dataesr/scientific-paragraphs-categorization huggingface dataset:
| Data collection Companies generally do not track problem-solving projects in a systematic manner and neither did the companies in our sample. We therefore collected survey data for hypotheses testing. Before data collection, five academics and four subsidiary managers assessed the face validity of our questionnaire. We undertook necessary amendments to ensure question clarity. We used three different questionnaires to collect information: in the first questionnaire, the project leader responsible for the problem solving reported on characteristics of the problem and problem solving (e. g. , the individuals involved), a senior manager in the subsidiary answered questions about the solution achieved in the second questionnaire. The outcome variables were thus always informed by a different manager than the information on the project (e.
A name generator question in the questionnaire to the project leader captured data on the individuals involved. A name generator is useful for studying social structures if it is impractical to generate a predetermined roster of all possible participants (e. g. ,  Mors, 2010). The name generator focused on supportive exchanges in the problem-solving project, that is, it featured behavioral linkages rather than affect-based recalls  (Bell, Belli-McQueen, & Haider, 2007). Thus, our approach leads to the inclusion of weak ties, countering the potential bias of recalling primarily strong ties. The phrasing of the name generator question included prompts to different ways of involvement, which helps to counter the effects of forgetting. Respondents could name up to 10 people and we asked how many people were important in addressing the problem if 10 people were named.
ACKNOWLEDGEMENTS We thank  Kyle Mayer  and the anonymous reviewers for very detailed and constructive comments. We are grateful to  Renate Kratochvil  for leading parts of the data collection. This article greatly benefited from discussions with and comments of  Tina Ambos ,  Niron Hashai ,  Thomas Keil ,  Marcus M. Larsen ,  Randi Lunnan ,  Felipe Monteiro , and  Torben Pedersen. We are thankful to seminar participants at  Aalto University ,  ESSEC ,  Copenhagen Business School, Nova SBE, Stockholm School of Economics, TU Munich, WU Vienna  as well as reviewers and participants of the  Academy of International Business Annual Meeting  for their valuable inputs. Consultants at McKinsey & Company provided important insights related to problem solving in multinational corporations. This study received funding from the  Irish Research Council , the  European Commission , the  Fundação para a Ciência e a Tecnologia  ( UIDP/00124/2020 ,  UIDB/ 00124/2020  and  Social Sciences DataLab ,  PINFRA/22209/2016 ),  POR Lisboa  and  POR Norte (Social Sciences DataLab ,  PINFRA/22209/2016 ).
Acknowledgments The authors are grateful for the intellectual support received from the  Santa Fe Institute , in particular during the Complex Interactive Summer School in June 2021. Discussions with SFI faculty proved extremely helpful in setting up and designing this project. Comments from two anonymous referees are gratefully acknowledged. Contributions to an earlier draft on the discussion of resilience in health care by  Osondu Ogbuoji  ( Duke Center for Policy Impact in Global Health ) are gratefully acknowledged.
Sustainable agrifood global value chains depend on chain governance by the lead firm and transaction governance. However, the link between them is still unclear. We therefore investigated the scientific field on "governance in agrifood global value chain" over 15 years in the Scopus and Web of Science databases through two analyses: a descriptive bibliometric and a keywords co-occurrence analysis. Our descriptive results show that the research on the theme has increased over the years, with a concentration of the papers published in countries such as the United States, the United Kingdom and the Netherlands, with emphasis on Wageningen University. The network graph showed a multidisciplinary theoreticomplcal field and four axes: chain governance; transaction governance; horizontal relationships; political and structural elements.
In that sense, a bibliometric analysis can turn into an efficient tool to support the to know the state of the art  (Wenningkamp & Schmidt, 2016; Donthu et al. , 2021; Malanski et al. , 2021). For this reason, our aim is to investigate the scientific field on "governance in agrifood global value chain", and research gaps in it through a survey on the production of knowledge between the years of 2005 to 2019. We aimed to answer the question: How research studies on "governance in agrifood global value chain" have been characterized for the past 15 years? Grounded on  Malanski et al. (2019; 2021; 2022) , the purpose of this analysis was to survey the scientific production indexed in the two most important international databases, Web of Science and Scopus. We particularly focused on the agrifood sector given that chains involving international contexts include, among others, transactions of products from this sector  (Trienekens, 2011).
