dataesr/scientific-paragraphs-categorization huggingface dataset:
This time Tom asks Paul to do it because J does not want to give them anymore, he does not understand why. Paul: It is an order from the PA [Public Accountant], we know very well where it comes from. Tom: Yes, but I don't understand… Without these pieces of information, Tom can hardly work. Paul's theory is that the PA has explicitly asked J not to give this information anymore. Paul and Tom chat about the fact that there is little verification over accounting in the course of the year. Paul regrets the very low level of control if any that exists toward the accounting office. Paul often suspected the budgetary office to be 'on the side of the PA' and here imagines that they have been ordered not to share specific accounting information with Tom. When asked about it, the budget office did not show any animosity toward the management accountant but did express a sense of surprise that only he is granted access to their databases without any reciprocitythe budget office itself cannot access the management accountant's database.
Where he considered himself legitimate and knowledgeable in public accounting, she deemed it illegitimate to have to answer to a management accountant whom she did not consider qualified to question her work. She interpreted these intrusions as an attempt to strip her of her property, her knowledge and her position. Blocking the access of the management accountant to critical blocks of the accounting database was a way for her to claim property over her zone of expertise and reassert at the same time the specificity of the financial management of a public establishment. Silence, in this form of restricted access to key language blocks, efficiently constrained the management accountant in his work and highlighted the limits to his understanding of the articulation between private-inspired management accounting and public accounting.
Acknowledgements The authors would like to thank  Jeff Everett  and the anonymous reviewers for their comments. We would also like to thank  Bernard Leca ,  Claire Dambrin ,  Rebecca Warren ,  Rob Charnock ,  Maureen Mcculloch ,  Per Ahblom ,  Finia Kuhlmann ,  Lukas Lohlein  as well as the participants at a Paper Development Workshop (June 4, 2020), the CBF (November 19, 2021) and the Alternative Accounts Conference (online, 2022).
, 2015; Kamble et al. , 2018; Xu, 2014)  of digitalization, but does not develop any quantitative and actionable model to ease digital technology adoption in the supply chain. In this paper, we use the Bayesian Belief Network (BBN) method and rely on institutional and contingency theories to find answers to the stated research questions. BBN allows us to analyse the problems from an open and exploratory perspective  (Khazraee et al. , 2018)  as was necessary to meet the objectives of the study. We utilized data collected from experts in the field of supply chain management and academia to create the network and then validate the same. Our paper makes a substantial contribution to the field of digitalization in general and supply chain digitalization in particular. We use the institutional and contingency theoretic lens to provide generalizable theoretic insights on the role of competing forces and their moderation by external shocks.
This review revealed the forces that influence the digitalization of organizations, either positively or negatively. These identified forces were used for drawing causal maps that were then presented to the panel of experts for verification. The subsequent steps are detailed below. In this paper, we followed the knowledge-based methodology of BBN construction proposed by  Nadkarni and Shenoy (2004)  and it includes the following major steps. (a) Data Elicitation: We used a structured approach of data elicitation from a panel of eight experts. The details of the panel and their associated industry are listed in Appendix A2. Each of the experts was provided with a structured questionnaire consisting of a comprehensive list of 1 external force, 8 institutional forces, and 21 influencers and their definitions, which were identified from the literature review in Section 3.
Limitations This study, as with all research studies, comes with its own set of limitations that provides avenues to study interesting research questions in the future. One of the limitations of this study is its design itself. As exploratory research, we designed this study to capture new theoretical relationships and frameworks. Through the use of BBN, we were able to validate and test these relationships. However, more studies are required with larger datasets and diverse methods to evaluate the limits of the proposed relationships in different circumstances. Another limitation emerges from the definition of the terms used in the study. In order to generate a generalizable framework, we treated the object of study to be digitalization in supply chains of any kind. We acknowledge the differences that different kinds of technologies may have in implementation and associated challenges.
Examining various categories of determinants would enable a better understanding of the nature of digital technology adoption drivers and evaluate the weight of each category. The core approach of this framework relies on identifying the effects of internal factors (i. e. , technological and organizational) and external environmental factors. The use of an integrative model that combines various drivers and a wide range of digital technologies would contribute to building a theoretical framework on why SMEs adopt digital technologies, and whether distinctive features influence digital technology adoption in SMEs at different levels. Data collected were used in 2020 from 15 346 SMEs employing one or more persons from the EU27 and additional 12 non-EU countries. The data focus on the triggers and challenges that SMEs in Europe face when growing, transitioning to more digitalization and innovative business models.
