dataesr/scientific-paragraphs-categorization huggingface dataset:
Multiple application cases: The second cycle of empirical validation In this phase, multiple application cases involving four companies and five SPSS cases were conducted to verify the usability of the refined methodology in rich and heterogeneous SPSS contexts. Six interviews were conducted with top managers, including Uber (the National Senior Manager of Operations in Italy), DriveNow (the Managing Director), Europ Assistance (the Manager of Innovations & Product Marketing, and the Specialist of Innovations & Product Marketing), and Coca-Cola (the Global Manager of IT Digital Marketing Services, and the Senior Business Analyst).
Application case: DriveNow DriveNow is a "free floating" car-sharing company in which vehicles can be located by app or online within the neighborhood of the immediate location. In this SPSS, the IoT is a key attribute, as this service is an alternative to personal cars, and the client desires to know the exact car position. The IoT also affects functional quality, since it affects responsiveness and customer services. It influences the app's performance and reliability to find the nearest car in a short period. Therefore, the impacts on the production and delivery determinants of quality are clear: communication technologies affect the SPSS design, as it is essential to communicate to the customer assertively. The identification, tracking, and communication technologies influence the SPSS personalization.
The more the company can create a customized business, the more likely customers will perceive a better experience. The company also benefits from more information regarding the profile of users. In this SPSS, the network technologies affect the ability to match the demand and offer of cars around the city, balancing the distribution of cars in the city. These technologies impact the production and delivery determinants, influencing the overall performance of the service. Already, service management technologies are essential to grant the system's functionality and high-quality standards. They influence the technical quality and, in particular, the usability. A distinctive element of the business is fleet management solution, that is, how the company manages the demand and offers and operates the network to maintain a quality standard.
The classification of the IoT technologies turned out to be more challenging to understand than expected. Due to the several technologies that compose the IoT, and the classifications proposed by the literature, it was difficult to identify precisely where the specific technology adopted can be classified. In terms of the applicability of the methodology, some managers mentioned that the various IoT technologies considered could have other impacts and play a different function depending on the specific PSS types. The customer involvement matrix revealed that the different phases of the SPSS lifecycle could be considered representative. The methodology robustness was also corroborated in the research findings. It was observed that the IoT quality determinants matrix might play a strategic role in providing direction for future improvements and defining/mapping the current stage of the SPSS, although the matrix lacks more operational-level aspects to enable improvements in the firm's routine.
In addition, all managers agreed on the usability of the customer involvement matrix to evaluate the client's interaction in the SPSS lifecycle. The synthesis of the five application cases is reported (Table  1 ), which describes how the IoT affects the different quality determinants of the SPSS. Tables  2  and A3  provide the results of the cases obtained with the two matrices proposed. Finally, this validation stage paved the way for the application of the final methodology on multiple secondary cases to validate and refine it. The following section details the final stage of the recursive validation and refinement through eight additional use cases. (See Table  3. ).
Multiple-use case studies: The third cycle of empirical validation and refinement The recursive application of the methodology proposed with heterogeneous business contexts extended the analyses through eight additional use cases. More importantly, the study of companies delivering SPSS solutions in practice enabled us to comprehend similarities and differences in how IoT technologies can affect different quality determinants of the business and identify the role of IoT technologies in customer involvement along the SPSS lifecycle. In these use cases, the information on the type of SPSS, the IoT application field, and the IoT technologies employed in the solutions was gathered and structured (Appendix D, Table  A4. 1). Subsequently, it was possible to fill the quality determinants and customer involvement matrices (Appendix D, Tables A4.
Discussion This paper proposed and tested a methodology supporting firms to assess the impact of IoT technologies on perceived quality of SPSSs and determine the intensity of customer engagement through the IoT in the SPSS lifecycle to enhance the perceived quality of customers and stakeholders. The empirical analyses performed have provided key findings for the interface between marketing and operations strategies. In addition, they give a better understanding of how disruptive IoT technologies are embedded in SPSSs in most of their fields of application, considering a context in which servitization, productization, and the service paradox shape the competition. It has been found that the IoT primarily enables companies to differentiate their solutions from their competitors by improving the perceived quality and personalizing their SPSS by involving the customer throughout the lifecycle.
Findings demonstrated that this ability to differentiate SPSS offers results in enhanced customer experience and value-added to the business. The research findings also indicate that the methodology developed can support companies in evaluating both the contribution of IoT technologies in the SPSS lifecycle and the quality elements perceived by customers. This research has defined an essential categorization of quality determinants, according to which it is possible to better understand how the perceived quality of an SPSS can be determined. From the literature, it was observed that the research is mainly focused on the analysis of the quality performance of products or services, neglecting the SPSS as an integrated and complex system  (Ghotbabadi et al. , 2015; Gray et al. , 2015; Seth et al.
