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Despite the variety of supply chain management (SCM) research, little attention has been given to the use of Big Data Analytics for increased information exploitation in a supply chain. The purpose of this paper is to contribute to theory development in SCM by investigating the potential impacts of Big Data Analytics on information usage in a corporate and supply chain context. As it is imperative for companies in the supply chain to have access to up-to-date, accurate, and meaningful information, the exploratory research will provide insights into the opportunities and challenges emerging from the adoption of Big Data Analytics in SCM.

Although Big Data Analytics is gaining increasing attention in management, empirical research on the topic is still scarce. Due to the limited availability of comparable material at the intersection of Big Data Analytics and SCM, the authors apply the Delphi research technique.

Portraying the emerging transition trend from a digital business environment, the presented Delphi study findings contribute to extant knowledge by identifying 43 opportunities and challenges linked to the emergence of Big Data Analytics from a corporate and supply chain perspective.

These constructs equip the research community with a first collection of aspects, which could provide the basis to tailor further research at the nexus of Big Data Analytics and SCM.

The research adds to the existing knowledge base as no empirical research has been presented so far specifically assessing opportunities and challenges on corporate and supply chain level with a special focus on the implications imposed through Big Data Analytics.

Media users daily exchange personal data for “free” personalised media. Is this a fair trade, or user “exploitation”? Do personalisation benefits outweigh privacy risks?

This study surveyed experts in three consecutive online rounds (e-Delphi). The authors explored personal data processing value for media, personalisation relevance, benefits and risks for users. The authors scrutinised the value-exchange between media and users and determined whether media communicate transparently, or use “dark patterns” to obtain more personal data.

Communication to users must be clear, correct and concise (prevent user deception). Experts disagree on “payment” with personal data for “free” personalised media. This study discerned obstacles and solutions to substantially balance the interests of media and users (fair value exchange). Personal data processing must be transparent, profitable to media and users. Media can agree “sector-wide” on personalisation transparency. Fair, secure and transparent information disclosure to media is possible through shared responsibility and effort.

This study’s innovative contribution is threefold: Firstly, focus on professional stakeholders’ opinion in the value network. Secondly, recommendations to clearly communicate personalised media value, benefits and risks to users. This allows media to create codes of conduct that increase user trust. Thirdly, expanding literature explaining how media realise personal data value, deal with stakeholder interests and position themselves in the data processing debate. This research improves understanding of personal data value, processing benefits and potential risks in a regional context and European regulatory framework.

Personae are simple tools for describing users, their characteristics and their goals. They are valuable tools when designing for a specific group of users, such as children with autism spectrum condition (ASC). The purpose of this paper is to propose, validate and revise a methodology for creating accurate, data grounded personae for children with ASC.

The proposed method is based mainly on Cooper et al.’s (2007) persona construction method. It proposes gathering and analysing qualitative data from users and experts to either create a new persona or extend an existing one. The method is then applied to create personae for the design of a visual programming tool for children with ASC. Based on the results of the application, observations and lessons learnt, a revised version of the method is proposed.

The method’s combined use of user data and expert knowledge produced a set of personae that have been well reviewed by experts so far. The method’s use of a questionnaire to validate personae also produced relevant qualitative feedback. On review, possible downsides of extending existing personae were identified. Therefore, a revised method was introduced, eliminating the need to extend existing personae, and stressing the importance of utilising user data, expert knowledge and feedback.

This paper addresses the need for a well-defined method for creating data grounded personae that accurately describe the characteristics and goals of children with ASC. Such personae can be used to design and develop more accessible and usable products.

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

Cost transparency is of central importance to reach a consensus between supply chain partners. The purpose of this paper is to contribute to the instrument of cost analysis which supports the link between buyers and suppliers.

Based on a detailed literature review in the area of cost analysis and purchasing, intelligent decision support systems for cost estimation are identified. Subsequently, expert interviews are conducted to determine the application possibilities for managers. The application potential is derived from the synthesis of motivation, identified applications and challenges in the industry. Management recommendations are to be derived by bringing together scientific and practical approaches in the industry.

On the one hand, the results of this study show that machine learning (ML) is a complex technology that poses many challenges for cost and purchasing managers. On the other hand, ML methods, especially in combination with expert knowledge and other analytical methods, offer immense added value for cost analysis in purchasing.

