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The introduction of artificial intelligence (AI) technology has had a substantial influence on the retail industry. However, AI adoption entails considerable responsibilities and risks for senior managers. In this study, the authors developed an evaluation and selection mechanism for successful AI technology adoption in the retail industry. The multifaceted measurement and identification of critical factors (CFs) can enable retailers to adopt AI technology effectively and maintain a sustainable competitive advantage.

The evaluation and adoption of organisational AI technology involve multifaceted decision-making for management. Therefore, the authors used the analytic network process to develop an AI evaluation framework for calculating the weight and importance of each consideration. An expert questionnaire survey was distributed to senior retail managers and 17 valid responses were obtained. Finally, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was used to identify CFs for AI adoption.

The results revealed five CFs for AI adoption in the retail industry. The findings indicated that after AI adoption, top retail management is most concerned with factors pertaining to business performance and minor concerned about the internal system's functional efficiency. Retailers pay more attention to technology and organisation context, which are matters under the retailers' control, than to external uncontrollable environmental factors.

The authors developed an evaluation framework and identified CFs for AI technology adoption in the retail industry. In terms of practical application, the results of this study can help AI service providers understand the CFs of retailers when adopting AI. Moreover, retailers can use the proposed multifaceted evaluation framework to guide their adoption of AI technology.

In large organizations, space planning relies on workgroup leaders to indicate spatial adjacency preferences. However, many factors affect workgroups’ adjacency preferences, and it is not clear how the choices are made. This paper aims to explore whether the adjacency preferences are influenced by the collaboration relationship or constrained by the organizational structure.

The authors studied a large company’s spatial adjacency planning with an in-depth analysis of its formal organizational structure and collaboration network. A sample of 183 managers was surveyed regarding groups with whom they want to be spatially adjacent and groups with whom they mostly interact. The data enabled us to test three structural factors related to adjacency preference: department affiliation, workgroup’s prestige and collaboration relation. The authors used the quadratic assignment procedure analysis to examine the correlations between network matrices.

The results suggest that department affiliation and collaboration relations are significantly correlated to adjacency preferences. The authors did not find evidence supporting the notion that a workgroup’s prestige affects the preference. Among the three factors, collaboration relation best predicts the preference, which echoes Pena et al.’s (1977) argument that space planners should look into how groups function, rather than merely following the organizational chart.

To the best of the authors’ knowledge, this research was the first to explore the choice of spatially adjacent workgroup through a detailed network analysis of the formal structure, work collaboration relations and other group-level characteristics. The findings have noteworthy cross-disciplinary implications, given that spatial proximity can be taken as a human resource management strategy to facilitate the overall interactions between workgroups.

Workplace space utilization data reveals patterns of space usage, the occupants’ presence and mobility within the office building. Nowadays, emerging technology such as smart sensors and devices can revolutionize the measurement of space utilization data, which is originally dominated by human observers with paper and pencil. However, these novel instruments are often used in an old fashion, which restricts the exploitation of their full potential. This study aims to shed new light on the benefits and limits of using smart technology in measuring space utilization data and discusses the challenges and opportunities in analyzing the data measured by smart sensors.

First, the literature regarding common methods and previous studies about office space utilization measurement was reviewed. Then, a data set consisting of space utilization data collected through Passive Infra-Red sensors for 35 meeting rooms in a bank building was carefully evaluated. Finally, the space utilization results based on methods calculated in two different granularities were compared.

The number of occupied hours calculated at an hour level was 1.32-hour larger than that calculated at a minute level. As both results show the concept of space utilization, which was the amount of time that the space was occupied, this paper revealed a gap between the two space utilization calculation methods and further discussed the issues and challenges for future space utilization data analysis and benchmarking.

To the best of the authors’ knowledge, this is the first study critically addressing office space utilization issues by comparing calculation methods in different granularity.

The purpose of this paper is to review the use of technologies for measuring space occupancy to guide the selection of appropriate tools for workplace post-occupancy evaluation (POE) studies. The authors focus on how actual space occupancy was measured in previous studies and the pros and cons of the different technologies and tools. This paper also addresses research gaps and directions for future research.

