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The paper aims to explore the value generated by a specific configuration of a smart city's infrastructure by proposing a comparison between a silos configuration versus a crowd configuration at the data storage and processing level.

A system dynamics simulation is adopted to determine and compare the value created by the two configurations of smart city's infrastructure. The simulation outlines the flow of data and their positive and negative feedback that reinforce and hinder the smart city value generation.

The results demonstrate the huge impact of the availability of data for App developers when crowdsourcing configuration is adopted. Furthermore, results unveil the potential in value generation of a crowdsourcing smart city platform configuration compared to a silos architecture.

The authors have proposed a comparison between two alternative smart city digital platform configurations. The paper seeks to test the magnitude of the pros and cons of a crowdsourcing approach in setting up a smart city digital platform. The paper provides new guidelines for improving the data management of smart cities.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

Using a mobile phone is increasingly becoming recognized as very dangerous while driving. With a smartphone, users feel connected and have access to information. The inability to access smartphone has become a phobia, causing anxiety and fear. The present study’s aims are as follows: first, quantify the association between nomophobia and road safety among motorists; second, determine a cut-off value for nomophobia that would identify poor road safety so that interventions can be designed accordingly.

Participants were surveyed online for nomophobia symptoms and a recent history of traffic contraventions. Nomophobia was measured using the nomophobia questionnaire (NMP-Q).

A total of 1731 participants responded to the survey; the mean age was 33 ± 12, and 43% were male. Overall, 483 (28%) [26–30%] participants received a recent traffic contravention. Participants with severe nomophobia showed a statistically significant increased risk for poor road safety odds ratios and a corresponding 95% CI of 4.64 [3.35-6.38] and 4.54 [3.28-6.29] in crude and adjusted models, respectively. Receiver operator characteristic (ROC)-based analyses revealed that NMP-Q scores of = 90 would be effective for identifying at risk drivers with sensitivity, specificity and accuracy of 61%, 75% and 72%, respectively.

Nomophobia symptoms are quite common among adults. Severe nomophobia is associated with poor road safety among motorists. Developing screening and intervention programs aimed at reducing nomophobia may improve road safety among motorists.

The authors investigate how COVID-19 has influenced the amount, type or topics of abuse that UK politicians receive when engaging with the public.

This work covers the first year of COVID-19 in the UK, from March 2020 to March 2021 and analyses Twitter abuse in replies to UK MPs. The authors collected and analysed 17.9 million reply tweets to the MPs. The authors present overall abuse levels during different key moments of the pandemic, analysing reactions to MPs by gender and the relationship between online abuse and topics such as Brexit, the government’s COVID-19 response and policies, and social issues.

The authors have found that abuse levels towards UK MPs were at an all-time high in December 2020. Women (particularly those from non-White backgrounds) receive unusual amounts of abuse, targeting their credibility and capacity to do their jobs. Similar to other large events like general elections and Brexit, COVID-19 has elevated abuse levels, at least temporarily.

Previous studies analysed abuse levels towards MPs in the run-up to the 2017 and 2019 UK General Elections and during the first four months of the COVID-19 pandemic in the UK. The authors compare previous findings with those of the first year of COVID-19, as the pandemic persisted, and Brexit was forthcoming. This research not only contributes to the longitudinal comparison of abuse trends against UK politicians but also presents new findings, corroborates, further clarifies and raises questions about the previous findings.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-07-2022-0392

This paper aims to examine the key challenges experienced and lessons learned when organizations undergo large-scale agile transformations and seeks to answer the question of how incumbent firms achieve agility at scale.

Building on a case study of a multinational corporation seeking to scale up agility, the authors combined 36 semistructured interviews with secondary data from the organization to analyze its transformation since the early planning period.

The results show how incumbent firms develop and successfully integrate agility-enhancing capabilities to sense, seize and transform in times of digital transformation and rapid change. The findings highlight how agility can be established initially at the divisional level, namely with a key accelerator in the form of a center of competence, and later prepared to be scaled up across the organization. Moreover, the authors abstract and organize the findings according to the dynamic capabilities framework and offer propositions of how companies can achieve organizational agility by scaling up agility from a divisional to an organizational level.

Along with in-depth insights into agile transformations, this article provides practitioners with guidance for developing agility-enhancing capabilities within incumbent organizations and creating, scaling and managing agility across them.

Examining the case of a multinational corporation's exceptional, pioneering effort to scale agility, this article addresses the strategic importance of agility and explains how organizational agility can serve incumbent firms in industries characterized by uncertainty and intense competition.

Collaborating with consumers during new product development can provide companies with significant benefits and competitive advantages. Although several studies have been conducted on the design of co-innovation platforms, there is still a need for a more comprehensive understanding of the co-innovation phenomenon. To address this gap, this research aims to identify the critical success factors of co-innovation platforms and provide an extensive analysis of the variables that determine their effectiveness.

This study presents a systematic literature review of co-innovation platforms based on an analysis of 89 articles published in 50 scholarly journals in the disciplines of information systems, marketing and business, covering the years from 2006 to 2022.

