dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2022; Janssen, Brous, Estevez, Barbosa, & Janowski, 2020) , accuracy  (Gudivada, Apon, & Ding, 2017; Sengupta, Garg, Choudhury, & Aggarwal, 2018)  and completeness  (Rozado, 2020; Salvato et al. , 2018; Slaughter et al. , 2020)  of data as capabilities to manage data bias. As such, introducing the feature selection technique  (Sun et al. , 2020)  and precise labeling as well as adopting random sampling in data selection can be an example of a dynamic capability to create a balanced training dataset which in turn helps in producing reliable and flexible marketing models  (Zhang & Qu, 2019). For example, gender-specific interpretations were made available by Google Translate in 2018. While converting questions that are gender-neutral in the original language, this functionality gives users a choice between male and female sound versions  (Castaneda et al.
, 2022). Similarly, IBM unveiled AI Fairness 360 in 2018. With this extendable free software toolbox, one may investigate, monitor, and reduce prejudices and biases in machine learning algorithms across AI applications  (Thompson, 2021). Therefore, based on the abovementioned discussion, we posit the following hypothesis focusing on an individual analytics practitioner. H1. Perception of data bias management capability has a significant positive impact on model bias management capability. Any data bias added to machine learning can result in significant deployment bias  (Parikh et al. , 2019). Deployment bias takes place when algorithm designers unintentionally use or interpret the analytical and artificial intelligence (AI) systems in inappropriate ways. As a dynamic capability in data science, deployment starts as soon as the ML algorithmic system is brought into action as part of a business project  (Davenport & Malone, 2021).
For example,  Vigdor (2019)  asserted that despite being engineered to be unbiased to the fact, the Apple Credit system gave males better credit levels as compared to females. Additionally, the extant cultural and societal biases embedded in the data sources can worsen the situation for previously marginalized groups from particular races, socioeconomic backgrounds, faiths, genders, and age groups. Based on findings from MIT research, three facial recognition software which was commercially deployed to the market failed to provide accurate identification for darker-skinned female  (Hardesty, 2018)  as the training datasets were estimated to be mostly male and white. The case of Amazon can be stated as another example of deployment bias caused by data bias. In order to improve their working operations and productivity, the company determined whether a specific postal address had enough paid subscribers, the presence of neighboring warehouses, and the number of qualified personnel capable of delivering to those locations (O'Donnellan, 2020).
Similarly, banking chatbot service is being employed in the financial sector to provide customers with financial advice on how to manage and invest their money, helping them in making wise financial decisions  (Okuda & Shoda, 2018). In an effort to increase consumers' trust and confidence in AI-based services, IBM released AI Fairness 360, a complete open-source toolbox for assessing and mitigating unintentional biases in datasets and machine learning models. Overall, deploying robust and bias-free ML models would enable marketers to make sure that the products and services remain relevant during every touch point throughout the customer interactions while applying responsible and ethical AI would deliver the speed and scalability necessary to manage thousands of customer engagements in realtime  (Akter et al.
Scale development The study has adapted scales from past studies (see Table  2 ) to measure data-bias management capability  (Nelson et al. , 2005) , model bias management capability  (Wixom & Todd, 2005) , and deployment bias management capability  (Akter, D'Ambra, & Ray, 2011). The study has also measured customer equity as the outcome constructs using value equity, brand equity, and relationship equity subdimensions  (Ou et al. , 2017; Rust et al. , 2004). We measured all the constructs from the firm's perspective except for customer equity. The customer equity construct was measured using cross-sectional survey data from customers of the bank who have used AI-powered solutions for the last three years at least. The pre-testing phase collected data from 25 respondents to check the structure and format of the questionnaire.
Main study We used two sources of cross-sectional survey data: analytics practitioners (marketing managers, CRM managers, data analysts, IT professionals, machine learning experts, etc. ) who are part of the algorithmic bias management team as well as the actual customers of the bank who received AI-powered service solutions. Using a professional market research firm, we approached a panel of 781 respondents in the bank who met the screening criteria of at least three years' analytics/ algorithmic decision-making experience and 18+ years old. 233 respondents filled out the complete survey, and after excluding spurious responses, we finally analysed 200 responses from analytics practitioners in the bank. The spurious responses refer to straight-lining responses, missing values, quick response time, and abnormal response patterns (e.
Following the guidelines of  Hulland, Baumgartner, and Smith (2018) , we applied a priori and post-hoc methods to address common method variance (CMV) issues. As part of the priori method, we separated the three algorithmic bias management capability constructs from the customer equity construct as data were collected from two different sample units (analytics practitioners vs. actual customers). As part of the post-hoc method, we collected data using theoretically unrelated variables as marker variables (e. g. , I have never heard of blockchain technology)  (Simmering, Fuller, Richardson, Ocal, & Atinc, 2015). The findings of the correlation coefficients show a non-significant relationship (r = 0. 063 -0. 071, p>0. 05) between marker variables and three antecedents (data bias, model bias and deployment bias management capabilities).