The space occupancy measures/tools are categorized based on the three types of technologies: environmental/ambient sensors, wearable sensors/smartphones and computer vision. A total of 50 studies are reviewed to identify the capabilities and limitations of these measurements.

Based on review results, the authors propose that although sensor technology can be a useful addition to the measures/tools list, a comprehensive review of the research goal, the occupants' behavior, and the environmental settings' characteristics should be conducted beforehand. Selecting appropriate technology is critical for collecting the proper behavioral data type, with a lower level of surveillance and increased validity.

This paper urges critical thinking about existing occupancy measures/tools across various fields, to inform the adoption and creation of new building occupancy measures. The knowledge of emerging sensor technology allows researchers to better study the temporal patterns of occupant behavior over extended periods and in a wide range of settings.

The purpose of this paper is to study whether the use of a shared study space played a role in shaping graduate students’ social networks by exploring how the copresence in space was related to the structure of friendship and advice networks. The authors first proposed two concepts of spatial copresence: measured spatial-temporal copresence and perceived copresence. The authors then examined the role of copresence through a case study of a shared study space occupied by 27 graduate students in the same department.

Copresence relations were first constructed through a six-month room access history data set and self-reported data to examine whether measured spatial-temporal copresence was consistent with perceived copresence. Friendship and advice network relations were then analyzed with copresence, social media connections, class project collaboration relations and social homophily (nationality, gender, cohort) through quadratic assignment procedure (QAP) and MQAP analysis.

The authors found that students who used the shared study space more often reported more friendship and advice ties. The perceived copresence and the measured spatial-temporal copresence were highly correlated. Copresence relations, as measured by survey and room access history, were both significantly correlated with advice relation, which was associated with perceived social support.

This study contributes to the debate about whether “space” continues to play significant roles in graduate students’ social networks in the context of flexible learning environments. The results also reveal new directions for research methods in studying spatial proximity in flexible settings.

This study aims to explore the association between employees' sense of belonging and their perception of workplace physical attributes at a corporate campus, and the workplace planning and management strategies to support employee sense of belonging for the long‐term competitiveness and performance of organizations.

The research site for this study is the corporate campus of a large information technology firm, Wipro Technologies, located in India. A workplace questionnaire designed by the International Workplace Studies Program (IWSP) at Cornell University was used. A total of 267 employees working at Wipro participated in this study.

Factor analysis of the survey items identified five underlying factors that are related to workplace physical environment. Statistically significant correlations were identified among employees' sense of belonging and these factors about work environment. A statistically significant model comprising four of the factors was developed to predict employees' sense of belonging.

The paper presents a new model to link sense of belonging to perception of workplace physical environment. Workplace planning and management implications were discussed for organizations to incorporate physical and spatial measures in their workplace to effectively enhance employees' sense of belonging.

Total quality management (TQM) and technology are fast becoming essential features of business strategy for the success of many leading organizations in the world. More and more companies are using technology and adapting TQM for sustaining competitiveness in the marketplace. TQM works well for internal integration of logistics companies and they can benefit from the use of technology, including information technology (IT), to gain further internal and external integration. Seeks to examine this issue.

This research examines the relationship between quality management practices, technology and performances of the logistics companies. The study seeks to gain insights from organizational variables and their effect on operational, quality, technology and overall business performance.

TQM and technology play important and complementing roles in improving the performance. The analysis shows that both high technology firms and high technology TQM firms perform significantly better than their low technology peers.

The use of IT is crucial in improving operational, quality and overall business performance. The information and management technologies strongly correlate to TQM and serve as an enabler to quality performance.

The use of technology assists logistics operations in many ways, such as cutting down information and processing lead‐time, improve efficiency and minimize errors to the minimum. Perhaps, the logistics companies should look at the long‐term benefits of technology and gradually engage its use to streamline their operations.

The results in this research provide recognition for the importance of technology in quality management in the logistics industry.

