can be exchanged, transported and brought together with the company’s resources, implying that customers always play an active role in the production of the transformed by different users and contexts (Lusch and service outputs (Zeithaml etandal., 2006, Pinhanez, 2008, Sampson, 2010,with Spohrer Maglio, 2010). Service they create opportunities continuous innovation in and the systems -- can be exchanged, transported and ways that information - for that is a core input for service brought together the resources, implying that service outputs (Zeithaml et al., 2006, Pinhanez, 2008, production systems can be exchanged, transported transformed by different users and contexts (Lusch customers always play an company’s active role in the production of the production service outputs (Zeithaml et al., 2006, Pinhanez, 2008, Sampson, 2010, Spohrer and Maglio, 2010). Service Vargo, 2014). The recent- developments and the (Lusch adoption of production systems operate via an orchestrated set of ways that information that is a core input for service transformed by different users and contexts and production systems can be exchanged, transported customers always play an active role in the production of the Sampson, 2010, Spohrer 2010). Service Service byThe different users and contexts Vargo, 2014). recent developments and the (Lusch adoptionand of– service outputs (Zeithaml al.,Maglio, 2006, Pinhanez, 2008, Sampson, 2010, Spohrer and Maglio, 2010). production systems operateetand via an orchestrated set of transformed advanced information technologies (e.g. Internet-of-Things interactions, and exchanges, between the company and the production systems - technologies candevelopments be exchanged, transported and Vargo, 2014). recent and the adoption of transformed byThe different users and contexts (Lusch and service outputs (Zeithaml etand al.,Maglio, 2006, Pinhanez, 2008, production systems operate via an orchestrated set of Vargo, 2014). The recent developments and the adoption of– advanced information (e.g. Internet-of-Things Sampson, 2010, Spohrer 2010). and Service production systems operate via an orchestrated set of interactions, and exchanges, between the company the IoT, Cyber-Physical Systems, etc.) arecontexts triggering promising customer, involving the handling of materials, information, transformed byThe different users and and information technologies (e.g. Internet-of-Things Vargo, 2014). recent developments and the (Lusch adoption of–– Sampson, 2010, Spohrer and Maglio, 2010). Service interactions, and exchanges, exchanges, between the company and the advanced information technologies (e.g. Internet-of-Things IoT, Cyber-Physical Systems, etc.) are triggering promising production systems operate via of anmaterials, orchestrated set the of advanced interactions, and between the company and customer, involving the handling information, new Cyber-Physical avenues for service innovation and thethe emergence of and even the customer self (e.g. inanmaterials, theorchestrated so called “people Vargo, 2014). The recent developments and adoption of– IoT, Systems, etc.) are triggering promising advanced information technologies (e.g. Internet-of-Things production systems operate via set of customer, involving the handling of information, IoT, Cyber-Physical Systems, etc.) are triggering promising new avenues for service innovation and the emergence of interactions, and exchanges, between the so company and the smart service systems. The expanding links between the customer, involving the handling of information, and even the customer self (e.g. sectors in materials, the called “people processing services”, that include such as healthcare, advanced information technologies (e.g. Internet-of-Things new avenues for service innovation and the emergence of Cyber-Physical Systems, etc.) areand triggering promising interactions, and exchanges, between thesuch company and the IoT, and even even the the customer self (e.g. sectors in materials, the so called “people new avenues for service innovation the emergence of– smart service systems. The expanding links between the customer, involving the handling of and customer (e.g. in the so called “people processing services”, thatself include asinformation, healthcare, physical world and networked technologies, including transportation, education, etc.), as well as the coordination of IoT, Cyber-Physical Systems, etc.) are triggering promising smart service systems. The expanding links between the avenues for service innovation and links the emergence of customer, involving the handling of processing services”, thatself include sectors such asinformation, healthcare, smart service systems. The expanding between the physical world and networked technologies, including and even the customer (e.g. in materials, the so coordination called “people processing services”, that include as healthcare, transportation, education, etc.), as sectors well as such the of new networked sensors creates a powerful and the augmented space production tasks (Sampson and Spring, 2012). This conew avenues for service innovation and emergence of physical world and networked technologies, including smart service systems. The expanding links between the and even the customer self (e.g. in the so called “people transportation, education, etc.), as well as the coordination of physical world and networked technologies, including networked sensors creates a powerful and augmented space processing services”, that include sectors such as healthcare, transportation, education, etc.), as well assystems, the2012). coordination of for the interactions and collaboration between service production tasks (Sampson and Spring, This coparticipated nature of service production in practice, smart service systems. The expanding links between the networked sensors creates a powerful and augmented space physical world and networked technologies, including processing services”, that include sectors such as healthcare, production tasks (Sampson and Spring, 2012). This cosensors creates powerful and augmented space for the interactions and a collaboration between service transportation, education, etc.), as well assystems, the2012). coordination of networked production tasks (Sampson and Spring, coparticipated nature of service production inThis practice, providers and and customers for technologies, value creation. The translates into aeducation, number ofetc.), interactions taking place between physical world networked including the interactions and between service networked sensors creates a collaboration powerful and augmented space transportation, as well as the2012). coordination of for participated nature of service service production systems, inThis practice, for the interactions and collaboration between service providers and customers for value creation. The production tasks (Sampson and Spring, coparticipated nature of production systems, in practice, translates into a number of interactions taking place between generalization of the connections between information to customers and the production system, i.e. with the company’s networked sensors creates powerful and augmented space and customers for value creation. The for the interactions and a collaboration between service production tasks (Sampson and Spring, 2012). This co- providers translates into into athe number of interactions interactions taking place between providers and customers for value creation. The generalization of the connections between information to participated nature of service production systems, in practice, translates a number of taking place between customers and production system, i.e. with the company’s people and people to people, is supporting the connection of employees or with its mediated or automated interfaces and for the interactions and collaboration between service generalization the connections between information to providers andof customers for value thecreation. The participated nature of service production systems, in practice, customers into and the production system, i.e.taking with the the company’s generalization of the connections between information to people and people to people, is supporting connection of translates athe number of interactions place between customers and production system, i.e. with company’s employees or with its mediated or automated interfaces and objects,and spaces, things etc. As such the context for service touch points (Giannakis,2011, Schumann, Wünderlich and people providers and customers for value creation. The people to people, is supporting the connection of generalization of the connections between information to translates into a number of interactions taking place between employees or with its mediated or automated interfaces and people and people to people, is supporting the connection of objects, spaces, things etc. As such the context for service customers and the production system, i.e. with the company’s employees or (Giannakis,2011, with itsLarivière, mediated or interfaces and production and interaction between service actors is evolving touch points Schumann, Wünderlich Wangenheim, 2012, et automated al.i.e.2017). In company’s this vein, generalization of the connections between information to objects, spaces, things etc. As such the context for service people and people to people, is supporting the connection of customers and the production system, with the touch points (Giannakis,2011, Schumann, Wünderlich objects, spaces, things etc. As such the context for service production and interaction between service actors is evolving employees or with its mediated or automated interfaces and touch points (Giannakis,2011, Schumann, Wünderlich Wangenheim, 2012, Larivière, et al.pioneers 2017). In the thisdomain vein, towards a reality that is intuitive, context-aware, and with service production systems have been in people and people to people, is supporting the connection of production and interaction between service actors is evolving things etc.intuitive, As suchservice the context forand service employees or (Giannakis,2011, with itsLarivière, mediated or interfaces and Wangenheim, 2012, et al. 2017). In this vein, production and interaction between actors is evolving towards spaces, a reality that is context-aware, with touch Wünderlich and objects, Wangenheim, 2012, Larivière, et automated al.pioneers 2017). In the thisdomain vein, servicepoints production systems have Schumann, been in objects, spaces, things etc.intuitive, As suchservice the context forand service towards aa reality that is context-aware, with production and interaction between actors is evolving touch points (Giannakis,2011, Schumann, Wünderlich and service production systems have been pioneers in the domain towards reality that is intuitive, context-aware, and with Wangenheim, 2012, Larivière, et al. 2017). In this vein, service production systems have been pioneers in the domain Copyright © 20192012, IFAC Larivière, et al. 2017). In this vein,1162production and interaction between service actors is and evolving towards a reality that is intuitive, context-aware, with Wangenheim, service production systems have been pioneers in the domain Copyright © 2019 IFAC 1162 towards a reality that is intuitive, context-aware, and with 2405-8963 © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. service production systems have been pioneers in the domain Copyright © 2019 IFAC 1162 Copyright 2019 responsibility IFAC 1162Control. Peer review©under of International Federation of Automatic Copyright © 2019 IFAC 1162 10.1016/j.ifacol.2019.11.350 Copyright © 2019 IFAC 1162