Digital transformation allows to facilitate the cost calculation process in purchasing decisions. In this context, the application of ML approaches has gained increased attention. While such approaches can lead to high cost reductions on the side of both suppliers and buyers, an intelligent cost analysis is very demanding.

India's textile industries play a vital role in the Indian economy. These industries consume the highest thermal energy (steam power). The demand of the steam in process industries is increasing rapidly, and this demand can be met by increasing the capacity utilization of steam boilers. The purpose of this paper is to present a new approach for reliability analysis by expert judgment method.

A lack of adequate life data is one of the biggest challenge in the reliability analysis of mechanical systems. This research provides an expert judgment approach for assessing the boiler's reliability characteristics. For this purpose, opinions of experts on time to failure and time to repair data were elicited in the form of statistical distributions. In this work, reliability analysis of the boiler system is carried out by expert judgment method and by using best-fit failure model. The system reliability along with preventive maintenance intervals of all components is also evaluated.

It is observed that the reliability analysis results obtained by expert judgment method and best-fit failure model method indicate that there are no significant differences. Therefore, in case when insufficient data are available, the expert judgment method can be effectively used. The analysis shows that the feedwater tank, feedwater pump, supply water temperature sensor, strainer, return water temperature sensor, condensate filter, mechanical dust collector, coal crusher and fusible plug are identified as critical components from a reliability perspective, and preventive maintenance strategy is suggested for these components.

In this research paper, a system reliability model by the expert judgment method is developed, and it can be effectively used where insufficient failure data are available. This paper is useful for the comparative evaluation of reliability characteristics of a boiler system by expert judgment method and best-fit failure model method.

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.

The need effectively to integrate decision making tasks together with knowledge representation and inference procedures has caused recent research efforts towards the integration of decision support systems with knowledge‐based techniques. Explores the potential benefits of such integration in the area of business forecasting. Describes the forecasting process and identifies its main functional elements. Some of these elements provide the requirements for an intelligent forecasting support system. Describes the architecture and the implementation of such a system, the theta intelligent forecasting information system (TIFIS) that that first‐named author had developed during his dissertation. In TIFIS, besides the traditional components of a decision‐support onformation system, four constituents are included that try to model the expertise required. The information system adopts an object‐oriented approach to forecasting and exploits the forecasting engine of the theta model integrated with automated rule based adjustments and judgmental adjustments. Tests the forecasting accuracy of the information system on the M3‐competition monthly data.

Customer identity and access management (CIAM) is a sub-genre of traditional identity and access management (IAM) that has emerged in the past few years to meet evolving business requirements. CIAM focuses on the connectivity with the customer when accessing any type of systems, on-premises and in the cloud, from registration to track. The purpose of this study is to introduce different dimensions of CIAM toward exploiting them in organizations.

Based on a thorough review of the relevant literature and semi-structured interview with six experts in the field of digital IAM the necessary data were gathered. Then through the use of content analysis technique, analytic codes and also categories and sub-categories of the data were generated.

Results indicate that four categories, namely, customer identity management, customer access management and information technology and business management are the most important factors affecting the identification of CIAM dimensions.

Organizations could avail of the proposed conceptual model toward identification and offering customized products and services solutions to their customers.

Particularly in volatile, uncertain, complex and ambiguous (VUCA) business conditions, staff in supply chain management (SCM) look to real-time (RT) data processing to reduce uncertainties. However, based on the premise that data processing can be perfectly mastered, such expectations do not reflect reality. The purpose of this paper is to investigate whether RT data processing reduces SCM uncertainties under real-world conditions.

Aiming to facilitate communication on the research question, a Delphi expert survey was conducted to identify challenges of RT data processing in SCM operations and to assess whether it does influence the reduction of SCM uncertainty. In total, 14 prospective statements concerning RT data processing in SCM operations were developed and evaluated by 68 SCM and data-science experts.

RT data processing was found to have an ambivalent influence on the reduction of SCM complexity and associated uncertainty. Analysis of the data collected from the study participants revealed a new type of uncertainty related to SCM data itself.

This paper discusses the challenges of gathering relevant, timely and accurate data sets in VUCA environments and creates awareness of the relationship between data-related uncertainty and SCM uncertainty. Thus, it provides valuable insights for practitioners and the basis for further research on this subject.