An artificial-intelligence-based method for assessing service quality: insights from the prosthodontics sector

Abstract

PurposeThe prosthodontics sector is facing major challenges because of scientific and technological advances that imply a clearer definition of lines of action and decision making processes. Measuring quality of service in this sector is a complex decision problem since the perceptions of three main players need to be considered: patients, dentists and dental technicians. This study sought to develop an artificial-intelligence-based (AI-based) method for assessing service quality in the dental prosthesis sector.Design/methodology/approachUsing strategic options development and analysis (SODA), which is grounded on cognitive mapping, and the measuring attractiveness by a categorical based evaluation technique (MACBETH), a constructivist decision support system was designed to facilitate the assessment of service quality in the dental prosthesis sector. The system was tested, and the results were validated both by the members of an expert panel and by the vice-president of the Portuguese association of dental prosthesis technicians.FindingsThe methodological process developed in this study is extremely versatile and its practical application facilitated the development of an empirically robust evaluation model in this study context. Specifically, the profile analyses carried out in actual clinics allowed the cases in which improvements are needed to be identified.Originality/valueAlthough already applied in the fields of AI and decision making, no prior work reporting the use of SODA and MACBETH for assessing service quality in the prosthodontics sector has been found.

A sociotechnical approach to vaccine manufacturer selection as part of a global immunization strategy against epidemics and pandemics

“…The model basic format is a directed graph with nodes that are associated with concepts and arches. The latter are interpreted as causal relationships between concepts (Fonseca et al, 2020 ; Martins et al, 2020 ) and given influence signs (i.e . , + or –) or influence weights, which results in a signed or weighted map, respectively.…”

“…The first was to promote productive debate among participants in this decision-support procedure. The second reason was to decrease the number of relevant criteria omitted, while the last was to stimulate an expanded learning process based on a deeper comprehension of the criteria’s cause-and-effect links (Eden, 2004 ; Martins et al, 2020 ).…”

“…The model basic format is a directed graph with nodes that are associated with concepts and arches. The latter are interpreted as causal relationships between concepts (Fonseca et al, 2020;Martins et al, 2020) and given influence signs (i.e., + or -) or influence weights, which results in a signed or weighted map, respectively. The cognitive mapping technique facilitates groups of actors' efforts to manage complex decision problems more effectively (Eden & Ackermann, 2004;Paes de Faria et al, 2020).…”

“…Although cognitive maps are deeply affected by the decision makers' perceptions (Tegarden & Sheetz, 2003), these maps provide added value by visualizing the group's perspectives and real-life experience, thereby overcoming various limitations of purely statistical approaches (cf. Martins et al, 2020;Paes de Faria et al, 2020). For example, the issue of religious or philosophical beliefs opposing immunization is often omitted from discussions, but the cognitive mapping process was able to detect the effect of this issue.…”

Transcendent service management

“…The interesting question of customer attributions of blame to either robots or human servers in the case of service failures is reported by Belanche and colleagues (2020). Artificial intelligence (AI) for services is also a focus, a special issue, of the journal edited by Heinonen et al (2020), which includes work on AI service automation (Meyer et al, 2020), and the use of AI methods to assess service quality (Martins et al, 2020).…”