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The unstable labor productivity and periodic planning method cause barriers to improving construction logistics management. This paper aims to explore a demand-driven mechanism for efficient construction logistics planning to record the material consumption, report the real-time demand and trigger material replenishment from off-site to on-site, which is aided by Building Information Modeling (BIM) and the Kanban technique.

This paper follows the design science research (DSR) principles to propose a system of designing and applying Kanban batch with 4D BIM for construction logistics planning and monitoring. Prototype development with comparative simulation experiments of a river remediation project is conducted to analyze the conventional and Kanban-triggered supply. Two-staged industrial interviews are conducted to guide and evaluate the system design.

The proposed BIM-enabled Kanban system enables construction managers and suppliers to better set integrated on- and off-site targets, report real-time demands and conduct collaborative planning and monitoring. The simulation results present significant site storage and schedule savings applying the BIM-enabled Kanban system. Feedback and constructive suggestions from practitioners are collected via interviews and analyzed for further development.

This paper brings to the limelight the benefits of implementing BIM-enabled demand-driven replenishment to remove waste from the material flow. This paper combines lean production theory with advanced information technology to solve construction logistics management problems.

A principle prerequisite for designing and constructing an underground structure is to estimate the subsurface's properties and obtain a realistic picture of stratigraphy. Obtaining direct measure of these values in any location of the built environment is not affordable. Therefore, any evaluation is afflicted with uncertainty, and we need to combine all available measurements, observations and previous knowledge to achieve an informed estimate and quantify the involved uncertainties. This study aims to enhance the geotechnical surveys based on a spatial estimation of subsoil to customised data structures and integrating the ground models into digital design environments.

The present study's objective is to enhance the geotechnical surveys based on a spatial estimation of subsoil to customised data structures and integrating the ground models into digital design environments. A ground model consisting of voxels is developed via Revit-Dynamo to represent spatial uncertainties employing the kriging interpolation method. The local arrangement of new surveys are evaluated to be optimised.

The visualisation model's computational performance is modified by using an octree structure. The results show that it adapts the structure to be modelled more efficiently. The proposed concept can identify the geological models' risky locations for further geological investigations and reveal an optimised experimental design. The modifications criteria are defined in global and local considerations.

It provides a transparent and repeatable approach to construct a spatial ground model for subsequent experimental or numerical analysis. In the first attempt, the ground model was discretised by a grid of voxels. In general, the required computing time primarily depends on the size of the voxels. This issue is addressed by implementing octree voxels to reduce the computational efforts. This applies especially to the cases that a higher resolution is required. The investigations using a synthetic soil model showed that the developed methodology fulfilled the kriging method's requirements. The effects of variogram parameters, such as the range and the covariance function, were investigated based on some parameter studies. Moreover, a synthetic model is used to demonstrate the optimal experimental design concept. Through the implementation, alternative locations for new boreholes are generated, and their uncertainties are quantified. The impact of the new borehole on the uncertainty measures are quantified based on local and global approaches. For further research to identify the geological models' risky spots, the development of this approach with additional criteria regarding the search neighbourhood and consideration of barriers and trends in real cases (by employing different interpolation methodologies) should be considered.

Preferences of both Alzheimer patients and their spouse caregivers are related to a willingness-to-pay (WTP) measure which is used to test for the presence of mutual (rather than conventional unilateral) altruism.

Contingent valuation experiments were conducted in 2000–2002, involving 126 Alzheimer patients and their caregiving spouses living in the Zurich metropolitan area (Switzerland). WTP values for three hypothetical treatments of the demented patient were elicited. The treatment Stabilization prevents the worsening of the disease, bringing dementia to a standstill. Cure restores patient health to its original level. In No burden, dementia takes its normal course while caregiver’s burden is reduced to its level before the disease.

The three different types of therapies are reflected in different WTP values of both caregivers and patients, suggesting that moderate levels of Alzheimer’s disease still permit clear expression of preference. According to the WTP values found, patients do not rank Cure higher than No burden, implying that their preferences are entirely altruistic. Caregiving spouses rank Cure before Burden, reflecting less than perfect altruism which accounts for some 40 percent of their total WTP. Still, this constitutes evidence of mutual altruism.

The evidence suggests that WTP values reflect individuals’ preferences even in Alzheimer patients. The estimates suggest that an economically successful treatment should provide relief to caregivers, with its curative benefits being of secondary importance.

Automatic evaluation of job interviews has become an alternative for assessing interviewees. Therefore, questions arise regarding applicant reactions and behavior when algorithms automatically evaluate applicants' interview responses. This study tests arguments from previous research suggesting that applicants whose interviews will be automatically evaluated may use less impression management (IM), but could react more negatively to the interview.