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intelligence capabilities to enable gains in efficiency and value creation. Two additional concurrent facts, enabled by the advanced ICT, are colouring this scenario: the amazing growth in the volume and nature of data that is available, captured by the smart networked systems, and the sophistication of the computational capabilities, to deal with them (Yang et al. 2017). In such contexts, Artificial Intelligence (AI) allows for the delegation of the complex tasks of pattern identification, learning, etc. to the capabilities of computer based processes that can surpass humans in their performance for capturing, structuring and understanding large volumes of data (O’Leary, 2013). One of the emerging areas of research in AI concerns the development of capabilities to collect process and respond to emotional states of individuals. The concept of Emotional Intelligence (EI) refers to the ability of individuals to deal with his and others peoples’ emotions (Saloyey and Mayer, 1990). The nature of EI involves the activation of several concurrent skills concerning: i) the ability to identify emotions; ii) the ability to use emotions; iii) the ability to understand emotions and iv) the ability to manage them. Over the years several research efforts have been devoted to develop methods to support the assessment and measurement of EI, such as the Mayer-Salovey-Caruso EI Test – MSCEIT – that reflect the intrincate nature and complexity of EI (Mayer, Salovey, Caruso, and Sitarenios, 2003). In this paper we offer an exploratory study to gain understanding about AEI applications in service production systems. The study builds on the analysis from a structured literature review, to identify examples of applications and experimentation of AEI in service systems, therefore offering a structured approach to understand the potential of AEI in this domain, and suggesting paths for future research. 2. STUDY DESIGN AND METHODS 2.1 Process and criteria for selection of publications In December 2018, a systematic literature review was carried out resorting to SCOPUS database to search for publications (i.e. journal articles, extended abstracts, and conference proceedings) related with artificial emotional intelligence AIE. While no limitations were applied for date, or type of publication, only articles written in English were taken into account. The publications resulting from the data base query went through a selection process involving 2 researchers and that included the analysis of the title and the abstract, on a first reduction round, followed by the reading of the complete content of the manuscripts, to obtain a final set of publications to be included in the review. The guiding objective of the publications’ search was to identify research work describing or discussing the application of technologies for AIE in the specific context of service business. Given the novelty of the topic, and in order to limit the possible exclusion of relevant publications, in the database search the researchers employed the generic string “artificial emotional intelligence”, without restricting the query to the specific field of service companies, given that service