Methodological Procedures This research aimed to understand the scientific production indexed in the two most important international databases, Web of Science and Scopus on "governance in agrifood global value chain". Through bibliometric research, we developed a qualitative and descriptive analysis. The analysis was performed in three steps according to the PRISMA guideline (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), which establishes main criteria for the preparation of systematic reviews and meta-analysis  (Moher et al. , 2009; 2015)  (Figure  1 ). The first step intended to identify the articles on the subject "governance in agrifood global value chain" in the last 15 years (2005 to 2019) in Web of Science and Scopus databases to compose our database. The Agrovoc Thesaurus from United Nations Food and Agriculture Organization (FAO) (Agrovoc Multilingual Thesaurus, 2020) was used to identify the keywords though the scientific standardized vocabulary related to 'governance' and 'global value chain' 2  , which were the two main terms of our study.
Duplicates were removed in the second screening. Next, a third screening was performed by reading the title, summary, and keywords of the studies related to the agrifood sector. Finally, 477 articles discussing "governance in agrifood global value chain" were select to build the database, which was composed by articles' meta-data: authors, journal, country, times cited, keywords. Seminal works, even if conceptual or proposed in sectors other than agriculture, were kept, given that they are the foundation for the construction of research on the topic. Figure  1  synthesizes the methodological path. In the third step, two analyses were performed based on the 477 articles identified: 1) descriptive analysis of the main meta-data of the articles based on the frequency of the following variables: authors, affiliation (university and country), year of publication, number of citations, and journal; 2) network analysis based on keywords used by the authors of the article, showing the main areas of scientific research in "governance in agrifood global value chain" for the past 15 years.
Most cited articles The most cited articles, in both databases, were in large part from authors who have most published in the area, such as Gereffi, G. , Ponte, S. , Pietrobelli, C. , and Lee, J. It is also noteworthy that these articles were related to main journals, countries, and universities that published on the topic (Table  5 ). This shows that studies were largely theoretical, with concerns for scientific evolution when talking about global value chain. That is why part of them was frequently cited. They are derived from seminars and are not related to the agrifood context. In addition, although we could note an increase in the number of research studies since 2012, the most cited works are dated from 2005 to 2014. This could mean the consolidation on studies about this subject.
For this reason, considering governance from these other perspectives is important, as it makes it possible to analyze elements beyond efficiency, such as the impacts on development. Public and private governance mechanisms: including the public and private governance mechanisms of chain coordination can contribute to the identification and delimitation of public policies in the different high value chains. Public policies that emphasize social and environmental aspects for chain governance, including CSR, labor, etc. Finally, as a way to overcome limitations faced by this work, we suggest searches in databases with indexed articles from Latin American sources. Although databases such as Web of Science and Scopus bring consolidated results, these databases include mostly international journals from developed countries in the northern hemisphere.
Acknowledgments We would like to thank the  Coordination for the Improvement of Higher Education Personnel (CAPES)  through doctoral scholarship funding, as well as sandwich doctorate, the support of the  Engineering School (École d'Ingénieurs de Purpan) in Toulouse, France , and also the  University without Borders Program of the General Superintendence of Science, Technology and Higher Education (USF/ SETI ) - Extension  project  TC 152/2018.
Benchmark Input data The proposed MILP is solved using PULP 2. 4. 1. The MARL approach is coded from scratch in python 3. 9 and runs on a PC with a Core ™ i7-11850 H 2. 5 GHz CPU with 32GB of RAM. The computation times are measured in CPU seconds. In this section, a FFL benchmark problem is conducted to explore the proposed model and the MARL method. As shown in Fig. 3 , the FFL production system consists of three processes where the first two processes have a single machine and the third one has three non-identical parallel machines. To apply the proposed approach, one process agent is required for the third process. However, process 1 and process 2 are modeled with their corresponding machines agents. On-site PV panels, ESS and the conventional grid meet the electrical demand of the studied FFL.