Lambrecht and Tucker Empirical While examining how an algorithm- (2018) powered advertisement promotes job opportunities in the discipline of Science, Technology, Engineering and Math (STEM), the research finds that an algorithm solely based on cost-optimization in ad delivery creates discrimination in terms of targeting candidates based on gender. Instead, to be gender- neutral, the advertisement reached more men than women. Parikh, Teeple, and Conceptual Although AI itself impetuously Navathe (2019) contributes to bias, the authors suggest heedful use of AI technologies, like, the application of AI decision support tools, unified collection of the diversified dataset, and appropriate algorithm prediction, can mitigate the risk of biases. Ntoutsi et al. (2020) ConceptualIn addition to the technical solutions like generating a balanced dataset, refining classification models, and modifying the regression model's predictions; the authors additionally.
Stage 3 The article therefore provides rich contributions to the early process stages, while the paper at hand contributes mainly to the later stages of the SLR process How to conduct systematic literature reviews in management… published in a special issue editorial, its presentation and elaboration are somewhat limited by the already mentioned word count limit. Nevertheless, the SPAR-4-SLR protocol provides a very useful checklist for researchers that enables them to include all data required to document the SLR and to avoid confusion from editors, reviewers, and readers regarding SLR characteristics. Beyond Table  1 ,  Durach et al. (2017)  synthesized six common SLR "steps" that differ only marginally in the delimitation of one step to another from the sub-stages of the previously mentioned SLR processes.
Decisions to be made Step 1: Defining the research question Preparing the research by identifying a relevant gap in the literature Justifying the need to map a certain field of research Theoretical grounding of the SLR (1) Specifying the research gap and related research question(s) (2) Opting for a theoretical approach (e. g. , inductive, abductive, or deductive (3) Defining the core theoretical framework and constructs and, in the case of a deductive approach, defining the coding scheme Step 2: Determining the required characteristics of primary studies Define criteria for the inclusion and exclusion of literature to be reviewed (4) Specifying inclusion and exclusion criteria Step 3: Retrieving a sample of potentially relevant literature Collecting literature to be analyzed (5) Defining sources and databases (6) Defining search terms and crafting a search string Step 4: Selecting the pertinent literature Selection of relevant literature against the inclusion and exclusion criteria defined in step 2 (7) Including and excluding literature for detailed analysis and synthesis Step How to conduct systematic literature reviews in management…    this results in multiple guidelines that all lack the space to go into detail, while only a few guidelines focus on filling a gap in the process.
Here, it would be relevant to specify what kind of analysis is aimed for the SLR, where three approaches might be distinguished (i. e. , bibliometric analysis, metaanalysis, and content analysis-based studies). Briefly distinguishing them, the core difference would be how many papers can be analyzed employing the respective method. Bibliometric analysis  (Donthu et al. 2021 ) usually relies on the use of software, such as Biblioshiny, allowing the creation of figures on citations and co-citations. These figures enable the interpretation of large datasets in which several hundred papers can be analyzed in an automated manner. This allows for distinguishing among different research clusters, thereby following a more inductive approach. This would be contrasted by meta-analysis (e. g. ,  Leuschner et al.
Here, it is essential to ensure the exclusion of poor-quality evidence that would lower the quality of the review findings  (Mulrow 1987; Tranfield et al. 2003). This could be ensured by, for example, only taking papers from journals listed on the Web of Science or Scopus or journals listed in quartile 1 of Scimago (https:// www. scima gojr. com/), a database providing citation and reference data for journals. The selection of relevant publication years should again follow the purpose of the study defined in Step 1. As such, there might be a justified interest in the wide coverage of publication years if a historical perspective is taken. Alternatively, more contemporary developments or the analysis of very recent issues can justify the selection of very few years of publication (e. g. ,  Kraus et al.
Step 3: retrieving a sample of potentially relevant literature After defining the required characteristics of the literature to be reviewed, the literature needs to be retrieved based on two decisions. Decision 5 concerns suitable 1 3 How to conduct systematic literature reviews in management… literature sources and databases that need to be defined. Turning to Web of Science or Scopus would be two typical options found in many of the examples mentioned already (see also detailed guidance by  Paul and Criado (2020)  as well as  Paul et al. (2021) ). These databases aggregate many management journals, and a typical argument for turning to the Web of Science database is the inclusion of impact factors, as they indicate a certain minimum quality of the journal  (Sauer and Seuring 2017). Additionally, Google Scholar is increasingly mentioned as a usable search engine, often providing higher numbers of search results than the mentioned databases (e.