production contexts can be embedded in manufacturing contexts. Likewise in order to cover an ample spectre of publications that could refer to the application of artificial emotional intelligence to the broad field of production systems, the search considered an ample scope for the scientific fields, namely, “Engineering”, “Social Sciences”, and “Business, Management and Accounting”. Employing a stepwise approach the each researcher independently assessed the relevance of the publications, by reading the title and the abstract, using a rating scale, from 0 not relevant – to 2 – very relevant. Publications rated with a 0 were excluded from the sample, and those with at least 2 points were selected. In a following step the full text was read, allowing, if necessary for the revision of the decision about the inclusion of the publication. 2.2 Characterization of selected publications In the first phase of the query, a total of 511 documents were identified, by applying the aforementioned filtering conditions. The researchers’ assessment about the relevance of the publications led to a sample number of 67 publications, that was further restricted to 61 after full text reading. Most of the publications were excluded because they focused in the advancement in the technical development of artificial intelligence functions and capabilities, without analysing a specific industry or service scenario for its application. As expected, it was possible to observe an upward trend in the publication volume (Fig. 1). Regarding the domain and type of publication, a substantial number of the selected articles were obtained from indexed conference proceedings, something that can be explained by the emergent nature of the topic. Predominant domains included artificial intelligence scientific meetings as well as computer science, robotics and automation. Nevertheless, the selected sample suggested that the topic is gaining momentum among industry and production systems academics, as per some of the articles appeared in outlets such as: International Journal of Modern Manufacturing Technologies, International Journal of Production, International Journal of Cognitive Research in Science, Engineering and Education, Computers and Electrical Engineering, Journal of Cognitive Engineering and Decision.

