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This study assessed two technology systems aimed at enabling a man with intellectual disability, blindness, deafness and motor and tactile discrimination problems to make verbal requests through simple one-hand signs.

The study was conducted according to an ABAB design. During the B (intervention) phases, the man used the two systems, which included (1) nine mini recording devices fixed on the man’s clothes or wheelchair (i.e. in positions the man touched with his sign movements) and (2) nine tags with radio frequency identification codes (fixed at approximately the same positions as the mini recording devices) and a dedicated tag reader, respectively. Making a sign (i.e. touching a recording device or reaching a tag) led to the verbalization of the request related to that sign.

During baseline, the mean frequency of signs/requests made was below 2 per session, and only some of those requests were identified/satisfied. During the intervention, the mean frequency of requests made and satisfied was about 10 per session with each of the systems.

The results, which are to be taken with caution given the preliminary nature of the study, seem to suggest that the systems can help translate simple signs into verbal requests.

This paper aims to develop predictive methods, based on recurrent neural networks, useful to support facility managers in building maintenance tasks, by collecting information coming from a computerized maintenance management system (CMMS).

This study applies data-driven and text-mining approaches to a CMMS data set comprising more than 14,500 end-users’ requests for corrective maintenance actions, collected over 14 months. Unidirectional long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) recurrent neural networks are trained to predict the priority of each maintenance request and the related technical staff assignment. The data set is also used to depict an overview of corrective maintenance needs and related performances and to verify the most relevant elements in the building and how the current facility management (FM) relates to the requests.

The study shows that LSTM and Bi-LSTM recurrent neural networks can properly recognize the words contained in the requests, thus correctly and automatically assigning the priority and predicting the technical staff to assign for each end-user’s maintenance request. The obtained global accuracy is very high, reaching 93.3% for priority identification and 96.7% for technical staff assignment. Results also show the main critical building elements for maintenance requests and the related intervention timings.

This work shows that LSTM and Bi-LSTM recurrent neural networks can automate the assignment process of end-users’ maintenance requests if trained with historical CMMS data. Results are promising; however, the trained LSTM and Bi-LSTM RNN can be applied only to different hospitals adopting similar categorization.

The data-driven and text-mining approaches can be integrated into the CMMS to support corrective maintenance management by facilities management contractors, i.e. to properly and timely identify the actions to be carried out and the technical staff to assign.

The improvement of the maintenance of the health-care system is a key component of improving health service delivery. This work shows how to reduce health-care service interruptions due to maintenance needs through machine learning methods.

This study develops original methods and tools easily integrable into IT workflow systems (i.e. CMMS) in the FM field.

A deteriorating security situation and an increased need for defence equipment calls for new forms of collaboration between Armed Forces and the defence industry. This paper aims to investigate the ways in which the accelerating demand for increased security of supply of equipment and supplies to the Armed Forces requires adaptability in the procurement process that is governed by laws on public procurement (PP).

This paper is based on a review of current literature as well as empirical data obtained through interviews with representatives from the Swedish Defence Materiel Administration and the Swedish defence industry.

Collaboration with the globalized defence industry requires new approaches, where the PP rules make procurement of a safe supply of defence equipment difficult.

The study's empirical data and findings are based on the Swedish context. In order to draw more general conclusions in a defence context, the study should be expanded to cover more nations.

The findings will enable the defence industry and the procurement authorizations to better understand the requirements of Armed Forces, and how to cooperate under applicable legal and regulatory requirements.

The paper extends the extant body of academic knowledge of the security of supply into the defence sector. It serves as a first step towards articulating a call for new approaches to collaboration in defence supply chains.

The purpose of this study was to create an air movement operations planning model to rapidly generate air mission request (AMR) assignment and routing courses of action (COA) in order to minimize unsupported AMRs, aircraft utilization and routing cost.

In this paper, the US Army Aviation air movement operations planning problem is modeled as a mixed integer linear program (MILP) as an extension of the dial-a-ride problem (DARP). The paper also introduces a heuristic as an extension of a single-vehicle DARP demand insertion algorithm to generate feasible solutions in a tactically useful time period.

The MILP model generates optimal solutions for small problems (low numbers of AMRs and small helicopter fleets). The heuristic generates near-optimal feasible solutions for problems of various sizes (up to 100 AMRs and 10 helicopter team fleet size) in near real time.