A. Data Collection To test our research hypotheses, a dataset collected in Europe, namely the Eurobarometer 486, was used. The survey was conducted in the EU27 and an additional 12 non-EU countries, and focuses on the barriers and challenges that SMEs in Europe face when growing, transitioning to more digitization. The survey collected responses from 15 346 telephone interviews with SMEs employing one or more persons between February 19 and May 5, 2020.
C. Model The data analysis has followed different stages. A descriptive analysis and a univariate analysis were performed to explore the sample profile, and then multivariate analysis techniques and ordered logit regression were applied to address the research questions. The steps involved in data analyses using Stata Software are introduced in the following. 1) Ordered Logit Model: Because the dependent variable is ordered (no adoption, difficulty to adopt, adoption of basic technologies, and adoption of advanced technologies), an ordered logit regression was used to estimate the following models: Y i = Business i β 1 + ε i (1) where Y i is the level of digital adoption of firm i, Business i indicates the rating of the business environment, β is the vector of coefficients, and ε i is a normally distributed random error term.
VI. CONCLUSION Our current understanding of what drives SMEs' digital transformation is limited by the lack of integrative research that covers issues beyond contextual imperatives (i. e. , economic, technological, legal, and financial infrastructure). Using a quantitative large-scale panel dataset across different EU and non-EU countries, this study intended to complement the existing qualitative and conceptual works on digital technology adoption drivers. Investigating a variety of categories of drivers allowed us to overcome the fragmented picture provided by previous research that focused on separate aspects of the adoption decision. Our research helps advance the conversation on digitalization drivers especially by bringing the discussion into the organization boundaries, as our findings highlight the predominance of organizational drivers over the technological and environmental ones.
, 2021; Fliess and Lexutt, 2019; Sun et al. , 2021). Nevertheless, despite several extant methodologies to measure quality attributes, it is difficult to find reliable and widely accepted approaches supporting companies to evaluate the quality of SPSS business with robustness. This occurs because the product and service elements are usually assessed separately  (Gaiardelli et al. , 2021; Kropp and Totzek, 2020). Consequently, considering the challenges mentioned above for companies and the emerging business trends reshaping markets and operations  (Kumar et al. , 2019)  triggered by the adoption of the IoT in SPSSs, the purpose of this research is to develop, test, and validate a novel methodology able to support companies to evaluate the impact on both the perceived quality and the degree of customer engagement using the IoT in SPSS models.
This research makes a major contribution to the current body of knowledge that examines the role of the IoT in digitally servitized business. Firstly, the paper addresses literature gaps between quality determinants and emerging IoT technologies in the SPSS research  (Sun et al. , 2021). Second, the study responds to recent calls from the literature  (Schroeder et al. , 2020; Kumar et al. , 2019)  to examine the relationships between technical (i. e. , IoT) and non-technological (i. e. , users) elements at the interface between marketing and operation decisions and their impact on SPSS customers' acceptance. Third, the findings extend previous research that aims to understand how the customers' role can help shape digital servitization phenomena  (Paiola and Gebauer, 2020)  and how companies can use the IoT to interact with them to analyze customer experience in SPSSs.
Fourth, the paper contributes to theory and practice, providing additional evidence that helps us to understand how IoT technologies may be implemented in different businesses to add more value to the customer journey through SPSS  (Kropp and Totzek, 2020). The paper is structured as follows. Section 2 introduces the research context on which this study is based, also unveiling the knowledge gaps. Section 3 describes the research methodology adopted, explaining data collection and analysis criteria. Section 4 presents the process that led to the development of the proposed methodology, its generation and improvement with the application case, and validation through primary and secondary cases. The discussions and the managerial and theorybuilding implications are provided in Section 5. Finally, Section 6 reports the conclusions, limitations, and further research opportunities.
As a result, although the importance of effective customer involvement in the SPSS context has been highlighted  (Baines et al. , 2017) , there is still a great deal of uncertainty about how firms can implement servitization strategies for effective customer involvement  (Kimita et al. , 2016; Schroeder et al. , 2020)  along with SPSS offers. Previous studies related to the analysis of customer involvement in product-based or service-based businesses focus on specific business contexts and lack generalizable information about theory and practice. Most importantly, the research on customer involvement in SPSSs is mainly related to the first phase of the lifecycle  (Jui-Che et al. , 2013) , the so-called Beginning of Life (BoL), neglecting the Middle of Life (MoL) and the End of Life (EoL) stages  (Wiesner et al.