, 2006; Su et al. , 2014). The proposed determinants have been successively empirically validated with companies that considered them clear and sufficient to cover all aspects of defining SPSS quality. The significance of these determinants in evaluating the perceived quality is also supported by the fact that the primary application cases tested belong to heterogeneous business segments. The managers interviewed highlighted some relations between the four IoT technologies previously defined and the quality determinants to measure the perceived quality of an SPSS. Overall, the recursive validation and refinement process with 14 cases indicated that the methodology developed is able to assess the benefits of IoT adoption and informs about the customer's experience and perceived quality in SPSSs.
, 2019). In this regard, companies can offer customers a better experience and higher product and service quality. As a result, while preliminary, the findings demonstrated that the customer involvement matrix effectively evaluates customer involvement in the PSS lifecycle through IoT technologies, unveiling that they can positively influence the perceived quality of the users. From the literature in the area (e. g. ,  Paschou et al. , 2020; Pirola et al. , 2020; Aheleroff et al. , 2020a; Sun et al. , 2021) , it emerged that IoT technologies could offer uncountable possibilities to facilitate customer involvement in all the different SPSS phases. This justifies the choice in the second model to consider the SPSS lifecycle proposed by  Wiesner et al. (2015). The further step has been to identify the intensity of customer involvement that these technologies can trigger.
A qualitative characterization to analyze the degree of intensity of customer involvement has been proposed, addressing recognized knowledge gaps claiming for investigations on the expected benefits of the IoT in servitization from a customer's perspective  (Suppatvech et al. , 2019) , where non-technological (customer view) aspects are examined  (Schroeder et al. , 2020). The research findings also contribute to extend prior research claiming new methodologies supporting the successful realization of SPSSs  (Gaiardelli et al. , 2021)  in companies. The methodology tested proved helpful in expanding the understanding of how IoT technologies mainly act by involving the users in the delivery support and evolution phases of the SPSS lifecycle. Finally, an interesting finding observed, corroborated by the cases analyzed, was that IoT technologies are primarily implemented in the delivery phase of the SPSS solution, and the data acquired are used to incrementally upgrade the product and service for the successive release to the customer.
Contributions to theory and practice and implications This study extends the literature with a novel methodology capable of assessing how the IoT affects the perceived quality and customer involvement in SPSS businesses. Indeed, it provides a categorization for SPSSs of the quality determinants, the leading IoT technologies' functionalities, and the intensity of customer involvement in the SPSS lifecycle. In addition, this contribution addresses persistent knowledge gaps in the literature, responding to recent calls for a better understanding of  • DriveNow • Uber • Coca-Cola Smart Cooler • Coca-Cola Freestyle • DriveNow • Europ Assistance • Coca-Cola Smart Cooler • Coca-Cola Freestyle • Uber • Coca-Cola Smart Cooler • Coca-Cola Freestyle • DriveNow • Uber • Europ Assistance • Coca-Cola freestyle.
, 2019; Paiola and Gebauer, 2020; Lamperti et al. , 2023)  in lights of perceived quality and customer involvement. Overall, the main practical contribution of this study is to raise companies' awareness of the advantages that specific IoT technologies have on SPSS quality determinants based on a mapping of the different cases and on the analysis of the different industries in which they operate or decide to enter. In addition, companies can better compare the quality and customer involvement of a specific servitized solution being offered without and with IoT technologies. Therefore, this paper informs firms on how to move the initial steps in this new and challenging endeavor based on the business trends of servitization and IoT technologies. The main implications of the methodology tested for managers and practitioners are its ability to identify the main opportunities and challenges on how firms can assess IoT technologies' impact on the perceived quality of SPSSs and the intensity levels of customer engagement in the SPSS lifecycle.