The review synthesizes the current state of scientific knowledge and groups prior studies thematically as critical success factors of co-innovation platforms. As a result, eight success factors have been identified in terms of quantity and quality of contributions. These factors include product involvement, perceived fairness, sense of community, interactive environment, employee involvement, participant diversity, assessment structure and task design.

The study consolidates existing research about the critical success of co-innovation platforms. It also provides a research framework that incorporates a diverse set of variables that can be used to assess co-innovation performance in future studies.

The e-commerce boom presents new challenges for last-mile delivery (LMD), which may be mitigated by new delivery technologies. This paper evaluates the impact of mobile parcel lockers (MPL) on costs and CO₂ equivalent (CO₂e) emissions in existing LMD networks, which include home delivery and shipments to stationary parcel lockers.

To describe customers’ preferences, we design a multinomial logit model based on recipients’ travel distance to pick-up locations and availability at home. Based on route cost estimation, we define the operating costs for MPLs. We devise a mathematical model with binary decision variables to optimize the location of MPLs.

Our study demonstrates that integrating MPLs leads to additional cost savings of 8.7% and extra CO₂e emissions savings of up to 5.4%. Our analysis of several regional clusters suggests that MPLs yield benefits in highly populous cities but may result in additional emissions in more rural areas where recipients drive longer distances to pick-ups.

This paper designs a suitable operating model for MPLs and demonstrates environmental and economic savings. Moreover, it adds recipients’ availability at home to receive parcels improving the accuracy of stochastic demand. In addition, MPLs are evaluated in the context of several regional clusters ranging from large cities to rural areas. Thus, we provide managerial guidance to logistics service providers how and where to deploy MPLs.

Despite the growing interest in the field of supply chain sustainability (SCS), little exploration of new theories exists. Therefore, this paper aims to introduce practice theories to SCS studies through a practice turn.

This is a conceptual paper in nature. Hence, based on theoretical arguments, the authors elaborate on how the practice turn can arise in the SCS field.

The theoretical elaboration is rooted in the understanding that sustainability is not limited to the materiality of environmental and social issues, as often observed. Instead, there is a need to include immaterial, emotional and intangible elements to better comprehend SCS practice. The authors argue that a continuum exists for a practice turn, including practice-based view, practice-based studies and critical practice theory.

The authors provide a research agenda with a comprehensive perspective of understanding the application and implications of practice theories to SCS.

The practice turn in SCS studies can support managers to better understand their practices not only through recognizing explicit activities but also mainly by reflecting on hidden elements that affect their performance.

SCS studies can better engage with grand challenges through a practice turn, which helps increase its contribution to solving social problems.

Unlike previous literature, the paper elaborates on how practice theories are powerful in supporting both scholars and practitioners in moving away from an extremely economic focus to genuinely embrace sustainability practice. In doing so, the practice turn appears as an important phase for SCS field maturity.

Due to e-commerce growth, technological advancements and environmental concerns, developing a more nuanced service portfolio has become a critical issue for last-mile logistics service providers. Concurrently, consumers are adopting new modes of consumption. This paper aims to investigate the potential for last-mile logistics service providers to act as intermediaries in access-based consumption and to revitalise their service offerings through product-service systems – a pioneering strategy not executed in the market yet.

This strategic customer foresight study uses a quantitative survey of 1,000 respondents and an online focus group comprising 10 early adopter consumers to investigate emerging last-mile service models. Potential service concepts were identified through the survey, and two distinct concepts were subsequently selected for evaluation and co-development within the focus group. The research was conducted in partnership with an SME logistics company in Finland.

The consumers expressed selective interest in access-based consumption related to the proposed offering of essential household goods. Young adults and consumers in early middle age living in the city centre emerged as the most potential user groups. Economic reasons and short-term needs were the primary motivations for adopting access-based consumption.

The study showed that engaging consumers in a customer foresight process is viable for SMEs innovating their offerings and demonstrates how the process works in practice.

Documented cases of customer integration into foresight processes are rare in earlier research, and this paper extends the knowledge base through a multidisciplinary examination of future consumer behaviour in the last-mile logistics domain. The paper also expands the limited literature on the role of logistics in access-based consumption.

Due to the unknown location, size and timing of disasters, the rapid response required by humanitarian operations (HO) faces high uncertainty and limited time to raise funds. These harsh realities make HO challenging. This study aims to systematically capture the complex dynamic relationships between operations in humanitarian settings.

To achieve this goal, the authors undertook a systematic review of the extant academic literature linking HO to system dynamics (SD) simulation.

The research reviews 88 papers to propose a taxonomy of different topics covered in the literature; a framework represented through a causal loop diagram (CLD) to summarise the taxonomy, offering a view of operational activities and their linkages before and after disasters; and a research agenda for future research avenues.

As the authors provide an adequate representation of reality, the findings can help decision makers understand the problems faced in HO and make more effective decisions.

While other reviews on the application of SD in HO have focused on specific subjects, the current research presents a broad view, summarising the main results of a comprehensive CLD.