Fig. 1. Yearly evolution of relevant publications 3. AIE AND SERVICE PRODUCTION SYSTEMS

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Articles were read in full, and analysed in order to address three questions: i) who is the subject of emotion identification?; ii) what kind of inputs are being used as sources data to understand the emotions; iii) what is the [production system] purpose of understanding the emotion? The researchers classified the manuscripts content into categories, to address each of the proposed questions independently, and this information was then compiled from the individual reading notes of each researcher. In order to provide a common framework for the classification of the information extracted from the publications, the researchers followed the service interaction classifications prevalent in the literature and that distinguish; face-to-face service interactions and face to screen service interactions. In this service typology proposed by Froehle and Roth (2004), faceto-screen services can be (usefully) subdivided in to technology mediated contact (e.g. phone service interactions, online calls, etc.) and technology generated customer contact (i.e. self-service). In what concerns the subjects considered for emotion identification, it was possible to observe articles focusing on two distinct actors of service systems: service employees, and service customers. The works addressing the emotions of employees, understood as key resources for the performance and quality of the service system outputs, were outnumbered by research work focusing on the emotions of customers. Examples of advanced work addressing the emotions of employees include: the work of Ribak et al. (2017) who propose an “intelligent control room” for monitoring and assessing the condition of employees (e.g. stress) from the collection and interpretation of voice and facial expressions; the work of Eboli, Mazzulla and Pungillo (2017) that explores the relationship between driving risk and the drivers’ physical and emotional conditions; the work of Thatcher and Kilingaru (2012) that build on iformation on eye movement to understand emotions from airline employees and identify abnormal flight conditons; the work of Baowman and Rogers (2016) that explores the influence of emotional issues for the decision making capabilities of direct care workers in ambient assisted living; and, in the same line of concern, the work of Coroiu (2015) that propose an emotional intelligent agent to support, and improve decision making in manufacturing contexts. Overall the extant work is acknowledging that the emotional conditions of employees are determinant for their effectiveness and for the quality of their work, and that their emotions are subject to variations (e.g. derived from personal contexts as well as from workload conditions). The development of AEI models and applications in this domain is advancing in ameliorating the capabilities for emotion recognition and in the development of tools that can assist and improve employee performance, by creating awareness of emotional conditions and providing tools and information to mitigate any undesirable consequences. In the domain of emotions understating for customers, there is a remarkable concentration of efforts in the areas of health and care services, education and online purchasing and service delivery. Illustrative examples include: the work of