g. , Pearce 2018). These results often entail duplicates of articles from multiple sources or versions of the same article, as well as articles in predatory journals  (Paul et al. 2021). Therefore, we concur with  Paul et al. (2021)  who underline the quality assurance mechanisms in Web of Science and Scopus, making them preferred databases for the literature search. From a practical perspective, it needs to be mentioned that SLRs in management mainly rely on databases that are not free to use. Against this limitation, Pearce (2018) provided a list of 20 search engines that are free of charge and elaborated on their advantages and disadvantages. Due to the individual limitations of the databases, it is advisable to use a combination of them  (Kraus et al. 2020 (Kraus et al. , 2022) )  and build a consolidated sample by screening the papers found for duplicates, as regularly done in SLRs.
scima gojr. com, last accessed 13. of April 2023) and restricting it to journals in the first quartile (e. g. ,  Yavaprabhas et al. 2022). Other papers (e. g. ,  Kraus et al. 2021; Rojas-Córdova et al. 2022)  use certain journal quality lists to limit their sample. However, we argue for a careful check by the authors against the topic reviewed regarding what would be included and excluded. Decision 6 entails the definition of search terms and a search string to be applied in the database just chosen. The search terms should reflect the aims of the review and the exclusion criteria that might be derived from the unit of analysis and the theoretical framework  (Durach et al. 2017; Snyder 2019). Overall, two approaches to keywords can be observed. First, some guides suggest using synonyms of the key terms of interest (e.
However, this might result in very high numbers of publications found and therefore requires a purposeful combination with other search criteria, such as specific journals  (Kraus et al. 2021; Lim et al. 2021) , publication dates, article types, research methods, or the combination with keywords covering domains to which the search is aimed to be specified. Since SLRs are often required to move into detail or review the intersections of relevant fields, we recommend building groups of keywords (single terms or multiple synonyms) for each field to be connected that are coupled via Boolean operators. To determine when a point of saturation for a keyword group is reached, one could monitor the increase in papers found in a database when adding another synonym. Once the increase is significantly decreasing or even zeroing, saturation is reached  (Sauer and Seuring 2017).
Beyond that, we would strongly recommend using data analysis software, such as MAXQDA or NVivo. Such programs enable the import of literature in PDF format and the automatic or manual coding of text passages, their comparison, and tabulation. Moreover, there is a permanent and editable reference of the coded text to a code. This enables a very quick compilation of content summaries or statistics for single codes and the identification of qualitative and quantitative links between codes and papers. All the mentioned data extraction or data processing tools require a license and therefore are not free of cost. While many researchers may benefit from national or institutional subscriptions to these services, others may not. As a potential alternative,  Pearce (2018)  proposed a set of free open-source software (FOSS), including an elaboration on how they can be combined to perform an SLR.
He also highlighted that both free and proprietary solutions have advantages and disadvantages that are worthwhile for those who do not have the required tools provided by their employers or other institutions they are members of. The same may apply to the literature databases used for the literature acquisition in Decision 5  (Pearce 2018). Moreover, there is a link to Step 1, Decision 3, where bibliometric reviews and meta-analyses were mentioned. These methods, which are alternatives to content analysis-based approaches, have specific demands, so specific tools would be appropriate, such as the Biblioshiny software or VOSviewer. As we will point out for all decisions, there is a high degree of interdependence among the steps and decisions made. Decision 9 looks at conducting the data analysis, such as coding against (predefined) constructs, in SLRs that rely, in most cases, on content analysis.
Seuring and Gold (2012)  elaborated in detail on its characteristics and application in SLRs. As this paper also explains the process of qualitative content analysis in detail, repetition is avoided here, but a summary is offered. Since different ways exist to conduct a content analysis, it is even more important to explain and justify, for example, the choice of an inductive or deductive approach (see Decision 2). In several cases, analytic variables are applied on the go, so there is no theory-based introduction of related constructs. However, to ensure the validity and replicability of the review (see Decision 11), it is necessary to explicitly define all the variables and codes used to analyze and synthesize the reviewed material  (Durach et al. 2017; Seuring and Gold 2012). To build a valid framework as the SLR outcome, it is vital to ensure that the constructs used for the data analysis are sufficiently defined, mutually exclusive, and comprehensively exhaustive.
For meta-analysis, the predefined constructs and items would demand quantitative coding so that the resulting data could be analyzed using statistical software tools such as SPSS or R (e. g. ,  Xiao and Watson 2019). Pointing to bibliometric analysis again, the respective software would be used for data analysis, yielding different figures and paper clusters, which would then require interpretation (e. g. ,  Donthu et al. 2021; Xiao and Watson 2019). Decision 10, on conducting subsequent statistical analysis, considers follow-up analysis of the coding results. Again, this is linked to the chosen SLR method, and a bibliographic analysis will require a different statistical analysis than a content analysis-based SLR (e. g. ,  Lim et al. 2022; Xiao and Watson 2019). Beyond the use of content analysis and the qualitative interpretation of its results, applying contingency analysis offers the opportunity to quantitatively assess the links among constructs and items.