The research findings indicate that the methodology enables the correct assessment of the role of the IoT, affecting current and future companies' service transition strategies, which can create more added value for customers  (Fliess and Lexutt, 2019; Khuntia et al. , 2021). The methodology validated also identifies the main quality determinants affected by the IoT of a PSS in a given industry and detects the critical factors concerning customers' perceived quality. Moreover, by measuring the intensity of IoT adoption, managers can use this research to identify how they involve customers in creating value in the SPSS, decide where to implement the IoT, and increase awareness of the role of customer involvement in new or existing SPSS models. These results help firms better understand the ongoing transformations (e.
g. , infrastructure, resources need, and future strategies) in the interfaces between marketing and operations strategies. The findings of the research can primarily support two main categories of companies. The first category involves the companies that already have an SPSS portfolio implemented, and for each SPSS, a specific strategy is defined to reach strategic goals aimed at satisfying the final customer. Therefore, based on these considerations, the methodology is useful for firms to check whether the IoT technologies embedded in the SPSS support the companies' strategic goals (translated into the quality determinants from the customer perspective). In this way, companies can structure their servitization strategy with better precision using the quality determinants of the methodology tested.
Accordingly, the research contributes to revealing to managers the factors or determinants that are not affected by IoT technologies. Consequently, firms can set directions for future improvements and prioritize investments in digitalization initiatives  (Peters and Saidin, 2000; Zancul et al. , 2016; Caputo et al. , 2022). Furthermore, the methodology developed supports the firms in examining if there is a misalignment concerning the desired digital strategic goals. Lastly, the approach tested in our study can assist companies in reviewing and realigning the current servitization strategy with the desired market demands in this context. The second category of potential companies that could be analyzed is those that have not yet implemented IoT technologies in their SPSS business portfolio.
Conclusions This research developed and validated a novel methodology to assess both the impact of the IoT on the perceived quality of SPSSs and the levels of intensity of customer engagement in the SPSS lifecycle to enhance the perceived quality of customers. Indeed, the literature shows that the research fields concerning the perceived quality and customer involvement in the SPSS lifecycle are in an immature stage of discussion. Furthermore, the gaps identified stress the significant amount of research still required to study the SPSSs embedded with IoT technologies. This study can be considered a pioneer attempt at a reference methodology for assessing the perceived SPSS quality impacted by disruptive IoT technologies. In particular, this study contributes to prior knowledge by filling the gaps between quality determinants and emerging IoT technologies in the SPSS literature.
This work also addresses recent calls from the literature to examine the interdependencies between technical (different IoT technologies) and non-technological (customer perspective) aspects in marketing and operations and SPSS customers' acceptance. The study responds to calls to understand how the customer's role can help shape digital servitization and how companies can use the IoT to interact with them to analyze customer engagement and experience in SPSSs through various theoretical lenses as well as to explore the impacts of the IoT in business. From a practical perspective, our study enhances our understanding of how IoT technologies may be implemented in various sectors through digital SPSSs, creating more value for customers and adding value to the customer journey. The methodology tested, composed of two main matrix-based models, addresses these knowledge gaps and offers guidance to identify the impact of IoT technologies on the perceived quality of an SPSS and evaluate the customer involvement in the different phases of its lifecycle.
The methodology proposed intends to be applicable in various SPSS contexts and suitable for the most common industrial segments. The impacts of IoT technologies on quality determinants of the value proposition are measured not by a particular metric but by comparing the same product or service without the IoT elements. This analysis is essential to identify the improvements that the IoT can provide to the product/service. The first model tested (IoT quality determinants matrix) can help companies with a well-defined strategy focused on enhancing specific quality determinants of an SPSS, with the awareness that IoT technologies can address a specific determinant. In this way, firms can prioritize investments and avoid falling into the so-called service paradox. For firms that do not have a clear digital service strategy, the methodology helps identify the quality determinants that have been supported and affected by the IoT in specific sectors to deliver SPSSs.
This enables companies to understand the success factors in terms of quality and, consequently, to plan the service transition strategy accordingly. The second model (customer involvement matrix) proved useful to firms to understand the degree of customer involvement along the SPSS lifecycle to identify areas for improvements and plan service value propositions for customer involvement using IoT technologies. Companies can measure the intensity of customer engagement and identify the phases of participation throughout the entire SPSS lifecycle. As a result, this element increases organizational awareness of how to design services and personalized value propositions for customers by applying IoT technologies that support this value creation. However, this research is not without limitations, offering relevant opportunities for a future research agenda and progress in the field.
First of all, the results obtained from both models are purely qualitative. Further research can be conducted to provide a quantitative impact of each technology on single quality determinants as well as to identify if customer involvement plays a higher role. In addition, due to the lack of literature specifically in this field, further research could be useful in defining a set of specific key performance indicators for measuring each quality determinant considered in the SPSS. Finally, the impact of IoT technologies on the SPSSs, in terms of the perceived quality and customer involvement in their lifecycle, represents a recognized underdeveloped research field, and its applications are still limited. The proposed classification developed in our research may thus be further extended in future studies.