Participants (N = 124; primarily German students) took part in an online mock interview where they responded to interview questions via voice recordings (i.e. an asynchronous interview). Prior to the interview, half of them were informed that their answers would be evaluated automatically (vs by a human rater). After the interviews, participants reported their honest and deceptive IM behavior as well as their reactions to the interview.

Participants in the automatic evaluation condition engaged in less deceptive IM, felt they had fewer opportunities to perform during the interview, and provided shorter interview answers.

The findings of this study suggest a trade-off between IM behavior and applicant reactions in technologically advanced interviews. Furthermore, the results indicate that automatically evaluated interviews might affect interview validity (e.g. because of less deceptive IM) and influence interviewees' response behavior.

Hiring managers might hope that automatically evaluated interviews decrease applicants' use of deceptive IM. However, the results also challenge organizations to pay attention to negative effects of automatic evaluation on applicant reactions.

This study is the first empirical study investigating the impact of automatically evaluated interviews on applicant behavior and reactions.

The technological evolution of job interviews continues as highly automated interviews emerge as alternative approaches. Initial evidence shows that applicants react negatively to such interviews. Additionally, there is emerging evidence that contextual influences matter when investigating applicant reactions to highly automated interviews. However, previous research has ignored higher-level organizational contexts (i.e. which kind of organization uses the selection procedure) and individual differences (e.g. work experience) regarding applicant reactions. The purpose of this paper is to investigate applicant reactions to highly automated interviews for students and employees and the role of the organizational context when using such interviews.

In a 2 × 2 online study, participants read organizational descriptions of either an innovative or an established organization and watched a video displaying a highly automated or a videoconference interview. Afterwards, participants responded to applicant reaction items.

Participants (n=148) perceived highly automated interviews as more consistent but as conveying less social presence. The negative effect on social presence diminished organizational attractiveness. The organizational context did not affect applicant reactions to the interview approaches, whereas differences between students and employees emerged but only affected privacy concerns to the interview approaches.

The organizational context seems to have negligible effects on applicant reactions to technology-enhanced interviews. There were only small differences between students and employees regarding applicant reactions.

In a tense labor market, hiring managers need to be aware of a trade-off between efficiency and applicant reactions regarding technology-enhanced interviews.

This study investigates high-level contextual influences and individual differences regarding applicant reactions to highly automated interviews.

This study aims to provide probable future developments in the form of holistic scenarios for business negotiations. In recent years, negotiation research did not put a lot of emphasis on external changes. Consequently, current challenges and trends are scarcely integrated, making it difficult to support negotiation practice perspectively.

This paper applies the structured, multi-method approach of scenario analysis. To examine the future space of negotiations, this combines qualitative and quantitative measures to base our analysis on negotiation experts’ assessments, estimations and visions of the negotiation future.

The results comprise an overview of five negotiation scenarios in the year 2030 and of their individual drivers. The five revealed scenarios are: digital intelligence, business as usual, powerful network – the route to collaboration, powerful network – the route to predominance and system crash.

The scenario analysis is a suitable approach that enables to relate various factors of the negotiation environment to negotiations themselves and allows an examination of future changes in buyer–seller negotiations and the creation of possible future scenarios. The identified scenarios provide an orientation for business decisions in the field of negotiation.

Over the past decade, the rapid evolution of social media has impacted the field of human resource management in numerous ways. In response, scholars and practitioners have sought to begin an investigation of the myriad of ways that social media impacts organizations. To date, research evidence on a range of HR-related topics are just beginning to emerge, but are scattered across a range of diverse literatures. The principal aim of this chapter is to review the current literature on the study of social media in HRM and to integrate these disparate emerging literatures. During our review, we discuss the existent research, describe the theoretical foundations of such work, and summarize key research findings and themes into a coherent social media framework relevant to HRM. Finally, we offer recommendations for future work that can enhance knowledge of social media’s impact in organizations.

The growing uses of digital technologies have been creating several new business opportunities. Recently, a new concept has emerged in the literature, the “digital entrepreneurship ecosystem” (DEE). However, it has been empirically understudied. Thus, this study aims to analyze the influence of DEE on entrepreneurial activity. The meta-organization theory was used as a perspective of analysis. A quantitative methodology was applied in a sample that includes data from 28 European countries through the generalized method of moments. It was concluded that the DEE pillars, informal and formal institutions, market conditions, physical infrastructure, human capital and talent and networking and support positively influence entrepreneurial activity. It was also found that the variables knowledge, creation and dissemination and finance have a negative impact on entrepreneurial activity. Several theoretical and empirical contributions are also left for the various stakeholders. The present study is original, as no known studies analyze the influence of DEE on entrepreneurial activity in European countries from the perspective of meta-organization theory.