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In recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) techniques. This paper aims to provide a significant reference for researchers and engineers in robotic mapping.

This paper focused on the research state of LiDAR-based SLAM for robotic mapping as well as a literature survey from the perspective of various LiDAR types and configurations.

This paper conducted a comprehensive literature review of the LiDAR-based SLAM system based on three distinct LiDAR forms and configurations. The authors concluded that multi-robot collaborative mapping and multi-source fusion SLAM systems based on 3D LiDAR with deep learning will be new trends in the future.

To the best of the authors’ knowledge, this is the first thorough survey of robotic mapping from the perspective of various LiDAR types and configurations. It can serve as a theoretical and practical guide for the advancement of academic and industrial robot mapping.

Due to its ability to support well-informed decision-making, business intelligence (BI) has grown in popularity among executives across a range of industries. However, given the volume of data collected in health-care organizations, there is a lack of exploration concerning its implementation. Consequently, this research paper aims to investigate the key factors affecting the acceptance and use of BI in healthcare organizations.

Leveraging the theoretical lens of the “unified theory of acceptance and use of technology” (UTAUT), a study framework was proposed and integrated with three context-related factors, including “rational decision-making culture” (RDC), “perceived threat to professional autonomy” (PTA) and “medical–legal risk” (MLR). The variables in the study framework were categorized as follows: information systems (IS) perspective; organizational perspective; and user perspective. In Jordan, 434 healthcare professionals participated in a cross-sectional online survey that was used to collect data.

The findings of the “structural equation modeling” revealed that professionals’ behavioral intentions toward using BI systems were significantly affected by performance expectancy, social influence, facilitating conditions, MLR, RDC and PTA. Also, an insignificant effect of PTA on PE was found based on the results of statistical analysis. These variables explained 68% of the variance (R²) in the individuals’ intentions to use BI-based health-care systems.

To promote the acceptance and use of BI technology in health-care settings, developers, designers, service providers and decision-makers will find this study to have a number of practical implications. Additionally, it will support the development of effective strategies and BI-based health-care systems based on these study results, attracting the interest of many users.

To the best of the author’s knowledge, this is one of the first studies that integrates the UTAUT model with three contextual factors (RDC, PTA and MLR) in addition to examining the suggested framework in a developing nation (Jordan). This study is one of the few in which the users’ acceptance behavior of BI systems was investigated in a health-care setting. More specifically, to the best of the author’s knowledge, this is the first study that reveals the critical antecedents of individuals’ intention to accept BI for health-care purposes in the Jordanian context.

The ongoing paradigm shift in the energy sector holds paramount implications for the realization of the sustainable development goals, encompassing critical domains such as resource optimization, environmental stewardship and workforce opportunities. Concurrently, this transformative trajectory within the power sector possesses a dual-edged nature; it may ameliorate certain challenges while accentuating others. In light of the burgeoning research stream on open innovation, this study aims to examine the intricate dynamics of knowledge-based industry-university-research networking, with an overarching objective to elucidate and calibrate the equilibrium of ambidextrous innovation within power systems.

The authors scrutinize the role of different innovation organizations in three innovation models: ambidextrous, exploitative and exploratory, and use a multiobjective decision analysis method-entropy weight TOPSIS. The research was conducted within the sphere of the power industry, and the authors mined data from the widely used PatSnap database.

Results show that the breadth of knowledge search and the strength of an organization’s direct relationships are crucial for ambidextrous innovation, with research institutions having the highest impact. In contrast, for exploitative innovation, depth of knowledge search, the number of R&D patents and the number of innovative products are paramount, with universities playing the most significant role. For exploratory innovation, the depth of knowledge search and the quality of two-mode network relations are vital, with research institutions yielding the best effect. Regional analysis reveals Beijing as the primary hub for ambidextrous and exploratory innovation organizations, while Jiangsu leads for exploitative innovation.

The study offers valuable implications to cope with the dynamic state of ambidextrous innovation performance of the entire power system. In light of the findings, the dynamic state of ambidextrous innovation performance within the power system can be adeptly managed. By emphasizing a balance between exploratory and exploitative strategies, stakeholders are better positioned to respond to evolving challenges and opportunities. Thus, the study offers pivotal guidance to ensure sustained adaptability and growth in the power sector’s innovation landscape.

The primary originality is to extend and refine the theoretical understanding of ambidextrous innovation within power systems. By integrating several theoretical frameworks, including social network theory, knowledge-based theory and resource-based theory, the authors enrich the theoretical landscape of power system ambidextrous innovation. Also, this inclusive examination of two-mode network structures, including the interplay between knowledge and cooperation networks, unveils the intricate interdependencies between these networks and the ambidextrous innovation of power systems. This approach significantly widens the theoretical parameters of innovation network research.

This paper aims to investigate the prevalence of corruption in Ghana to understand how and why it has turned public procurement into a mere money-making scheme instead of a means to provide needed public goods and services.

The study focuses on Ghana as a case study and mobilizes the monetization of politics lenses. Data are collected via interviews with key officials across the procurement sector (including the government, donors and civil society), documents, documentaries and news articles.

