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This study aims to investigate the manufacturing reshoring decision-making content from an Eclectic Paradigm perspective.

Data were collected through a six-step systematic literature review on factors influencing manufacturing reshoring decision-making. The review is based on 100 peer-reviewed journal papers discussing reshoring decision-making contents published from 2009 to 2022.

In total, 80 decision factors were extracted and then categorized into resource-seeking (8%), market-seeking (11%), efficiency-seeking (41%) and strategic asset-seeking (16%) advantages. Additionally, 24% of these were identified as hybrid, which means that they were classified into multiple categories. Some decision factors were further identified as reshoring influencing factors (i.e. drivers, enablers and barriers).

Scholars need to consider what other theories can be used or developed to identify and evaluate the decision factors (determinants) of manufacturing reshoring as well as how currently adopted theory can be further advanced to create clearer and comprehensive theoretical frameworks.

This research underscores the importance of developing clearer and more comprehensive theoretical frameworks. For practitioners, understanding the multifaceted nature of decision factors could enhance strategic decision-making regarding reshoring initiatives.

To the best of the authors’ knowledge, this is the first study to investigate the value and practicality of the Eclectic Paradigm in categorizing factors in manufacturing reshoring decision-making content and presents in-depth theoretical classifications. In addition, it bridges the gap between decision factors and influencing factors in the decision-making content research realm.

This study aims to estimate the value of the impact from digital transformation (DX) focusing on its automation effect, looking at the time and cost savings coming from the substitution effect with an adoption of digital technologies. For example, cloud and artificial intelligence technologies such as ChatGPT have the potential to change ways of working, substituting and replacing several of the tasks that are currently carried out by public administration (PA) employees and labor processes underpinning PA services.

The paper outlines a new framework to estimate the potential impact of DX on the public sector. The authors apply this framework to estimate the value of the impact of DX on the Italian PA, defining the latter by the collection of the value of its labor (i.e. PA workforce salaries) and by the collection of the value of its outputs (i.e. public services’ costs).

This study ultimately maps out the magnitude and trends of how likely the PA occupations and services could be substituted in a wider process of DX. To do this, the authors apply their framework to the Italian PA, and they triangulate secondary data collection, from official accounts of the Italian Ministry of Economics and the National Statistical Institute, with methodological antecedents from the UK Office for National Statistics and experts’ insights. Results provide a snapshot on the type and magnitude of PA jobs and services projected to be affected by automation over the next 10 years.

To the best of the authors’ knowledge, this paper provides for the first time an approach to estimate the value of the impact of DX on the public sector in a data-constrained environment – or in the lack of the required primary data. Once applied to the Italian PA, this approach provides a granular map of the automatability of each of the PA occupations and of the PA services. Finally, this paper mentions preliminary insights on potential challenges related to equity in public sector jobs and implications on recruitment processes.

Ongoing automation processes may render a fair share of the existing jobs redundant or change their nature. This begs the question to what extent employees affected invest in training in order to strengthen their labour market position in times of uncertainty. Given the different national labour market regimes and institutions, there may be an important geographical dimension to the opportunities to cope with the challenges set by automation. The purpose of this study is to address both issues.

Using data from the 2016 European labour Force Survey, the authors estimate with logit and multi-level regression analyses how the automation risk of a worker's job is associated with the propensity of following non-formal education/training. The authors allow this relationship to vary across European countries.

The results show that employees in jobs vulnerable to automation invest relatively little in training. Also, there are significant differences across Europe in both the provision of training in general and the effect of automation on training provision.

While there is quite a lot of research on the structural labour market effects of automation, relatively little is known about the actions that employees take to deal with the uncertainty they are faced with. This article aims to contribute to our understanding of such mechanisms underlying the structural macro-level labour-market dynamics.

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.

To meet customers' expectations on shorter lead times, high product availability, flexibility, and variation in delivery and return options, retailers have turned their attention to warehousing and are making big investments in technology. Currently, technology providers are pushing for smart warehousing, a new and under-researched phenomenon. This study aims to conceptualize the term and examine pathways toward implementing smart warehousing.

An exploratory survey was administered to 50 leading Swedish retailers in varying segments. A two-tailed t-test for equality of means was used to detect significant differences between current and future states.

The study found that future smart warehouses will be automated, autonomous, digital, and connected, but that retailers will follow different paths along this journey, driven by contextual trends, e.g. sales growth, wider product assortment, shorter lead-time offerings, and integration of brick-and-mortar and online stores. Interestingly, the study revealed that many of the retailers that aim to create smart warehouses in five years are not the retailers with the most developed technology today.

The paper operationalizes smart warehousing in two dimensions: degree of automation and degree of digitalization and connectivity of information platforms. Based on the findings, 16 theoretical propositions are put forth that, based on contextual factors, explain different pathways for retailers to implement smart warehousing.

The empirical insights and theoretical discussions provide practically useful guidance, including outlined trends, for selecting and benchmarking automation and complementary technologies in warehouse operations.

This paper conceptualizes and operationalizes smart warehousing – an original approach. It is also one of the first to investigate the technological transformation in retail warehousing empirically, explaining how and why retailers choose different pathways toward smart warehousing.

