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The motivation to participate in crowdsourcing (CS) platforms is an emerging challenge. Although researchers and practitioners have focused on crowd motivation in the past, the results obtained through such practices have not been satisfactory. Researchers have left unexplored research areas related to CS pillars, such as the evolution of the crowd’s primary motivations, seekers applying effective policies and incentives, platform design challenges and addressing task complexity using the synchronicity of the crowd. Researchers are now more inclined to address these issues by focusing on sustaining the crowd’s motivation; however, sustaining the crowd’s motivation has many challenges.

To fill this gap, this study conducted a systematic literature review (SLR) to investigate and map the challenges and factors affecting sustained motivation during CS with the overcoming implications. Studies that satisfied the inclusion criteria were published between 2010 and 2021.

Important sustainable factors are extracted using the grounded theory that has sustained participation and the factors' cohesion leads to the identification of challenges that the pillars of CS face. Crowds being the most vital part of CS contests face the challenge of engagement. The results reported the factors that affect the crowd’s primary and post-intentions, perceived value of incentives and social and communal interaction. Seekers face the challenge of knowledge and understanding; the results identify the reason behind the crowd’s demotivation and the impact of theories and factors on the crowd's psychological needs which helped in sustaining participation. Similarly, the platforms face the challenge of being successful and demanding, the results identify the latest technologies, designs and features that seekers proclaim and need the platforms designer's attention. The identified task challenges are completion and achievement; the authors have identified the impact of trait of task and solving mechanisms that have sustained participation.

The study identifies, explores and summarizes the challenges on CS pillars researchers are facing now to sustain contributions by keeping participants motivated during online campaigns. Similarly, the study highlights the implication to overcome the challenges by identifying and prioritizing the areas concerning sustainability through the adoption of innovative methods or policies that can guarantee sustained participation.

Family businesses are essential to the global economy but often grapple with family-related issues, especially during succession. This study explores how governance tools like the family protocol (FP) mitigate conflicts by setting standards for family firm management and continuity. Pioneering the use of game theory and adverse selection setups in family business governance, this research uncovers FP determinants.

This research employs game theory and adverse selection setups to delve into the strategic decision-making processes of stakeholders in family firms. The authors break new ground by applying principal–agent theory (PAT) to family business governance structures. This innovative approach uncovers the determinants of the FP, enhancing the authors’ understanding of family firm dynamics.

The authors emphasize the importance of custom governance structures, such as the FP, in managing complex family-business interactions. These structures mitigate conflicts and promote smoother transitions during succession, ensuring family firm continuity. This study identifies key determinants, and these results will aid founders, families and practitioners in achieving smoother transitions, ensuring family firm continuity.

This research pioneers game theory and PAT applications in family business governance, shedding light on the effectiveness of customized governance mechanisms. By identifying FP determinants, the authors contribute to a deeper understanding of family firm dynamics. The findings have practical implications for founders, families, practitioners and consultants, promoting the long-term success and harmony of family firms in the global economy.

Frequent renegotiations within public-private partnership (PPP) have been recognised and affect project efficiency. Literature has focussed on diverse issues associated with renegotiation within PPP, especially in Latin America and Europe. However, a systematic summary what they have already provided appears lacking. Thus, the paper aims to conduct a critical review of publications concerning PPP renegotiation and explore the status quo, future interests and gaps in research.

This study carried out a four-phase literature review research framework to identify the quality PPP-renegotiation articles published from 2003 to 2020. Assessing the full articles for eligibility by providing a structured summary including: background; objectives; data sources; study appraisal; results; limitations; conclusions and implications of key findings. After that, filtering papers associated with PPP renegotiation in terms of the structured summary, and a total of 60 research papers were selected in the database of web of science and Scopus for review.

Methods adopted by researchers, research topics and theoretical foundations of PPP renegotiation research in different disciplines were identified through content analysis. Amongst the popular research topics identified were renegotiation factors, the outcomes with renegotiations, the framework to deal with renegotiations and contract design dealing with renegotiation based on rigid or flexible contracts.

This study contributes to the current body of PPP knowledge by revealing the research trend in the past 20 years. It also points out the directions that the renegotiations of PPP research may go towards in the future. Moreover, this study is very valuable in understanding how governments and concessionaires effectively handle renegotiations.