Study design and sampling method A national online survey was carried out from October 2021 to November 2021 during the peak of the fourth COVID-19 wave to assess the impacts of COVID-19 on various aspects of life among Vietnamese people. Link to the questionnaire created by the online platform -Survey monkey to target subjects across different regions of Vietnam (surveymonkey. com). This survey was conducted in three groups: 1) health professionals; 2) medical students; 3) general population. For health professionals group, we focused on three topics of interest, including a) work motivation and retention; b) quality of life; c) burn out and mental well-being. After answering general social demographic questions, each respondent was randomly assigned to one of these three topics, generating three separate data sets.
In this target group, we included participants who 1) aged 20 or older; 2) agreed to participate in this study by providing online informed consent; 3) worked at medical facilities in Vietnam in the last six months; 4) could access the survey link and complete the questionnaire. The snowball sampling method was applied to collect and speed up the data collection process. At the end of the data collection duration, a total of 2814 health care professionals worked at medical facilities from 61 / 63 provinces in Vietnam. The substudy of work motivation among Vietnamese health care workers included 939 complete records.
After the questionnaire had been edited and launched, the above test data will be removed. In the next stage, a questionnaire link was sent to a small group of 30 medical staff. In particular, we selected 10 health professionals from each region of Vietnam (Northern, Central, and Southern) who worked in different types of health facilities (general hospitals, specialized hospitals, central clinics, and commune health stations) as the core facilitator group. Participants took 30 minutes to complete the questionnaire. Respondents were then invited their acquaintances and colleagues to participate in the survey. Participants were informed of the benefits and risks of participating in this study. Data are monitored and tracked through Survey Monkey's system. The data of the study was confidential and was used for research purposes only.
Second, as the study was carried out during the November peak of COVID-19, health care workers were generally overloaded and many could not participate in the online survey. Our study, therefore, could not be representative of the whole frontline workforce. Lastly, data collected through self-reporting may be subject to personal bias. Nonetheless, this study is among the largest efforts to measure work motivation during COVID-19 and is able to identify existing problems within the health system as well as signify important implications for policy adjustments.
Acknowledgments: Bach Tran is the principal investigator (PI) of the research project which is managed by the  Youth Research Institute  as a ministerial-level research project implemented by the  Vietnam Young Physician Association. The project was supported by  Action Aid Vietnam ,  Gia Lam Urban Development And Investment Company Limited, Vingroup , and supported by  Vingroup Innovation Foundation (VINIF)  (Grant No. VINIF. 2020. COVID-19. DA03 );  NUS Department of Psychological Medicine  ( R-177-000-100-001/R-177-000-003-001 ); and  NUS iHeathtech Other Operating Expenses  ( R-722-000-004-731 ). The authors would like to acknowledge  ActionAid Vietnam ,  Youth Research Institute , and  Vietnam Young Physician Association  for financial support. Doan Phuong Linh  is an iMPH student at  Hanoi Medical University  working in this project and presenting this work at the  Science and Technology Conferences of medical and pharmacy universities.
Funding: This study is supported by  Actionaid Vietnam, Vingroup Innovation Foundation (VINIF)  (Grant No. VINIF. 2020. COVID-19. DA03 ). Publication fee for the manuscript was funded by the  NUS Department of Psychological Medicine  ( R-177-000-100-001/R-177-000-003-001 ); and  NUS iHeathtech Other Operating Expenses  ( R-722-000-004-731 ).
TMT members and CEOs were invited to reply via an email that included an introductory letter and a link to a confidential survey. A feedback report was sent to each team with the main results of the research. Survey data were collected from mid-January to mid-April 2019. The questionnaire was replied by top managers of twenty public hospitals within the Valencian Community in Spain. This region has a total number of 22 public hospitals, but one hospital chose not to participate, and another was in the process of becoming part of a private institution. Each hospital comprised a TMT with an average size of 6. 15 employees (SD = 2. 13; range 2-10). In total, 123 TMT members out of 154 replied to the questionnaire. The response rate was about 80%, which is well above the 10-12% rate common for surveys targeting top executives  (Hambrick and Cannella, 1993; Simsek et al.
