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Due to technology advancement or transparency in system, there is a constant inflow and outflow of technology in the business for transparency and efficiency. To seize a competitive advantage, companies have emerged new technological solutions to respond to the change in the organization environment. There is a surge in the requirement of learning opportunities and effective training programs in the organization. The current study has been an effort to understand the potential of blockchain technology that can create better training evaluation.

The electronic-Delphi (e-Delphi) method has been conducted by recording the final consensus and to find a balance for implementation of blockchain technology and measuring training effectiveness. The current research is one of its new types where blockchain-enabled training effectiveness measurement (BETEM) model has been formulated using a qualitative approach.

The study has considered human resource (HR) professionals as the experts and based on their responses, the formulation of theoretical network model has been structured using e-Delphi–BETEM (e-DLH–BETEM) approach. By critically examining the experts’ responses and comments, the study formulated the four major themes and 11 subthemes for the smooth functioning of the BETEM for an organization.

The research aims to aid innovations in BETEMs model for training evaluation. The model will contribute incrementally toward the complete transformation of the training development programs of employees. The goal of BETEMs is to ensure that organizations, specifically HR personals can prepare themselves to have competitive advantage by using blockchain technology.

The application of blockchain technology in measuring the training effectiveness is an addition to existing literature as majority of existing studies have studied the use of technology for measuring training effectiveness.

The purpose of this paper is to propose an approach to achieve better accuracy in technology forecasting (TF) by providing the concepts of the service components and service composition based on the theory of the combining forecasts. Next, it adopts three quantitative analyses as service components to form service composition. This will support the need of more predictable TF, which raises the accuracy of the quantitative analysis and, at the same time, presents the service composition logic in a consistent manner in the form of customized TF.

This paper provides a systematic analysis of the technology forecasts for third‐generation (3G) telecommunication industry. This systematic approach mainly unifies the Bass model, logit model, and least squares analysis forecasting techniques, along with a reasonable assessment of the scope for the normal curve (±1 standard deviation), and attempts to find the maximum possibility frontier of the predictive value.

Through the integration and comparison of these three techniques, not only can the predicted values of the three forecasting methods be determined, but a preferred solution can also be derived through new methods, and in return, to investigate better accuracy and performances. Such an approach can also integrate the advantages of various methods to provide a prediction interval, as well as objective and realistic projections.

This envisaged concept of “service component and service composition” is an integration of backing up in TF instruments in selection and reselection, which in return, provide optimization of service composition and accuracy maximization, as well as better performance prediction. A well‐known limitation of this research is that sudden technology breakthroughs are often unforeseeable in the majority of main‐stream quantitative analyses.

Constructing a new effective approach as results of “service component and service composition” can be compared to the traditional research methods such as Delphi method or other mathematical algorithms. This method generally produces higher quality forecasts than those attained from a single source.

This paper aims to present the results of experiments with groups making online group stock price predictions and include the research process and a summary of the preliminary results. The overall objectives of the study are to assess the effect of individual and remote group decision-making approaches to stock price predictions, to assess whether a learning effect exists through the feedback loop of an e-Delphi process and to identify the underlying key mechanisms of the individual and of the group that influence the decision-making process.

The experiments consist of a pilot and a main run. The main run was performed with three lay groups (totaling 49 participants) and two expert groups (totaling ten financial analysts and other stock market professionals). The groups were benchmarked with actual market prices as well as with each other, over ten e-Delphi cycles (ten weeks). Each participant in the experiment was asked to provide an estimation of the movement (up or down) for one-week, one-month and three-month future periods for each share, as well as to enter a stock price prediction for a three-month period.

Although the pilot run has provided some indications that in certain situations and with careful group design, stock price predictions can be superior to the predictions of experts, the main experiment indicated a more differentiated picture and provided some information about the underlying decision-making process.

The paper presents influence factors and measures the impact of the group decision-making process of Internet communities focusing on stock trading, based on predicting share prices.

Education and training are critical factors for achieving the Lisbon strategy's objectives of encouraging economic growth, competitiveness and social inclusion in the European Union (EU). The role of continuing vocational training (CVT) and its contribution to personal development and fulfilment is increasingly recognised in EU Member States' National Reform Programmes. In this context the main objective of this paper is to establish a ranking of the importance of the competences required by CVT trainers in Spain according to national lifelong learning (LLL) standards.

A combination of literature review analysis and evaluation of experts' participation is used, based on two‐round e‐Delphi techniques. The literature review analysis identified the criteria, sub‐criteria and indicators that describe the trainers' basic (pedagogical) and specific (skills, abilities, aptitude/attitude and attributes) competences. The questionnaire summarises a wide range of competences and skills related to Spanish trainers. It was distributed among 20 national experts (with experience ranging from three to 30 years in informal and non‐formal training and professional profiles such as training manager, high manager, training programmer, trainers and tutors among others) in order to evaluate the importance of different competences.

The analysis of the e‐Delphi expert evaluation yields two main findings. First, it provides a unique e‐Delphi draft that contains trainers' professional competences in a non‐formal permanent learning context. Second, it also includes the evaluation of their importance by 20 Spanish experts who are considered to be a qualitative national sample.

The role of European and Spanish trainers has changed in the last two decades. Trainers need wide recognition of their professional profile in a LLL framework. This requires the recognition of new and renewal of traditional competences, so that they can work as professionals of non‐formal education and assume responsibilities.

CVT takes on a variety of forms in different countries and also within a given country. Thus the identification and anticipation of competences and skills required by trainers as important actors on whom training quality and efficiency depends is important. In this case study, the analysis of the expert evaluations shows that specific competences/skills (personal abilities, attitudes in classroom and workshop), are very important in developing a trainer's professional figure. Traditional pedagogical competences (planning, imparting and evaluation) are not forgotten, but new skills have additional characteristics such as “identification, and analysis of training needs”, “training management”, “training implementation” and participant focus.