The findings suggest that the increasing costs of elections and political financing coupled with the costs of vote-buying, which has become informally institutionalized, intensify corruption practices and, consequently, turns public procurement into a mere source of cash for political ends. Political appointments and legalized loopholes facilitate this by helping to nullify the safeguard accounting and other control institutions are designed to provide. Likewise, enduring poverty and rising inequality “force” citizens into a vote-buying culture which distorts democratic premises that may drive out unscrupulous politicians; thus, perpetuating capture schemes. Civil society's efforts to remedy these have had little success, and corruption and inequality remain rife.

The main practical implication of the study lies in the need for a gradual demonetization of elections, and the consideration of the fundamental function of public procurement as a policy instrument embedded in economic, social, cultural and environmental plans. Additionally, given the connectedness of the various corruption issues raised, a comprehensive system-based approach in dealing with them would be more effective than a piecemeal approach targeting each issue/problem in isolation.

While extant literature has examined the issue of endemic corruption in developing countries using state capture, few have attempted to explain why it remains enduring, particularly in public procurement. This study, therefore, contributes to the literature on corruption and state capture theoretically and empirically by drawing on monetization of politics from political science to explain why corruption and state capture endure in certain contexts (with Ghana as an illustrative example) which reduce public procurement to a cash-milking scheme.

This paper aims to apply a mixed-methods approach to redefine essential work skill sets, propose components of these skill sets that are necessary for workers in the contexts of emerging market economies (EMEs), examine potential selection tools that should be applied across firms and explain implications for the labour process theory (LPT) of work.

During the first phase of this study, qualitative data was collected from methods, including semi-structured interviews with human resources (HR) experts from the Personnel Management Association of Thailand, top and HR executives and managers of firms across industries in Thailand. Non-participant observations were made during the pre-interview, interview and post-interview stages. Analysis of archival documents and Web-based resources was then conducted. The authors used the qualitative data obtained from the first phase to develop survey instruments for conducting quantitative research during the second phase of this study.

The empirical findings demonstrate that essential work skill sets are “soft skills” that help workers survive and thrive in the business arena in EMEs. These essential work skill sets have implications for the LPT of work in that they play an important role in transferring the power of negotiation from employers to workers in the labour market. Essential work skill sets here can be divided into eight skill domains: (1) ideation and system thinking, (2) information and digital literacy, (3) social skills with appreciation for diversity and inclusion, (4) communication and language, (5) creativity and innovation, (6) emotional quotient (EQ) for self-management and development, (7) growth/outward mindset and (8) cognitive skills for the job role. Within each essential work skill set, there are several skills that workers in the current world of work need to possess (42 skills in total). Additionally, potential selection tools include behavioural observation, behavioural-based interviews, STAR (Situation, Task, Action and Results) interviews, role plays, case studies and simulations, high-pressure interview questions, project assignments, assessment centres, in-depth interview questions and special methods, such as face reading and fingerprint reading. Top and HR managers across industries strongly agree that the eight essential work skill sets and 42 skills are necessary for workers to survive in the business arena in EMEs. They also strongly agree that talent selection tools, especially behavioural-based interviews, are used by their firms to select high-skilled job candidates in the labour market.

Because the eight essential skill sets proposed in this paper are based primarily on the qualitative data obtained from top/HR managers in firms across some industries, generalization to respondents across other industries or across other EMEs may be limited. It is possible that the context of other EMEs may be different from that of Thailand. In this regard, some of the essential work skill sets that are suitable in the case of Thailand may not be suitable in the case of other EMEs. Future studies should thus explore how institutional contexts of other countries/economies shape the definition of essential skill sets and their components, as well as potential selection tools that shall be applied to select high-skilled labour in those contexts.

This paper provides practical implications for top managers and/or HR managers of firms across various industries in EMEs. In particular, managers should internally train and develop their employees/workers to possess the eight essential skill sets: (1) ideation and system thinking, (2) information and digital literacy, (3) social skills with appreciation for diversity and inclusion, (4) communication and language, (5) creativity and innovation, (6) EQ for self-management and development, (7) growth/outward mindset and (8) cognitive skills for the specific job role so that their employees/workers can survive and thrive in the era of the brittleness, anxiety, non-linearity and incomprehensibility of the business world under pandemic conditions. Additionally, top managers and/or HR managers of these firms should apply the potential selection tools proposed in this paper to probe into job candidates’ past experience and behaviours to better predict such candidates’ success at work. In this regard, job candidates/workers should prepare themselves to possess these essential work skill sets so that they can be successful in the business arena and should understand potential selection tools that firms may apply to recruit and select them.

This paper provides social/policy implications for the government and/or relevant public agencies of Thailand and of other EMEs. These governments should encourage firms across industries to invest resources in training and developing their employees/workers to possess those essential work skill sets so that these employees/workers are industry-ready, leading to the alleviation of the problems of skill and mismatch in the labour market.