Due to the inability of the MILP to produce optimal solutions for mid- and large-sized problems, this research is limited in commenting on the heuristic solution quality beyond the numerical experimentation. Additionally, the authors make several simplifying assumptions to generalize the average performance and capabilities of aircraft throughout a flight.

This research is the first to solve the US Army Aviation air movement operations planning problem via a single formulation that incorporates multiple refuel nodes, minimization of unsupported demand by priority level, demand time windows, aircraft team utilization penalties, aircraft team time windows and maximum duration and passenger ride time limits.

This study aims to fill a distinct gap in the literature on disability-assistance animals (disability-AAs) and inclusive employment by investigating human resource (HR) practitioners’ perceptions of disability-AAs in the staffing process and workplace. HR practitioners play a critical role in accommodation and inclusion, yet their experiences and insights have been largely ignored in prior research.

This study used a phenomenological approach, drawing on signaling theory and employability constructs, to explore insights from 17 HR practitioners’ experiences with assistance animals in the workplace.

The potential for unconscious bias in employment practices was found, as well as a significant percentage of practitioners who were unprepared to handle animal accommodations. First, the potential development of a positive stereotype bias suggests all genuine assistance animals are high functioning. Second, the assumption that employees’ assistance animal requests for invisible disabilities without previous disclosure are presumed fraudulent until proven valid.

As a qualitative study, findings from this study are not generalizable to a larger population but may be transferable to similar employment contexts.

This study extends knowledge from previous studies, which focused predominately on insights from disabled individuals, animal trainers and therapists, to the HR practitioner domain in creating a more inclusive work environment. Findings from this study suggest the need to improve education about disability-AAs and the potential for unconscious bias for HR practitioners and hiring managers when accommodating requests, particularly when those assistance animals are not described as high functioning.

The purpose of this study is to situate information and communications technology (ICT) “transparency reports” within the theoretical framework of corporate social responsibility (CSR) reporting. The self-denominated transparency report serves a dual purpose of highlighting an ICT company’s socially responsible behavior while also holding government agencies accountable for surveillance and requests for user data. Drawing on legitimacy theory, neo-institutional theory and stakeholder theory, this exploratory study examines how ICT companies are implementing industry-specific voluntary disclosures as a form of CSR.

A content analysis of ICT voluntary nonfinancial reporting (N = 88) was used to identify motivating principles, the company positioning to stakeholders, the relevant publics and intended audience of these disclosures and the communication strategy used to engage primary stakeholders.

Key findings suggest that most ICT companies used transparency reporting to engage consumers/users as their primary stakeholders and most used a stakeholder information strategy. A majority of ICT companies signaled value-driven motives in their transparency reports while also positioning the company to stakeholders as a protector of user data and advocate for consumer rights.

This study enriches the literature on CSR communication strategies and reporting practices by extending it to an underdeveloped topic of study: novel voluntary disclosures as CSR activities of prominent ICT companies (i.e. “Big Tech”). These polyphonic reports reflect varied motives, varied positioning and varied stakeholders. For market-leading companies, transparency reporting can serve to legitimize existing market power. And for midsize and emerging companies, transparency reporting can be used to signal adherence to industry norms – set by market-leading companies.

Conflict and violence are the main drivers of globally escalating humanitarian needs. Local grassroots initiatives are pivotal in distributing humanitarian supplies in the acute response phase until more established humanitarian aid organizations can enter. Nevertheless, scant research exists regarding the role of grassroots associations in providing humanitarian assistance during a military conflict. The purpose of this paper is to understand the role of grassroots associations and identify important themes for effective operations.

This paper adopts a case-study approach of three Ukrainian grassroots associations that began operating in the immediate days of the full-scale invasion of Ukraine. The findings are based on analyzing primary sources, including interviews with Ukrainian volunteers, and are supported by secondary sources.

Grassroots associations have local contacts and a contextual understanding of population needs and can respond more rapidly and effectively than large intergovernmental agencies. Four critical themes regarding the operations of grassroots associations emerged: information management, inventory management, coordination and performance measurement. Grassroots humanitarian response operations during conflict are challenged by personal security risks, the unpredictability of unsolicited supplies, emerging volunteer roles, dynamic transportation routes and shifting demands.