Consequently, our discipline has come a long way in crafting search strings, inclusion, and exclusion criteria, and elaborating on the validity and reliability of an SLR. Nevertheless, we left critical areas underdeveloped, such as the identification of relevant research gaps and questions, data extraction tools, analysis of the findings, and a meaningful and interesting reporting of the results. Our study contributes to filling these gaps by providing operationalized guidance to SLR authors, especially early-stage researchers who craft SLRs at the outset of their research journeys. At the same time, we need to underline that our paper is, of course, not the only useful reference for SLR authors. Instead, the readers are invited to find more guidance on the many aspects to consider in an SLR in the references we provide within the single decisions, as well as in Tables  1  and 2.
2003; Xiao and Watson 2019). While this insight might suggest merging Steps 2 and 3, we refrain from this superficial change and building yet another SLR process model. Instead, we prefer to add detail and depth to  Durach et al. 's (2017)  model. (Decisions: D1: specifying the research gap and related research question, D2: opting for a theoretical approach, D3: defining the core theoretical framework and constructs, D4: specifying inclusion and exclusion criteria, D5: defining sources and databases, D6: defining search terms and crafting a search string, D7: including and excluding literature for detailed analysis and synthesis, D8: selecting data extraction tool(s), D9: coding against (pre-defined) constructs, D10: conducting a subsequent (statistical) analysis (optional), D11: ensuring validity and reliability, D12: deciding on the structure of the paper, D13: presenting a refined theoretical framework and discussing its contents, and D14: deriving an appropriate journal from the analyzed papers).
This is also done through the third refinement, which underlines which previous or later decisions need to be considered within each single decision. Such a consideration moves beyond the mere temporal sequence of steps and decisions that does not reflect the full complexity of the SLR process. Instead, its focus is on the need to align, for example, the conduct of the data analysis (Decision 9) with the theoretical approach (Decision 2) and consequently ensure that the chosen theoretical framework and the constructs (Decision 3) are sufficiently defined for the data analysis (i. e. , mutually exclusive and comprehensively exhaustive). The mentioned interrelations are displayed in Fig. 2  by means of directed arrows from one decision to another. The underlying explanations can be found in the earlier paper sections by searching for the individual decisions in the text on the impacted decisions.
Others are researchers who conduct studies to demonstrate the effectiveness, i. e. , the good concordance of telediagnosis with traditional face-toface consultations  (Duong et al. , 2013 (Duong et al. , , 2015;; Thonnelier et al. , 2015; Hirsch et al. , 2018a,b; Bataille et al. , 2019 Bataille et al. , , 2021;; Friedel et al. , 2019; Ridard et al. , 2020; Skayem et al. , 2020; Démoulins et al. , 2021). Finally, a broader ethnographic approach completes this corpus, with observations made during several working meetings and professional conferences organized by scientific societies on telemedicine, as well as by various data and documents (review of the scientific literature produced by the participants, statistical databases elaborated by public bodies on medical demography, gray literature produced by professional organizations, press review of news on teledermatology, etc.
We note this as a clear research gap. Therefore, we propose our second research question as follows. RQ2. What are the impacts of government policy effectiveness on the relationship between digital capabilities and supply chain resilience? We have gathered data: qualitative and quantitative to answer RQ1 and RQ2 from Indian Cement manufacturing firms. To support the theoretical framework, our research hypotheses informed by the dynamic capability view (DCV)  (Teece et al. , 1997; Schilke, 2014). Our study builds on previous literature concerning digital adaptability, digital agility, supply chain resilience, and government effectiveness. Hence, we advance theoretical understanding at the intersection of operations management with information management  (Kumar et al. , 2018). Our data shows that digital agility is often regarded as useful for uncertain environments  (Warner and Wäger, 2019; Grover and Sabherwal, 2020; Grover, 2022).
Survey We surveyed between September 2021-February 2022, in Indian cement manufacturing organizations that rely on the use of digital technologies capabilities to make decisions related to their supply chain activities. The samples were drawn from the database of the CMA (known as the Cement Manufacturers Association of India), the leading trade association body of the cement industry in India  (Luo et al. , 2017). The authors distributed the questionnaires to senior-level managers (regional head/vice president/general manager) of 450 cement manufacturing units and ready-mixed concrete plants of 80 organizations, in India. We selected these 450 units based on the input received from the spokesperson of the CMA regarding them having a relatively high level of use of digital technologies. We also selected our organizations in a way that represents a wide range of geographical and cultural diversity.