The study aims to provide a critical review of the extent to which digital technologies are likely to replace human labour, the exponential rise in the amount of work to be done and how far distinctively human skills are future-proofed and therefore likely to be in short supply. It reviews the evidence for a permanent switch to home and remote working enabled by emerging technologies. It assesses the business, digital and labour strategies of work organisations and the promise and challenges from a dominant trend towards a digitally enabled flexible labour model.

A critical review of 1020 plus case studies and the extant literature was carried out.

The relationship between emerging technologies and work is widely misunderstood, and there are major qualifiers to the idea of an overwhelming tsunami of technology drastically reducing headcounts globally. Distinctive human skills remain valuable, the amount of work to be done is increasing exponentially and automation is becoming more a coping than a labour replacement mechanism. Moves to a hybrid digitalised flexible labour model are promising but not if short-term, and if the challenges they represent are not managed well.

The main limitation is that we are making projections into the future, though we are drawing on a lot of different sources and evidence and past data projected into the future.

The problem is not labour displacement but large skills shortages that will slow down the speed of technology adoption. Skills development is vital, as is the taking of long-term perspectives towards the management of hybrid, flexible working based on human-machine interactions.

Organisations need to revitalise their training and development and labour management models. Governments and intermediary institutions need to manage transition states if the skills required to gain economic growth are to be available, and to ensure that large labour pools do not get bypassed from not having requisite skills.

The study offers a more subtle and complex perspective on the emerging evidence about the future of technology and work.

Recent studies on commuter parking in an age of fully autonomous vehicles (FAVs) suggest, that the number of parking spaces close to the workplace demanded by commuters will decline because of the capability of FAVs to return home, to seek out (free) parking elsewhere or just cruise. This would be good news because, as of today, parking is one of the largest consumers of urban land and is associated with substantial costs to society. None of the studies, however, is concerned with the special case of employer-provided parking, although workplace parking is a widespread phenomenon and, in many instances, the dominant form of commuter parking. The purpose of this paper is to analyze whether commuter parking will decline with the advent of self-driving cars when parking is provided by the employer.

This study looks at commuter parking from the perspective of both the employer and the employee because in the case of employer-provided parking, the firm’s decision to offer a parking space and the incentive of employees to accept that offer are closely interrelated because of the fringe benefit character of workplace parking. This study develops an economic equilibrium model that explicitly maps the employer–employee relationship, considering the treatment of parking provision and parking policy in the income tax code and accounting for adverse effects from commuting, parking and public transit. This study determines the market level of employer-provided parking in the absence and presence of FAVs and identifies the factors that drive the difference. This study then approximates the magnitude of each factor, relying on recent (first) empirical evidence on the impacts of FAVs.

This paper’s analysis suggests that as long as distortive (tax) policy favors employer-provided parking, FAVs are no guarantee to end up with less commuter parking.

This study’s findings imply that in a world of self-driving cars, policy intervention related to work commuting (e.g. fringe benefit taxation or transport pricing) might be even more warranted than today.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

This study examines the propensity to innovate in automation of family firms (FFs) based on the socio-emotional wealth (SEW) perspective.

This study’s analysis is based on three aspects. First, the authors consider three main non-economic goals and priorities of FFs: the family’s relationship with employees (read as to care for their satisfaction and well-being); the inner pride of building and maintaining the family and firm image and reputation; and the inner feeling to be socially responsible. Second, the authors consider how these goals and priorities vary among FFs according to four dimensions: family ownership, the presence of family members on the board of directors, the involvement of young successors, and the presence of founding and later generations. Finally, the consequences of automation are considered: lower firm employment, lower employees’ satisfaction and well-being, and higher firm productivity. The analysis is based on a sample of 4,150 Italian firms.

The analysis revealed that FFs are less prone to innovate in automation than non-FFs. Specifically, family ownership, the presence of family members on the board of directors, and the presence of founding generation are negatively associated with innovation in automation. Instead, the involvement of young successors and the presence of later generation are positively associated with innovation in automation.

To the authors’ knowledge, this study is the first investigation that, based on SEW, examines how FFs act on the decision to innovate in automation, thereby providing empirical evidence.

The paper aims to raise awareness in the industry of design automation tools, especially in early design phases, by demonstrating along a case study the seamless integration of a prototypically implemented optimization, supporting design space exploration in the early design phase and an in operational use product configurator, supporting the drafting and detailing of the solution predominantly in the later design phase.

Based on the comparison of modeled as-is and to-be processes of ascent assembly designs with and without design automation tools, an automation roadmap is developed. Using qualitative and quantitative assessments, the potentials and benefits, as well as acceptance and usage aspects, are evaluated.

Engineers tend to consider design automation for routine tasks. Yet, prototypical implementations support the communication and identification of the potential for the early stages of the design process to explore solution spaces. In this context, choosing from and interactively working with automatically generated alternative solutions emerged as a particular focus. Translators, enabling automatic downstream propagation of changes and thus ensuring consistency as to change management were also evaluated to be of major value.

A systematic validation of design automation in design practice is presented. For generalization, more case studies are needed. Further, the derivation of appropriate metrics needs to be investigated to normalize validation of design automation in future research.

Integration of design automation in early design phases has great potential for reducing costs in the market launch. Prototypical implementations are an important ingredient for potential evaluation of actual usage and acceptance before implementing a live system.

There is a lack of systematic validation of design automation tools supporting early design phases. In this context, this work contributes a systematically validated industrial case study. Early design-phases-support technology transfer is important because of high leverage potential.