Grassroots responses are central to humanitarian responses during the acute phase of a military conflict. By examining the operations of grassroots associations in the early months of the 2022 war in Ukraine, the authors provide a unique perspective on humanitarian logistics. Nonetheless, more inclusive models of humanitarian responses are needed to harness the capacities and resilience of grassroots operations in practice.

The purpose of this study was to investigate a framework for the implementation of freedom of information (FOI) legislation in South Africa, against Article 19’s nine principles of FOI legislation.

This qualitative study used semi-structured interviews to collect data from six experts selected by means of the snowball sampling technique and content analysis. The study used a modified Delphi design consisting of two rounds of interviews.

The results showed that little effort is made by government officials to demonstrate commitment to the implementation of FOI legislation.

The passing of FOI is expected to reduce corruption, increase public participation, reduce the level of secrecy and increase transparency and openness. This is not the case as the implementation of this socioeconomic right in South Africa is faced by numerous challenges, such as a lack of political will, secrecy laws providing for the opposite of what the FOI legislation seeks to achieve, poor legislative interpretation and a lack of clear policies. The study proposes a framework aimed at addressing these challenges.

The study provides a framework for the implementation of FOI legislation. The framework was developed under the guidance of Article 19 principles of freedom of information legislation.

This research aims to examine and understand the rationales and modalities behind the use of disclosure before, during and after a corporate governance scandal involving CPA Australia (CPAA).

Data beyond CPAA's annual reports were collected, such as news articles, media releases, an independent review panel (IRP) report, and the Chief Operating Officer's letter to members. These disclosures were manually coded and analysed through the word counts and word trees in NVivo. This study also relied on Norbert Elias' conceptual tool of power games among networks of actors – figurations – to model the scandal as a power game between the old Board, the press, concerned members, the IRP and the new Board. This study analysed the data to reveal a collective and in fieri power balance that changed with the phases of the scandal.

A mix of voluntary, involuntary, requested and absent disclosures was important in triggering, managing and ending the CPAA scandal. Moreover, communication and disclosure fulfilled a constitutive role since both: mobilised actors, enabled coordination among actors, contributed to pursuing shared goals and influenced power balances. Such a constitutive role was at the heart of the ability of coalitions of figurations to challenge and restore the powerful status quo.

This research introduces to accounting studies the collective and in fieri dimensions of power from figurational theory. Moreover, the research sheds new light on using voluntary, involuntary, requested and absent disclosures before, during and after a corporate crisis.

This study aims to develop a model based on the financial variables for better accuracy of financial distress prediction on the sample of private French, Spanish and Italian firms. Thus, firms in financial difficulties could timely request for troubled debt restructuring (TDR) to continue business.

This study used a sample of 312 distressed and 312 non-distressed firms. It includes 60 French, 21 Spanish and 231 Italian firms in both distressed and non-distressed groups. The data are extracted from the ORBIS database. First, the authors develop a new model by replacing a ratio in the original Z”-Score model specifically for financial distress prediction and estimate its coefficients based on linear discriminant analysis (LDA). Second, using the modified Z”-Score model, the authors develop a firm TDR probability index for distressed and non-distressed firms based on the logistic regression model.

The new model (modified Z”-Score), specifically for financial distress prediction, represents higher prediction accuracy. Moreover, the firm TDR probability index accurately depicts the probabilities trend for both groups of distressed and non-distressed firms.

The findings of this study are conclusive. However, the sample size is small. Therefore, further studies could extend the application of the prediction model developed in this study to all the EU countries.

This study has important practical implications. This study responds to the EU directive call by developing the financial distress prediction model to allow debtors to do timely debt restructuring and thus continue their businesses. Therefore, this study could be useful for practitioners and firm stakeholders, such as banks and other creditors, and investors.

This study significantly contributes to the literature in several ways. First, this study develops a model for predicting financial distress based on the argument that corporate bankruptcy and financial distress are distinct events. However, the original Z”-Score model is intended for failure prediction. Moreover, the recent literature suggests modifying and extending the prediction models. Second, the new model is tested using a sample of firms from three countries that share similarities in their TDR laws.