Moreover, to develop insightful research in this area, we recommend further studies to address the following directions. On one side, the proposed framework could be further enriched, including the cognitive dimensions (see  Caputo et al. , 2023; Yang et al. , 2017)  to better understand how digital technologies could be better adopted throughout the SPSS lifecycle. Moving towards, while the IoT quality determinants matrix proved useful for identifying the quality elements, some additional questions could be explored: which quality elements are more crucial for enhancing the customer experience and involvement? Assuming that a firm has limited resources, how should the firm identify key areas for improvement and then prioritize the needs to improve? Having identified the key areas for improvement, how should the firm adjust its strategies for implementing the IoT technologies? What are the required changes to the firm's organization, infrastructure, and management tools? In a disruptive business model with IoT implementation, what are the key trade-offs faced by the firms' decisionmakers, and how are rigorous analytical models built to analyze and optimize the firms' decisions? Importantly, although this paper is aimed at investigating the impact of firms' IoT strategies on customers' perception of the quality and their interactions with the firms, it only presents the perspective of the firms through interviews with managers based on their experience and common sense rather than the customers' real feedback.
Indeed, given that the IoT is acknowledged for generating valuable data about customers' behavior and reactions, it is conceivable that a more rigorous analysis of the customers' perceived quality using some real data would be promising. Lastly, a fruitful avenue for future research that would interest scholars and practitioners is to investigate the concept of customer engagement in the Industry 4. 0 context. These research avenues are opportunities for developing new theories in the SPSS field. Overall, this paper provides contributions in demonstrating how to systematically assess the impact of the IoT on the perceived quality of SPSSs and customer involvement with SPSSs.
Method Three different databases were used: Medline, Scopus and Embase. The search was executed on March 23rd, 2022, for each of them. Some relevant articles were added manually to the search. To be selected for the review, articles needed to be published after 2000, written in English or French, focus on independent prescribing by community pharmacists, and concern a minor condition. Original research was accepted. Articles were imported into the Covidence systematic review software to perform the review.
Search strategy To identify articles of interest, three bibliographic databases were chosen and explored: Medline, Scopus and Embase, as they are generally considered as major databases for biomedical research. To be selected for the review, articles needed to be published after 2000 (due to recent implementation of pharmacist independent prescribing), written in English or French, focus on independent prescribing by community pharmacists, and concern a minor condition. All types of studies were accepted (quantitative, qualitative, and mixed-method studies). Papers were excluded if: They were not an original research; They were not focused on the main topic (e. g. , supplementary prescribing, collaborative prescribing, non-medical prescribing); They took place on a non-community setting (hospital, emergency, primary care clinic); They analyzed the implementation process (barriers and facilitators to PIP implementation); They explored the patients' experiences and views about PIP.
Papers mentioning speci c measures regarding the COVID pandemic or vaccination, contraceptive, naloxone, chronic conditions were also rejected because this area was considered not related to a minor condition. The search was executed on March 23rd, 2022, for each database. The bibliographies of included papers were reviewed for additional studies. The Medline search strategy is presented below and was adapted for Embase and Scopus. Articles were imported into the Covidence systematic review software to perform the review  [22].
Methodology A literature review using the key words 'Safe-and-Sustainable-by-Design' and 'SSbD', was performed for the identification of approaches, working descriptions and definitions for SSbD from policy, industrial and nongovernmental organisations (NGOs) with extra focus on the applicability of SSbD in practice by industry. To identify further relevant literature on already established concepts from the fields of nanosafety and chemistry, the literature review was then extended and included the keywords 'safe-by-design', 'sustainable chemistry', and 'design for X approaches'. Although these literature articles are not focused on SSbD, they can provide ideas on how to successfully implement safety and sustainability aspects in industrial processes that could be transferred to SSbD. Literature sources were open databases: policy documents and papers as well as public reports.
Significant data gaps are created due to data confidentiality practices employed by corporations to protect proprietary information and trade secrets about developments. For example, to assess the environmental impacts from a material using an LCA, the life cycle inventory (LCI) needs to be derived. To model LCI, extensive information on the production processes would be required which cannot be disclosed for commercial developments due to the risk of plagiarism. For some commercial products, LCIs are modelled but not open for review, which raises questions about the reliability of the datasets. Considering the issue that data gaps persist for many chemicals that have been on the market for years, it would be a great undertaking to obtain or model data in their design phase to apply SSbD. Apart from the data gaps, the lack of gap-filling approaches and the comparability of existing data is also a persistent problem plaguing academia and industry.