, 2015). Some approaches to analyzing strategies for customer involvement in the MoL and EoL phases  (Wiesner et al. , 2015)  have been introduced in the literature, lacking empirical validation and not considering all the PSS archetypes  (Tukker, 2004)  in different applications  (Kimita et al. , 2016). Consequently, an integrated methodology oriented to support companies to guide the process of customer involvement contemplating all the phases of the SPSS lifecycle is still missing. Although implementing customization strategies in SPSS requires new information and communication technologies (ICTs) infrastructures, the interrelation among these phenomena has been examined in an incipient way in the literature due to the novelty of this research theme. Disruptive IoT technologies' adoption requires relevant efforts regarding knowledge acquisition, product innovation, business process definition, and deployment  (Zancul et al.
, 2016). Therefore, it is evident that firms need systematic methods to prioritize actions related to IoT adoption and derive the related business decisions to prioritize their investments in specific areas  (Paschou et al. , 2020; Aheleroff et al. , 2020a; Suppatvech et al. , 2019; Zancul et al. , 2016). Consequently, it is assumed that applying the IoT as an ICT solution could assist the development of SPSS models by using information feedback loops in any lifecycle stage of the SPSS. Moreover, due to the increasing interest in customizing and identifying customer involvement  (Ostrom et al. , 2015)  as a mechanism to potentially increase the benefits for both companies and decision-makers, the use of prescriptive methodologies supporting SPSS customers' acceptance  (Kropp and Totzek, 2020)  and customer involvement in SPSS development can positively influence business performance  (Kimita et al.
To summarize, the key message from the literature demonstrated the lack of evidence examining how IoT technologies can enhance the perceived quality in the market of PSS models. In addition, reference frameworks are needed to guide scholars and managers to measure the quality of both products and services in SPSS simultaneously in a generalizable and suitable way for different industries. Besides, additional knowledge gaps are prominent due to the immature research stage discussing the integration of PSSs and the IoT from the perspective of quality dimensions. In this sense, the absence of exhaustive contributions in the literature analyzing the customer involvement aspects together with the different activities included in the SPSS phases (i. e. , BoL, MoL, and EoL) should be noted. Finally, the literature lacks methodologies relating customer involvement to the IoT adoption along with the different phases of SPSSs, evidencing the importance of customers in the value creation and development of both products and services.
Based on these gaps, the main research question was proposed, supported by three secondary ones: RQ: How can the impact of IoT technologies on perceived quality and customer involvement in the SPSS lifecycle be assessed? RQa: How can the perceived SPSS quality be defined and measured? RQb: How can IoT technologies be categorized in the context of SPSS models? RQc: How can IoT technologies influence customer involvement in the SPSS lifecycle? An integrated methodology was proposed and recursively tested in practice with 14 cases to answer these questions. The following section describes the research design, data collection, and analyses applied along with the research.
Data collection and analysis Once the methodology was structured, the first validation stage occurred with a pilot application case. To support the pilot case, participant observation and semi-structured interviews (Appendix A) were performed with the founder and CEO (i. e. , those who had a paramount view of the company vision and business) of the start-up La Comanda, an Italian technology-oriented company already providing an SPSS supported by the IoT to the market. The pilot case was used to further refine the research in terms of content and procedure for the subsequent application cases  (Ellram, 1996). The interview with the manager was over 90 min, and a research protocol was used  (Saunders et al. , 2009). The information acquired in the pilot case added with the grounded literature analysis allowed to enhance the methodology's structure.
The enhanced methodology was then tested and evaluated in five other primary application cases. Semi-structured interviews were conducted with companies' top managers through the interview protocol. The results of the application cases contributed to further refinement of the methodology to deduct the first preliminary results and evaluate its efficacy in practice. Finally, the last stage of recursive validation consisted of testing the validity of the refined version of the methodology with eight use cases. In this stage, data were collected using at least three different sources of  information, including the information available on the Internet and data gathered through direct interviews. The findings obtained after the three recursive validation cycles contributed to theoretical and managerial insights addressing the research questions.
IoT quality determinants matrix First, to define the impact of the IoT on quality determinants, it was essential to identify the elements whereby the IoT can be analyzed. Thus, the focus moved to the building blocks of the IoT technologies and their classification to understand their real meaning and functionality. Initially, the classification provided by  Al-Fuqaha et al. (2015)  was selected. They identified six elements that compose the IoT functionalities: (i) Identification is essential to name and match services with the tasks required. Typical identification methods include electronic product codes and ubiquitous codes; (ii) Sensing is related to obtaining data from objects within the system's network and, subsequently, sending them to a database, data warehouse, or cloud. Common IoT sensors include actuators, smart sensors, and wearable sensing, among others; (iii) Communication connects diversified objects to provide specific intelligent services.
