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This paper aims to describe an interdisciplinary and innovative research conducted in Switzerland, at the Geneva School of Business Administration HES-SO and supported by the State Archives of Neuchâtel (Office des archives de l'État de Neuchâtel, OAEN). The problem to be addressed is one of the most classical ones: how to extract and discriminate relevant data in a huge amount of diversified and complex data record formats and contents. The goal of this study is to provide a framework and a proof of concept for a software that helps taking defensible decisions on the retention and disposal of records and data proposed to the OAEN. For this purpose, the authors designed two axes: the archival axis, to propose archival metrics for the appraisal of structured and unstructured data, and the data mining axis to propose algorithmic methods as complementary or/and additional metrics for the appraisal process.

Based on two axes, this exploratory study designs and tests the feasibility of archival metrics that are paired to data mining metrics, to advance, as much as possible, the digital appraisal process in a systematic or even automatic way. Under Axis 1, the authors have initiated three steps: first, the design of a conceptual framework to records data appraisal with a detailed three-dimensional approach (trustworthiness, exploitability, representativeness). In addition, the authors defined the main principles and postulates to guide the operationalization of the conceptual dimensions. Second, the operationalization proposed metrics expressed in terms of variables supported by a quantitative method for their measurement and scoring. Third, the authors shared this conceptual framework proposing the dimensions and operationalized variables (metrics) with experienced professionals to validate them. The expert’s feedback finally gave the authors an idea on: the relevance and the feasibility of these metrics. Those two aspects may demonstrate the acceptability of such method in a real-life archival practice. In parallel, Axis 2 proposes functionalities to cover not only macro analysis for data but also the algorithmic methods to enable the computation of digital archival and data mining metrics. Based on that, three use cases were proposed to imagine plausible and illustrative scenarios for the application of such a solution.

The main results demonstrate the feasibility of measuring the value of data and records with a reproducible method. More specifically, for Axis 1, the authors applied the metrics in a flexible and modular way. The authors defined also the main principles needed to enable computational scoring method. The results obtained through the expert’s consultation on the relevance of 42 metrics indicate an acceptance rate above 80%. In addition, the results show that 60% of all metrics can be automated. Regarding Axis 2, 33 functionalities were developed and proposed under six main types: macro analysis, microanalysis, statistics, retrieval, administration and, finally, the decision modeling and machine learning. The relevance of metrics and functionalities is based on the theoretical validity and computational character of their method. These results are largely satisfactory and promising.

This study offers a valuable aid to improve the validity and performance of archival appraisal processes and decision-making. Transferability and applicability of these archival and data mining metrics could be considered for other types of data. An adaptation of this method and its metrics could be tested on research data, medical data or banking data.

This paper aims to investigate the requirements for automating aspects of the appraisal process for digital objects. It explores these requirements in the context of automating re‐appraisal and questions many of the assumptions commonly made about appraisal and about automating the processes needed for life‐cycle management of digital objects.

The literature of digital preservation and curation and the experience of one of the authors in planning to implement a digital archive at the Wellcome Library are the basis of an exploration of issues.

The development of automated appraisal systems and associated tools is a worthwhile endeavour, although the complexity and cost associated with designing, developing and implementing them may be prohibitive in some situations. An automated appraisal system may, however, have only limited benefits in some contexts. The re‐appraisal of technical attributes of digital materials, which is an essential part of their management, is a prime contender for some level of automation. The approach proposed has limitations which arise from such factors as metadata requirements and trustworthiness.

The paper articulates assumptions made about automation and applies these in order to gain a better understanding of the requirements of automating aspects of appraisal in a digital archive.

Robotic process automation (RPA) has recently emerged as a technology focusing on the automation of repetitive, frequent, voluminous and rule-based tasks. Despite a few practical examples that document successful RPA deployments in organizations, evidence of its economic benefits has been mostly anecdotal. The purpose of this paper is to present a step-by-step method to RPA investment appraisal and a business case demonstrating how the steps can be applied to practice.