The purpose of this paper is to describe a participatory qualitative research project using the Delphi consensus technique aided by Internet technology for successful transition of academic endeavors during and the post COVID era. The study aimed to derive a holistic competency matrix for an optometry program for transformation of the program to competency-based education.

This study combined a scoping review of literature for developing a baseline competency framework, along with derivation of an E-Delphi consensus within a panel of diverse stakeholders to achieve the objective of plotting a detailed competency matrix.

The involvement of all stakeholders of educational system in Delphi study resulted in a valid and all-inclusive competency framework with 18 competency units and 97 competency elements. This framework provided a strong base for redesigning pedagogy and assessment methods during COVID-19 crisis.

The paper highlights the feasibility and utility of adopting a participatory approach during COVID-19 outbreak. The Delphi technique aided by Internet technology was employed to develop a competency-based curriculum.

The paper narrates a suitable, feasible and scientific method for rapid transition in academia, following the restrictions and social distancing norms imposed during the COVID-19 outbreak.

Although there is good evidence for use of the Delphi technique in curriculum development, this paper adds value by focusing on a participatory approach in using it. The suggested method here shows ways to gain optimum scientific output with minimum resources in constrained situations such as COVID-19 crisis.

The use of socio-economic scenarios in small island developing states (SIDS) when assessing, and planning for, the impacts of global changes on national socioeconomic and environmental systems is still in its infancy. The research conducts a cross-scale foresight scenario exercise to produce regional scenarios and national storylines for Caribbean islands that are of “partial” consistency to the shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) and shows how future socioeconomic and climatic changes can be applied to inform natural resource management decisions.

To develop the scenarios, the study uses a three-staged linking process using mixed methods to “triangulate” each technique to compensate for weaknesses of one method by introducing a complementary method at each stage. A participatory-expert stepwise approach with feedback loops is used and complemented with a climate sensitive tourism water demand model.

Four regional exploratory socio-economic scenarios were constructed that are partially consistent with global scenarios. In addition, national storylines for four island states were developed based on the regional scenarios. Using RCP 4.5 hotel water demand in Barbados is estimated under three of the regional scenarios based on compatibility. The results indicate there is a 17% difference between the highest and lowest estimated water demand, indicating the effect of varying socio-economic conditions on water demand.

The paper contributes to the literature by presenting regional socio-economic scenarios, specifically for SIDS, that are partially consistent with both global climatic RCPs and SSPs using a cross-scale approach. The scenarios are then used to demonstrate how future socio-economic pathways impact on freshwater demand.

This study aims to analyze and synthesize the design features of existing digital transformation maturity models with a developed classification scheme and propose a generic maturity model development wireframe based on design science research.

A systematic literature review is conducted on digital transformation maturity models in peer-reviewed journals, including the Emerald Insight, Science Direct, Scopus, Taylor & Francis and Web of Science databases, which resulted in 21 studies. A concept-centric tabular approach is used to analyze the studies, and intersectional demonstrations are used to synthesize the findings regarding the design features.

The classification scheme derived from the tabular concept-centric approach and iteratively evolved results in three main and 25 subcategories related to the design features. Analysis and synthesis of the studies reveal the granularity of the existing digital transformation maturity models concerning the design features. Furthermore, considering the design features in the classification scheme, a generic maturity model development wireframe is proposed to guide the researchers.

The generic maturity model development wireframe and the classification scheme that represents the design features of existing maturity models guide the researchers for the maturity model development roadmap.

The existing literature review studies do not focus on the design feature of digital transformation maturity models within a systematic literature review perspective. A unique classification scheme derived from the tabular concept-centric approach aims to analyze the granularity level of the existing models. Furthermore, the generic maturity model development wireframe includes the guidelines and recommendations of design science studies and presents a roadmap for maturity model researchers.

Assessing the severity of failure modes of critical industrial machinery is often considered as an onerous task and sometimes misinterpreted by shop-floor engineer/maintenance personnel. The purpose of this paper is to develop an improved FMECA method for prioritizing the failure modes as per their risk levels and validating the same through a real case study of induction motors used in a process plant.

This paper presents a novel hybrid multi-criteria decision-making (MCDM) approach to prioritize different failure modes according to their risk levels by combining analytical hierarchy process (AHP) with a newly introduced MCDM approach, election based on relative value distance (ERVD). AHP is incorporated in the proposed approach to determine the criteria weights, evaluated in linguistic terms by industrial expert. Furthermore, ERVD, which is based on the concept of prospect theory of human cognitive process, is applied to rank the potential failure modes.

It is found that the proposed FMECA approach provides better results in accordance with the actual industrial scenario and helps in effectively prioritizing the failure modes. A comparison is also made to highlight the differences of results between the proposed approach with TOPSIS and conventional FMECA.

This research paper proposes an improved FMECA method and, thus, provides a deep insight to maintenance managers for effectively prioritizing the failure modes. The correct prioritization of failure modes will help in effective maintenance planning, thus reducing the downtime and improving profit to the organization.

A real case of process plant induction motor has been introduced in the research paper to show the applicability of this decision-making approach, and the approach is found to be suitable in correct prioritization of the failure modes.

Severity has been decoupled into various factors affecting it, to make it more relevant as per actual industrial scenario. Then, a novel modified FMECA has been developed using a hybrid MCDM approach (AHP and ERVD). This hybrid method, as well as its application in FMECA, has not been developed by any previous researcher. Moreover, the same has been thoroughly explained by considering a real case of process plant induction motors and validated with cross-functional experts.