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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 study aimed to investigate the impact of social networks on learning requests by Arab undergraduate EFL students.

Furthermore, both types of requests (i.e. direct and indirect) have been compared under the same condition. A total of 40 Saudi EFL learners participated in this study. Their scores in the pre-test and post-test were compared.

Results of the post-tests indicated that the experimental groups' performance significantly outscored the control groups. However, no significant difference was reported between the two experimental groups (i.e. direct and indirect).

Requests, as a component of pragmatics, have been investigated extensively in the literature to aid second/foreign language learning. However, little is known about the manner by which social networks could foster learning 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.

This research focuses on the distinct group of high-status employees commonly referred to as workplace vigilantes, and conceptually investigates how unethical requests by these individuals impact the behavior and attitude of other employees over time using the social identity theory.

For developing a conceptual model, literature from the domains of social identity, organizational behavior and general management was searched through Google Scholar. To search the literature, some key terms such as “unethical activities”, “Islamic work ethics” and “social identity” were searched and analyzed.

Using the social identity theory, a conceptual process model is developed which suggests that when high-status employees propose unethical requests to employees, individuals with high morality are likely to refuse those unethical requests to protect their self-categorizations. However, taking the unfair advantage of their illegitimate powers, high-status employees are likely to eventually make wrong judgments and give unnecessary punishments to moral employees. It is further argued that consistent victimization is likely to negatively impact the social identity of such employees and leads to irritability in moral employees, particularly when such individuals are unable to get the requisite social support from their leaders.

While a considerable body of literature has focused on the antecedents and consequences of intense unethical business practices and the crucial role of leaders in such activities, limited attention has been given to the role of other employees and how they engage in mild unethical misconduct regularly, which is the key focus of this research. The novel conceptual framework needs to be tested in diverse contexts for further development and validation.

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.

The study examines how the technical features and associated social practices of an anonymous, text-based online forum intended for young people make it a unique platform for acquiring and sharing health information among peers.

The features and content of a young people's section of a popular Finnish discussion forum were examined with a focus on health-related threads. Observational notes and thread content were analysed with a focus on the forum's affordances for health information practices.

The findings indicate that the forum's affordances including anonymity, persistence, searchability, cohesion and tolerance enabled the pooling of peer experiences, opinions and experience-based advice on health, rather than sharing factual information or embracing reciprocal discussion. As such, instead of competing for a cognitive authority position with medical authorities or offering emotional support like tight online support communities, the anonymous forum served as a platform for young people to gain information on others' experiences and opinions on sensitive, mundane and disnormative health issues and for reflecting their own lived experiences to those of others.

The study is original in its approach to examining the affordances of an online platform for health information practices. It helps in understanding young people's ways of using different resources to meet their diverse health information needs and the value of gaining access to experiential health information.

The purpose of this article is to study what platform-related user factors influence the employment potential of a lean platform for self-employed professionals.

The article employs the system data of a Dutch platform firm, which include consumers looking for painters (N = 17,224) and self-employed painters (N = 1,752) who pursue client acquisition by submitting proposals (N = 101,974). This data is analysed using non-parametric tests.

Study of this platform shows that the platform functions as a channel of acquisition for self-employed professionals. This lean platform enables matching of information of supply and demand, thereby facilitating processes of acquisition. The number of competitors, distance to a potential job and non-standard proposals are statistically significant factors that influence whether a consumer is interested in a proposal. Effect sizes are very small.

This platform is a two-way market for information about service jobs, which excludes a price setting mechanism. The findings of this study cannot be generalized to other forms of platforms.

The market for service professionals is very local; therefore, the platform firm may alter the algorithm to accommodate this. Self-employed professionals should approach using the platform in the same way as normal forms of acquisition.

This particular type of two-sided market is an extension of regular forms of acquisition by creating “weak ties” through the platform.

The article uses a unique data set to study the impact and limitations of digitalization of the (labour) market for service professionals.

This study aims to analyze how different room-committing practices affect the occupancy and profitability of hotels and it critically reviews the role of minimum-length-of-stay (MLOS) requirements given these findings.

The approach uses statistical analysis of simplified contexts to develop understanding, and simulations of more complex situations to confirm the relevance in realistic contexts.

The study demonstrates that proper solutions of the room-committing problem improve occupancy and profitability, in particular, for hotels working in high-season and high-occupancy situations. Smart committing algorithms diminish the role of MLOS requirements. More demand can be accepted without sacrificing late-arriving long reservations.

To the best of the authors’ knowledge, this work, building upon a previous one cited in this paper, is the first to rigorously study the room-committing problem and to demonstrate its relevance in practical situations and its implications on MLOS rules.