Acknowledgements Authors acknowledge the financial support of the  Spanish Ministerio de Innovación, Ciencia y Universidades  ( PGC2018-097981-B-I00 ),  Ministerio de Ciencia e Innovación  ( PID2021-127882NA-I00  and  PID2021-124474OA-I00  financiados por  MCIN /AEI /10. 13039/501100011033/FEDER , UE. ) and  Conselleria de Innovación, Universidades ,  Ciencia y Sociedad Digital  ( CIAICO/2021/ 260  and  CIGE/2021/095 ).
The sources of algorithmic bias in marketing offerings are often embedded in poor training datasets, weak mathematical models, or historical and social contexts. For example, Google's ad targeting to specific business groups based on particular gender profiles  (Simonite, 2015) , Facebook's gender-specific ad targeting (The Wall Street Journal, 2021), Apple's biased credit card offerings to businesses  (Akter et al. , 2022)  or in other areas of businesses ranging from healthcare to banking  (Cao, Duan, Edwards, & Dwivedi, 2021; Coombs et al. , 2021; Dalenberg, 2018; Duan, Edwards, & Dwivedi, 2019; Israeli & Ascazra, 2020; Kumar, Dwivedi, & Anand, 2021; Kumar, Sharma, & Dutot, 2023; Lambrecht & Tucker, 2018; Sun, Nasraoui, & Shafto, 2020; Stahl, 2022; Vigdor, 2019). In the context of the Robodebt scheme in Australia, AI-driven service systems wrongfully raised almost $750 million through biased decision-making algorithms  (Akter, Dwivedi, et al.
, 2021). Social and historical biases often disadvantage marketing decision-making either due to incomplete datasets or unreliable models, or poor deployment  (Akter et al. , 2022). The customer equity of advertisements on the Facebook platform has been questioned as customers with African-American backgrounds could not view targeted ads on housing, credit, and employment  (Angwin, Tobin, & Varner, 2017 ). An unrepresentative training dataset, weak model design, or prejudiced deployment results in unfair customer equity in terms of value, brand, or relationship  (Hartmann, Heitmann, Schamp, & Netzer, 2021; Israeli & Ascazra, 2020). Despite the unequal, unjust, and unfair effects of algorithm biases on customer equity, research in this stream is scarce in industrial marketing. Drawing on the dynamic capability view  (Helfat & Martin, 2015; Helfat & Peteraf, 2003; Martin, 2019; Teece, 2007) , this study explores how to integrate algorithms effectively within marketing decisionmaking that adapts to the changing business environment.
, 2021). Table  1  synthesizes the sources of these biases either through spurious datasets or, unreliable models or, or deep-rooted societal biases in marketing offerings. For example, the extant literature shows a discriminatory placement of online advertisements on gender-specific pages  (Israeli & Ascazra, 2020; Lambrecht & Tucker, 2018) , discriminatory pricing practices  (Dalenberg, 2018; Vigdor, 2019)  or, unjust offerings based on postcode/locations (USA Today, 2020). In this context, algorithmic bias management capability indicates how to manage deviation from the standards in AI-based marketing models that can stem from training datasets, types of models or market applications  (Danks & London, 2017). Despite the prevalence of unfair, unjust and unequal effects of AI-driven marketing models and their corresponding algorithmic biases, research in this emerging domain is still fragmented and anecdotal.