This study aims to create actionable guidelines for pricing decision-making by employing game a theory matrix to forecast the correlation between the average daily rate and the latest ambiance of hotels.

Utilizing a vector error correction model, the research employs game theory to assess the influence of the average daily rate on the hotel's newest atmosphere during both peak season (April–September) and valley season (October–March).

Findings indicate that during the peak season, when the average daily rate rises in resorts and falls in suburban areas, the hotel’s newest atmosphere is at its best in both types of accommodations. During the off-peak season, the hotel’s newest atmosphere is achieved when both resorts and suburban accommodations increase their average daily rates.

There are two study constraints. One is the assumption that hotel guests in both parties prefer not to change hotels, but in fact they would. Two is a limited sample of two resort and suburban markets.

This suggests that the hotel’s newest atmosphere can draw both leisure and business travelers to suburban areas during the low season and more leisure travelers to resorts during the high season.

The study’s findings have implications for revenue related to the hotel’s newest atmosphere and cleanliness for both suburban and resort hotels, particularly when promoting tourism collaboratively.

The study provides valuable insights for hotel managers in analyzing pricing strategies using matrices.

The purpose of this study is to present a general review that provides an overview of the concept of sustainability and the effectiveness of mathematics curricula in courses where deeper work on economic and environmental sustainability has become central.

A qualitative methodology consisting of a review based on a pre-defined systematic method was used to exhaustively search and identify the most relevant answers to the research question: What is the role of mathematics to sustainability? To facilitate answering such a broad question, several concrete questions were formulated. Answers from published and unpublished documents were analysed. The quality of the extracted data was assessed, and the results were synthesized.

It was concluded that, on the one hand, the discipline of mathematics has much to contribute to solving the problems of sustainability; on the other hand, new mathematics is appearing stimulated by new challenges.

This work presents social implications in an innovative way. It allows for an increase in educational sustainability by bringing the academic community closer to the business world and the challenges of society and, furthermore, by having a major impact on the motivation of teachers and students to develop cooperative work within university institutions.

The originality is based on an a priori analysis for the construction and implementation of didactic tools for university teacher training in the area of mathematics within the framework of sustainable development, both economically and environmentally.

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

Brief introduction on the importance and the need for plastic analysis methods were presented in the beginning section of this review. The plastic method for analysis was considered to be the more advanced method of analysis because of its ability to represent the true behaviour of the steel structures. Then in the following section, a literature analysis has been carried out on the previous investigations done on steel plates, steel beams and steel frames by other authors. The behaviour of them under different types of loading were presented and are under the investigation of innovative new analysis methods.

Structure member connections also have the potential for plastic failure. In this study, the authors have highlighted a few topics to be discussed. The three topics in this study are T-end plate connections to a square hollow section, semi-rigid connections and cold-formed steel storage racks with spine bracings using speed-lock connections. Connection is one of the important parts of a structure that ensures the integrity of the structure. Finally, in this technical paper, the authors introduce some topics related to seismic action. Application of the Theory of Plastic Mechanism Control in seismic design is studied in the beginning. At the end, its in-depth application for moment resisting frames-eccentrically braced frames dual systems is investigated.

When this study involves the design of a plastic structure, the design criteria must involve the ultimate load rather than the yield stress. As the steel behaves in the plastic range, it means the capacity of the steel has reached the ultimate load. Ultimate load design and load factor design are the methods in the range of plastic analysis. After the steel capacity has reached beyond the yield stress, it fulfills the requirement in this method. The plastic analysis method offers a consistent and logical approach to structural analysis. It provides an economical solution in terms of steel weight, as the sections designed using this method are smaller compared with elastic design methods.

The plastic method is the primary approach used in the analysis and design of statically indeterminate frame structures.

This study aims at advancing the understanding of University-Industry Collaboration (UIC) by proposing an adapted conceptual model for comprehensive contextual analysis taking industry perspective and identifying effective mechanisms for stimulating UIC in sub-Saharan Africa (SSA).

This study was designed as a multicase qualitative study. Data were collected through interviews of focus groups representing 26 agro-processing companies operating in Rwanda. The process consisted of two sessions evaluating the current collaboration patterns, and two sessions identifying the desired future and the mechanisms to realize it. Data were analyzed using ATLAS.ti software with the grounded theory techniques.