The methodology relies on design science research (DSR). The step-by-step method is a design artefact that builds on the mapping of processes and modelling of the associated costs. Due to the longitudinal nature of capital investments, modelling uses discounted cashflow and present value methods. Empirical grounding characteristic to DSR is achieved by field testing the artefact.

The step-by-step method is comprised of a preparatory step, three modelling steps and a concluding step. The modelling consists of compounding the interest rate, discounting the investment costs and establishing measures for comparison. These steps were applied to seven business processes to be automated by the case company, Estate Blend. The decision to deploy RPA was found to be trivial, not only based on the initial case data, but also based on multiple sensitivity analyses that showed how resistant RPA investments are to changing circumstances.

By following the provided step-by-step method, executives and managers can quantify the costs and benefits of RPA. The developed method enables any organization to directly compare investment alternatives against each other and against the probable status quo where many tasks in organizations are still carried out manually with little to no automation.

The paper addresses a growing new domain in the field of business process management by capitalizing on DSR and modelling-based approaches to RPA investment appraisal.

Government entities often implement automated records management systems, often without a clear governing framework to facilitate such automation. This study aims to explore the role played by information architecture (IA) in records automation in Botswana and propose a guiding framework in the context of e-government.

This qualitative study uses a focused literature review to study the importance of IA in records automation in e-government.

Without proper IA, information organization including retrieval/access to records becomes difficult. Practically, this would practically and negatively affect process automation in e-government solutions.

The proposed framework can guide e-government record automation in Botswana. However, its limitation lies in the fact that it has not been tested, thereby limiting its practicality until tested empirically.

The proposed framework can be used to inform record automation management processes in the realm of Botswana’s e-government project.

The proposed framework contributes to the body of knowledge on the automation of records and e-government in Botswana specifically and Africa in general.

Robots are still not used widely enough in the electrical and electronics industry, and the various reasons for this are explained.

Both uncertainty reduction theory and uncertainty management theory suggest that uncertainty reduction during organizational change is imperative as it may influence positively employees’ attitudes and behaviors. By drawing on the theory of planned behavior that links individual’s beliefs and behavior, the study seeks to examine how employees’ self- and other-interest in change may reduce perceptions of change uncertainty and consequently, foster their behavioral change support (i.e. compliance, cooperation and championing).

The study hypothesizes that employees’ self- and other-interest in change mediate the relationship between perceptions of change uncertainty and behavioral change support. The research was conducted in two large IT companies co-located in a big science park in an EU country in South-eastern Europe. Data were collected from 105 employees and their supervisors in three sequential phases.

The research findings suggest that both self- and other-interest in change partially mediate the negative relationship between perceptions of change uncertainty and behavioral change support.

The findings indicate that change management practitioners will benefit considerably if they try to decrease employees’ perceived change uncertainty by increasing their self- and other-interest in change to provoke supportive behaviors. Relevant suggestions are made.

The findings provide new insights into how perceptions of change uncertainty and self- and other-interest in change can affect employees’ change participation. Further, the research findings add to the uncertainty reduction theory and uncertainty management theory as well as, other related notions.

This study aims to explore the implementation of artificial intelligence (AI) in archival practice by presenting the thoughts and opinions of working archival practitioners. It contributes to the extant literature with a fresh perspective, expanding the discussion on AI adoption by investigating how it influences the perceptions of digital archival expertise.

In this study a two-phase data collection consisting of four online focus groups was held to gather the opinions of international archives and digital preservation professionals (n = 16), that participated on a volunteer basis. The qualitative analysis of the transcripts was performed using template analysis, a style of thematic analysis.

Four main themes were identified: fitting AI into day to day practice; the responsible use of (AI) technology; managing expectations (about AI adoption) and bias associated with the use of AI. The analysis suggests that AI adoption combined with hindsight about digitisation as a disruptive technology might provide archival practitioners with a framework for re-defining, advocating and outlining digital archival expertise.

The volunteer basis of this study meant that the sample was not representative or generalisable.

Although the results of this research are not generalisable, they shed light on the challenges prospected by the implementation of AI in the archives and for the digital curation professionals dealing with this change. The evolution of the characterisation of digital archival expertise is a topic reserved for future research.