Qualitative exploration Following the guidelines of  Tranfield et al. (2003)  and  Watson, Wilson, Smart, and Macdonald (2018) , the study has conducted a systematic literature review to plan the search protocols, identify the screening rules and develop the themes to address our research quest of algorithmic bias management capabilities that influence customer equity in marketing. A thorough review of the key databases, such as ABI/ Inform Collection (ProQuest), Emerald Insight, ScienceDirect, Business Source Complete (EBSCO) and Wall Street Journal, was conducted using the following search strings: "artificial intelligence", "bias" and "marketing", "artificial intelligence in marketing", "algorithmic bias in marketing", "bias in artificial intelligence", "machine learning in marketing", "deep learning in marketing", "dark side of AI in marketing" etc.
In addition to all other database, the Wall Street Journal was included as our research context is the financial industry and this business and economic-focused international daily newspaper has reported a significant number of news articles in recent years on the bright and dark side of AI applications in this context. The overall process has resulted in 45 studies after a careful review following the protocol in Figure  1. The exclusion of articles throughout the process was based on relevance, quality, and duplication criteria. Whereas relevance refers to the degree the articles were aligned with the research question on the dimension of algorithmic bias management capabilities, quality refers to the studies that offer depth, rigor and some novel insights beyond a recitation of past findings  (Palmatier, Houston, & Hulland, 2018; Snyder, 2019).
We excluded papers that are not directly linked to our research topic, such as physics, chemistry, geology and biology. As such, the criteria used to select each paper contained an explicit or implicit indication of algorithmic bias management capabilities in broader business decisionmaking. Applying QSR NVivo 12 software and following the guidelines of thematic analysis  (Braun & Clarke, 2006) , the study identifies three major dimensions (data, model, and deployment) and nine subdimensions in algorithmic bias management capabilities (see Figure  2 ). A panel of 5 experts consisting of two academics and three analytics professionals analyzed and scored the subdimensions and each primary dimension by applying the Q-soring method. We estimated an inter-rater reliability score of 0. 82, exceeding the cut-off value of 0.
Conceptual model and hypotheses development Building on the findings of the literature review and theoretical underpinnings of dynamic capabilities (DC), this study proposes the conceptual model (Figure  2 ) to extend algorithmic bias research in marketing. We define data bias management capability (DABMC) as the dynamic capability of analytics practitioners to manage the characteristics of datasets ensuring completeness, format, and accuracy in a dynamic environment  (Gebru, Morgenstern, Vecchione, Vaughan, & Wallach, 2020). Drawing on data quality literature (e. g. , Fosso Wamba,  Akter, & De Bourmont, 2019; Nelson, Todd, & Wixom, 2005) , completeness of the training dataset refers to the extent to which all possible attributes pertinent to the target population are reflected. Whereas currency represents the degree to which the dataset is up to date, format refers to the extent datasets are well integrated and presented in a way that is understandable and interpretable.
Since training datasets are the primary source of algorithmic bias  (Akter et al. , 2022; Israeli & Ascazra, 2020) , the inability to train data management capability in terms of completeness, currency and format results in sample selection bias. For example, Apple's credit card algorithms unfairly rejected female applicants over males since the dataset represents a higher ratio of male applicants. Similarly, model bias management capability (MOBMC) refers to the dynamic ability of analytics practitioners to manage methodological and procedural guidelines concerning model reliability, flexibility, and ambidexterity that influence the design and development of marketing models  (Walsh et al. , 2020). Model bias occurs due to incorrect specification of the AI models or improper methodological choices used in algorithmic decision-making  (Akter et al.
, 2020). The datasets occasionally contain various mistakes or flaws, including repeated entries, inaccurate data formats, and incomplete data or fields  (Akter et al. , 2022) , which make it challenging for the algorithms to analyze them. Reportedly, incomplete data has a negative effect on how well machine learning models function  (Slaughter, Kopec, & Batal, 2020). As such, if an algorithm for machine learning is employed to be trained from substandard inputs, the resulting model may also be incomplete and faulty  (Grote & Keeling, 2022). Subsequently, such an incomplete model may exclude a specific group of people, which can also lead to incorrect forecasts for particular communities  (Gianfrancesco, Tamang, Yazdany, & Schmajuk, 2018). For example, Amazon developed a machine learning algorithm to recruit potential candidates, which favored male candidates over female candidates.