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Amifra et al. (2018) that integrates emotion recognition – for home patients - into monitoring services for chronic disease; and, in the same vein the work of Myakala et al. (2017) that identify emotions from children cry-detection, in order to assist and inform care personnel. The early work of Marcelino et al. (2015) explored the same domain of service opportunity, i.e. elderly care by incorporating emotion understanding in an eService platform for pervasive case, to support the daily monitoring and detection of danger scenarios. In the domain of education, online learning contexts are a rich field for experimenting with AEI applications, given their ease in capturing and tracking the user information. Subramainan et al. (2017), Xu et al. (2017), among others, are including capabilities for students’ emotion recognition (e.g. anger, anxiety, boredom) into the monitoring capabilities for an online learning system in order to emit alerts and allow educators to adjust their teaching strategies. In the domain of online service, notably in retail and customer assistance, the pioneer examples stand out, such as the work of Povoda et al. (2015) that focused on understanding customer emotions building on data from helpdesk messages to devise priority rules for adequate service responses. The data being used to inform emotion-understanding models, is primarily driven from two types of sources. One domain concerns behavioural sources, such as facial expressions, voice tones, speech characteristics, eye movement, lip movement and so on. Collecting such data requires the adoption of capturing and recording technologies such as, eye tracking devices (Thatcher and Kilingaru, 2012); biometric mouses (Kaklauskas et al., 2008); computer and mobile cameras (e.g. Xu et al. 2017), among others. One of the key advantages of capturing behavioural data that is frequently pointed out is the fact that many of such devices are generalized technology, hold by most of the population and therefore being easily accessible and with a relatively low cost. Other area of data that can allow for the understanding of emotions is performance data, i.e. data that offers evidence on the tasks conducted by employees and customers, and whose characteristics (e.g. duration, delays, interruptions, failures) can allow for inferring about the emotional condition of individuals. This data is extracted from records and outputs of production processes (e.g. text, e-mail messages, log times, etc.). The digitalization of service processes comes in favour for the abundance of such data. With the proliferation of mediated services, self-service systems, online services and alike the traceability of customers and employees interactions and task results is largely increased (as compared to traditional face.to-face services where, most often, the service encounters were not recorded or tracked). The purposes of the reported adoption of AIE in service systems, are related with the objectives of improving the efficiency, the reliability and the overall performance of the production system. This is set to be achieved by the augmenting and/or complementary role that AEI has towards the capabilities of employers, customers and technology. Anchoring the analysis on the typologies for service interaction, that involve the elements of the service

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production system – employees, technology and customers – it was possible to observe that the AIE experimentations are contributing to enable service capabilities around three broad forms, as illustrated in Fig.1.

infused with some emotion recognition and adequate response capabilities that humans hold. Examples of this stream of work include, Tuyen, Jeong and Chong (2017), that have developed a robot for the delivery of ambient assisted living services, or Loghmani, Rovetta and Venture (2017). 4. CONCLUSIONS

Fig. 1 Subjects, Data Sources and Capabilities associated with AEI in service systems The application of AIE described in service systems is allowing for the improvement of service system capabilities in the following manners: i) “augment” the capabilities of employees and customers in face to face service interactions. In such service settings AI algorithms and models that are being advanced are, for instance providing employees with information about their own emotions, in order to allow them to adjust their behaviour (e.g. the aforementioned work of Ribak et al. (2017) that aims to gather knowledge about the emotional conditions of employees in a intelligent control room). In addition to providing such, own “awareness” information, we could also observe examples of the possibilities of AI for providing knowledge to employees about the emotional state of the customers, i.e., providing insights about the emotional sate of the “other” (e.g. the aforementioned work of Povoda et al. (2015) that offered to the company knowledge about customer emotions derived from the content of helpdesk messages). This perspective allows for a tipification of the functional purposes of AEI in face to face service settings as follows: augmenting service capabilities by offering self and/others’ emotion understanding; ii) “assist” the capabilities of the service production system in technology mediated interactions. In such settings some of the richness of the face to face contact between employers and customers is lost (e.g. call center services). As such some of the AEI proposals that are being advanced are supporting the work of the employees offering complementary information that they cannot get by personal contact (e.g. see for example the referenced works o AEI applications in online learning settings, were the system is capturing data to understand the users’ attention, motivation, and allow for pedagogical adjustments - Subramainan et al. (2017), Xu et al. (2017)). iii) “mimic” the capabilities of employees in automated contact, or self service systems. In such service settings the state of the art of the existing AEI proposals concerns the utilization of customers’ data to train and “qualify” automated systems, including robots, so that they can be

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