This paper contributes to the prior literature on human resource management (HRM), the comparative institutional perspective on employment systems based on the varieties of capitalism (VoC) framework and the LPT of work in the following ways: first, this paper fills in the research gap in the field of HRM that calls for studies that explore how the COVID-19 pandemic shapes essential skill sets and skills development among workers within firms (Cooke et al., 2021). Second, this paper provides implications for the LPT of work regarding how essential work skill sets are likely to return the power of negotiation from employers to workers in EMEs during the current situation. Third, the VoC framework tends to focus on only two types of economies, liberal market economies and coordinated market economies. However, this paper examines essential work skill sets and potential screening tools in the context of the underresearched country of Thailand, an EME. In fact, the Thai labour market is quite different from that of other EMEs labour markets, as it is impacted by an aging workforce. This paper contributes to the literature on comparative institutional perspectives on employment systems as it redefines essential work skill sets, proposes various components of these skill sets among workers and examines potential selection tools that are applied across firms located in EMEs.

This paper explores how conflicting institutional logics shape the behaviors of macro- and micro-level actors in their use of a calculative practice. Thereby, this paper explains how quantification can undermine the intended purpose of a governance system based on a single number.

The study draws upon the literature on calculative practices and institutional logics to present the case of how a single number—specifically the conversion factor for Atlantic Cod, established by macro-level actors for the purposes of governance within the Norwegian fishing industry—is interpreted and used by micro-level actors in the industry. The study is based on documents, field observations and interviews with fishers, landing facilities, and control authorities.

The use of the conversion factor, while intended to protect fish stock and govern industry actions, does not always align with the institutional logics of micro-level actors. Especially during the winter season, these actors may seek to serve their interests, leading to potential system gaming. The reliance on a single number that overlooks seasonal nuances can motivate unintended behaviors, undermining the governance system’s intentions.

Integrating the literature on calculative practices with an institutional logics perspective, this study offers novel insights into the challenges of using quantification for the governance of complex industries. In particular, the paper reveals that when the logics of macro- and micro-level actors conflict in a single-number governance system, unintended outcomes arise due to a domination of the macro-level logics.

Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.

The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.

The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.

In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.

The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).

This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

This paper aims to recall the attention on a key challenge for customer relationship management related to the role of human agents in the management of the “switch point” for ensuring the effectiveness and efficiency in a customer-machine conversation.

This study contributes to the discussion about the firms’ approach to artificial intelligence (AI) in frontline interactions under the conceptual umbrella provided by knowledge management studies.

This paper provides a theoretical model for clarifying the role of human intelligence (HI) in AI-based frontline interactions by highlighting the relevance of the actors’ subjectivity in the dynamics and perceptions of customer-machine conversations.

An AI-HI complementarity matrix is proposed in spite of the still dominant replacement view.

The main objective of this study is to evaluate the driving forces behind the adoption of indoor environmental quality (IEQ) principles in building designs from the perspectives of Nigerian quantity surveying firms.

A quantitative approach was used which involved administering a well-structured questionnaire to a sample of 114 quantity surveyors. The collected data were analyzed using various statistical methods, including frequencies, percentages, mean item scores, Kruskal–Wallis test and exploratory factor analysis.

The top five ranked drivers were climate change mitigation, conservation of natural resources, reduction of waste and pollution, use of sustainable building materials and development of new materials and building systems. Based on the factor analysis, the study identified five clusters of drivers: (1) health and well-being drivers (2) economic drivers (3) environmental drivers (4) innovation and technology drivers and (5) regulatory drivers.

The findings from this study suggest that to effectively integrate IEQ principles, quantity surveying firms should consider developing comprehensive guidelines and checklists that align with the identified drivers and clustered categories. These resources can serve as practical tools for project teams, facilitating a structured and holistic approach to the incorporation of IEQ factors throughout the project lifecycle.

The study’s identification of the top drivers and the subsequent clustering of these drivers into five distinct categories contributes to the existing body of knowledge on IEQ. This approach provides a structured framework for comprehensively understanding the factors influencing IEQ adoption, offering a valuable tool for researchers, policymakers and industry practitioners.

The aim of this study is to (1) construct a standard framework for assessing the capability of bioenergy enterprises' digital green innovation partners; (2) quantify the choice of partners for digital green innovation by bioenergy enterprises; (3) propose based on a dual combination empowerment niche digital green innovation field model.

Fuzzy set theory is combined into field theory to investigate resource complementarity. The successful application of the model to a real case illustrates how the model can be used to address the problem of digital green innovation partner selection. Finally, the standard framework and digital green innovation field model can be applied to the practical partner selection of bioenergy enterprises.

Digital green innovation technology of superposition of complementarity, mutual trust and resources makes the digital green innovation knowledge from partners to biofuels in the enterprise. The index rating system included eight target layers: digital technology innovation level, bioenergy technology innovation level, bioenergy green level, aggregated digital green innovation resource level, bioenergy technology market development ability, co-operation mutual trust and cooperation aggregation degree.

This study helps to (1) construct the evaluation standard framework of digital green innovation capability based on the dual combination empowerment theory; (2) develop a new digital green innovation domain model for bioenergy enterprises to select digital green innovation partners; (3) assist bioenergy enterprises in implementing digital green innovation practices.