Findings indicate that current interactions are unidirectional and focus on educational collaboration. Results are short-term and do not yield sufficient benefit for the companies involved. Industry aspirations for future collaboration were identified, along with linkages between inputs, activities and outcomes.

This study has two noteworthy limitations. First, this study did not consider the capacity of firms to collaborate. Second, for simplification purposes, this study did not integrate other complementary sources of knowledge for firms. To overcome these omissions, a short introduction of University–Industry Collaboration (UIC) was presented to participants highlighting and justifying the scope of the study. As research implications, this paper presents a new integrated conceptual framework, which can be useful for studies focusing on a comprehensive analysis of the UIC context and identifying effective mechanisms for improvement in the future. A construct of policies that stimulate UIC is proposed, thereby facilitating further operationalization and testing of context-specific hypotheses on policy stimuli.

In practice, the identified conceptual framework allows to holistically capture and reflect on the interrelationships between UIC factors and outcomes for a specific context, hence, informing better UIC decision-making. In this way, this paper advances the operationalization of the argument for reconciling organizational theories with their practices.

This paper presents a systemic means for organizational theories to perform their adaptive role in society. Indeed, as demonstrated by the empirical results, the proposed framework is effective not only in systematically assessing the current situation, but also in predicting the desired state in the future. In other words, the proposed framework facilitates prescience theorizing, which is a mechanism for ensuring that organizational theories adapt to future requirements of the society.

This study develops a new integrative conceptual framework to accommodate the interaction between UIC’s institutional decision-making and existing macrolevel frameworks of innovation ecosystems. In terms of methodology, the value of this study lies in its adoption of an ex ante approach to the development of mechanisms to stimulate UIC. This use of prescience theory (Corley and Gioia, 2011) constitutes an important – but long-neglected – approach to UIC and its adaptive role in society.

The purpose of this study is to examine how robotic anthropomorphism and personalized design may affect consumers' reactions to brands after service failure.

This study conducted two studies based on cognitive appraisal theory and artificial intelligence device acceptance theory. Study 1 explored the mechanisms by which the type of anthropomorphic design of the service robot (humanoid robot/nonhumanoid robot) influenced revisit intention after service failure through a one-factor between-subjects design based on a restaurant dining scenario. Study 2 was based on a hotel check-in scenario and explored the moderating effect of robot personalization design on the above mechanisms through a 2 (anthropomorphic design: humanoid robot/nonhumanoid robot) × 2 (personalized design: self-name/no name) between-subjects design.

Study 1 shows that consumers have higher performance expectations for nonhumanoid robots, leading to a higher tolerance for service failure, which in turn generates higher revisit intentions. Study 2 shows that consumers' performance expectations are significantly enhanced after custom naming of humanoid robots, so the serial mediation mechanism for the effect of robot anthropomorphic design on revisit intention does not hold.

This study extends the research of artificial intelligence device acceptance theory in the field of service failure and exploratively proposes an intervention mechanism for the negative effects of the anthropomorphic design of service robots.

The purpose of this study is to demonstrate that Strategic Enterprise Management (SEM) and Business Intelligence (BI) have the potential to integrate management decisions vertically through an organization’s hierarchy. This study also aims to present a design theory framework and build a model dimension using eight principles serving as mid-range theories.

This study uses a design science perspective to posit how organizations can successfully implement SEMBI (a union of SEM and BI). This study then completes the design theory by building the method dimension using two principles. Finally, the study presents testable hypotheses for the theory and an evaluation using stakeholder attitudes and judgments as proxies for objective measures.

In the search for a prescription for SEMBI success, this study finds that the notion of the Capability Maturity Model (CMM) is a good artifact with which to organize the principles the authors are seeking. CMM has since been adapted to suit different contexts by incorporating relevant principles from those domains. Hereafter, this study refers to SEMBI–CMM as the adapted solution for SEMBI's success.

This study coins and uses the term SEMBI to represent the union of SEM and BI. This term retains its distinct identities and principles and forms a holistic and integrated view of SEM and BI implementation strategies. In an effort to advance this line of research, this study employs a design science perspective to address the question of how an organization can successfully implement SEMBI.