The purpose of this study is to automatically generate a construction schedule by extracting data from the BIM (Building Information Modeling) model and combining an ontology constraint rule and a genetic algorithm (GA).

This study developed a feasible multi-phase framework to generate the construction schedule automatically through extracting information from the BIM, utilizing the ontology constraint rule to demonstrate the relationships between all the components and finally using the GA to generate the construction schedule.

To present the functionality of the framework, a prototype case is adopted to show the whole procedure, and the results show that the scheme designed in this study can quickly generate the schedule and ensure that it can satisfy the requirements of logical constraints and time parameter constraints.

A proper utilization of conceptual framework can contribute to the automatic generation of construction schedules and significantly reduce manual errors in the Architectural, Engineering, and Construction (AEC) industry. Moreover, a scheme of BIM-based ontology and GA for construction schedule generation may reduce additional manual work and improve schedule management performance.

The hybrid approach combines the ontology constraint rule and GA proposed in this study, and it is an effective attempt to generate the construction schedule, which provides a direct indicator for the schedule control of the project.

In this study, the data application process of the BIM model is divided into four modules: extraction, processing, optimization, and output. The key technologies including secondary development, ontology theory, and GA are introduced to develop a multi-phase framework for the automatic generation of the construction schedule and to realize the schedule prediction under logical constraints and duration interference.

The use of building information modelling (BIM) methodology has been increasing in the architecture, engineering, construction and operation sector, driven to a new paradigm of work with the use of three-dimensional (3D) parametric models. However, building information modelling (BIM) has been mostly used for as-built models of a building, not yet been widely used by designers during project and construction phases for occupational risks prevention and safety planning. This paper aims to show the capacity of developing tools that allow adding functionalities to Revit software to improve safety procedures and reduce the time spent on modelling them during the design phase.

To reach this objective, a structural 3D model of a building is used to validate the developed tools. A plugin prototype based on legal regulations was developed, allowing qualitative safety assessment through the application of job hazard analysis (JHA), SafeObject and checklists. These tools allow the automated detection of falls from height situations and the automated placement of the correspondent safety systems.

Revit application programming interface allowed the conception and addition of several functionalities that can be used in BIM methodology, and more specifically in the prevention of occupational risks in construction, contributing this paper to the application of a new approach to the prevention through design.

This paper is innovative and important because the developed plugins allowed: automated detection of potential falls from heights in the design stage; automated introduction of safety objects from a BIM Safety Objects Library; and the intercommunication between a BIM model and a safety database, bringing JHA integration directly on the project. The prototype of this work was validated for fall from height hazards but can be extended to other potentials hazards since the initial design stage.

The construction industry has been evolving in recent years through the adoption of smart tools such as building information modeling to reduce the complexity in the construction process and optimize the project's goals. This paper aims to identify and assess the key drivers for the implementation of smart sustainable practices in the construction industry.

Inferential and descriptive statistical techniques were employed in analyzing the data collected via an international empirical questionnaire survey deployed in soliciting the perceptions of 220 construction professionals across 21 countries. Factor analysis was used to categorize the identified key drivers into their underlying clusters for further discussion. Also, the data were analyzed based on the various groups and regions of the study's respondents.

The key drivers (KDs) are related to the technical competence of staff as well as knowledge and awareness level within the industry, issues related to organizational and project's strategy and policies, availability of financial resources and development of relevant standards and policies to aid its execution among others. A comparative analysis of the perceptions of the different respondents' groups was undertaken and discussed.

The analysis of the key drivers for the implementation of smart and sustainable practices in the construction industry is expected to aid the decision-making of the relevant stakeholders as well as serve as a consultation instrument for government agencies in their design of localized policies and guidelines to aid smart and sustainable urbanization. The findings revealed the gaps in the implementation of smart and sustainable practices in various climes and organization setups and provided useful and practical strategies for addressing the current hindrances during implementation.

The study has generated valuable insights into the significant drivers that can enhance the implementation of smart and sustainable practices across regions. It is evident that synergy among the relevant stakeholders in the built environment will help accelerate the implementation of smart sustainable practices in the construction industry. The study findings have provided profound contributions to theory and research as well as to industry practice.