The following step consisted of defining the main quality performance determinants according to which it is possible to analyze the perceived value that the customer can obtain by using the product and service in the SPSS analyzed. In this case, it is important to define criteria for assessing quality regardless of whether products or services are considered. During this process, 83 quality determinants were initially retrieved from the literature, and 11 were excluded since they were related to a specific sector and were not considered appropriate for evaluating the product or service. Hence, content analysis and a series of iterations were conducted to examine the overlaps and differences  (Nag et al. , 2007)  until the representative set of quality determinants was obtained. The following final list of determinants was obtained to analyze customers' perceived quality in the structure of the methodology: Reliability refers to the possibility that the SPSS will achieve its intended objective without interruption for a given period of time under the conditions specified by the customers  (Estrada and Romero, 2016).
Customer involvement matrix The customer involvement matrix (Fig. 4 ) was developed to understand how IoT technologies influence customer involvement in the SPSS lifecycle (RQc). The horizontal axis contemplates the SPSS lifecycle  (MSEE, 2014; Wiesner et al. , 2015) , which was also adopted in previous studies  (Sassanelli et al. , 2016) , composed by ideation, requirements, design, realization, delivery, support, and evolution. The vertical axis represents the degree of intensity of customer engagement  (Kimita et al. , 2016). Kimita et al. (2016)  argued that the intensity of customer involvement can be described according to three different levels: (i) X for customers, (ii) X with customers, and (iii) X by customers. The X factor in each of the three definitions represents a stage in SPSS development.
, 2016, p. 57). Kimita et al. (2016)  extended the concept of customer involvement proposed by  Kaulio (1998)  and adjusted it following the new needs of companies, increasing their focus on offering users a bundle of products and services. In this case, the customer involvement intensity has been evaluated considering the design phase and all the phases composing the SPSS lifecycle. Finally, the filling of this matrix must consider two options. Indeed, the involvement of the customer could contribute to the enrichment of either the first traditional SPSS lifecycle (the first loop, indicated in our proposed methodology with "1" in the single cells) or also its iterative loops (indicated with "2" in the single cells), giving data-based directions to the SPSS provider to improve the development phase of the second or more lifecycle iterations of the SPSS  (Sassanelli et al.
Pilot case: First cycle of empirical validation The first stage of the empirical validation process of the methodology was performed through an application case in an SPSS delivered by a technological and data-intensive start-up enterprise. The SPSS examined is based on a portable smart gadget enabled by the IoT that allows ordering a pizza through a one-click experience. It stems from the need of clients to order and receive pizza with agility. This solution is classified as a retail application and a product-oriented PSS  (Tukker, 2004). It is considered a product-related service representing the first of the eight archetypes of PSS models, a "home delivery" PSS offering type  (Gaiardelli et al. , 2014). The leading IoT elements identified in this SPSS were the following: identification, communication, computation, and services.
The IoT communication technologies cover a fundamental part  of the SPSS since the stand-alone object does not per se add value. The solution uses a stand-alone device (smart gadget) that communicates directly to a server through Wi-Fi. The reasoning behind such Wi-Fi implementation is that the customer uses the product at home. This implies that the end-users do not need to interact and be pushed by a traditional mobile app but only have to click a button on the smart gadget to shop the desired offer. This SPSS case was compared to traditional food delivery services not using IoT technologies. It has been observed that this SPSS allows customizations because the customers' orders can be customized by pressing a button on the smart gadget. Besides, the gadget has a display showing the wait and delivery times of the orders made.
The display proved to be helpful in creating a close relationship between customers and the company. The company communicates through it by showing promotional messages or offers from the pizza vendor at the right place at the right time instead of random ones received with a smartphone. In the SPSS, the computational IoT element is not directly added to the physical device; however, it was observed that a server supports intelligence and data management. Besides, the identification technologies applied in the gadget are represented by using the IPv4 or IPv6 protocol to address the product and identify it in the environment. The IoT quality determinants matrix results showed that usability is the first quality determinant affected by IoT technologies. Clients can use the device by clicking a button exploiting impulsive purchase behaviors.
The customer's feedback is considered when the company has to improve the existing product, add some functionalities, or use the customer support phase of the SPSS. The intensity of this type of involvement corresponds to adding some functionalities or the support phase. This involvement can be evaluated according to the matrix as an "X with" approach. As a result, the pilot case contributed to informing and improving some aspects of the IoT quality determinants matrix. It was decided to group the IoT elements based on their main functionalities by clustering technologies in a less segmented way, focusing on the key enabling IoT technologies according to  Xu et al. (2014)  in (i) identification and tracking technologies, (ii) communication technologies, (iii) network technologies, and (iv) service management.