Purpose – The purpose of this chapter is to introduce new methods to behavioral research on group negotiation.

Design/methodology/approach – We describe three techniques from the field of Machine Learning and discuss their possible application to modeling dynamic processes in group negotiation: Markov Models, Hidden Markov Models, and Inverse Reinforcement Learning. Although negotiation research has employed Markov modeling in the past, the latter two methods are even more novel and cutting-edge. They provide the opportunity for researchers to build more comprehensive models and to use data more efficiently. To demonstrate their potential, we use scenarios from group negotiation research and discuss their hypothetical application to these methods. We conclude by suggestions for researchers interested in pursuing this line of work.

Originality/value – This chapter introduces methods that have been successfully used in other fields and discusses how these methods can be used in behavioral negotiation research. This chapter can be a valuable guide to researchers that would like to pursue computational modeling of group negotiation.

The purpose of this study is to develop theoretically grounded and empirically derived organizational security governance (OSG) objectives. Developing organizational security governance (OSG) objectives pose significant challenges for organizations considering the ever-increasing vulnerability from lack of or misuse of appropriate controls. In recent years, there have been several cases of colossal losses to businesses due to inadequate security governance measure. In many cases, organizations do not even know as to what their ISG objectives might be. Following an extensive empirical study, this paper proposes 6 fundamental and 17 means objectives for designing security governance. The objectives were developed from individual values of information technology and security executives across a wide range of firms. The study comprised 52 interview respondents across 9 firms, which resulted in 23 OSG objectives. Theoretically, the study was grounded in Catton’s (1959) value theory and Keeney’s (1992) value-focused thinking. The objectives provide a useful basis for strategic planning for information security governance.

This research is grounded in value-focused thinking methodology. Step 1: develop a comprehensive list of personal values underlying the problem being explored. The researcher undertakes extensive interviews, using relevant probes, to elicit underlying values of respondents. Step 2: change the values enlisted to a common form and convert them into objectives. The data collected in Step 1 is collated and presented in a common form, which enables cross-comparison and easy interpretation. Step 3: classify the objectives as means and fundamental for the decision context. Objectives are clustered into groups and then classified into fundamental and means.

This study uses a value-focused approach to develop OSG objectives. Incorporating individual values in developing governance objectives would facilitate alignment of individual and organizational values about OSG. This study proposes 6 fundamental and 17 means objectives for OSG. The study provides a comprehensive list of OSG that is rooted in values of stakeholders in an organization.

The main contributions study can be classified in two categories. First, it represents a collective set of OSG objectives which touch upon technical, formal, informal, moral and ethical dimensions of governance. This is a unique, synthesized and cohesive framework for OSG, which incorporates several aspects of OSG into one platform, thus allowing the development of a comprehensive security management program. Second, some of the objectives developed in this research (“establish corporate control strategy”, “establish punitive structure”, “establish clear control development process”, “ensure formal control assessment functionality” and “maximize group cohesiveness”) have not been emphasized enough in security governance literature.

The purpose of this research is to improve efficiency of the traditional scheduling methods and explore a more effective approach to solving the scheduling problem in supply networks with genetic algorithms (GAs).

This paper develops two methods with GAs for detailed production scheduling in supply networks. The first method adopts a GA to job shop scheduling in any node of the supply network. The second method is developed for collective scheduling in an industrial cluster using a modified GA (MGA). The objective is to minimize the total makespan. The proposed method was verified on some experiments.

The suggested GAs can improve detailed production scheduling in supply networks. The results of the experiments show that the proposed MGA is a very efficient and effective algorithm. The MGA creates the manufacturing schedule for each factory and transport operation schedule very quickly.

For future research, an expert system will be adopted as an intelligent interface between the MRPII or ERP and the MGA.

From the mathematical point of view, a supply network is a digraph, which has loops and therefore the proposed GAs take into account loops in supply networks. The MGA enables dividing jobs between factories. This algorithm is based on operation codes, where each chromosome is a set of four‐positions genes. This encoding method includes both manufacture operations and long transport operations.