Search results

In the hybrid knowledge-sharing platform where paid and nonpaid (“free”) knowledge activities coexist, users’ free knowledge contribution may be influenced by financial factors. From the perspective of opportunity cost, this study investigates the direct effect of how the amount of monetary income from users’ contribution to paid knowledge activities influences their free knowledge contribution behavior in the future. Further, this study aims to verify the interaction effect of financial and nonfinancial factors (i.e. the experience of free knowledge contribution and social recognition) on free knowledge contribution.

Objective data was collected from a hybrid knowledge-sharing platform in China and then analyzed by using zero-inflated negative binomial regression model.

Results show that the amount of monetary income that knowledge suppliers gain from paid knowledge contribution negatively influences their free knowledge contribution. Experience of free knowledge contribution strengthens the negatively main effect, while social recognition has the weakening moderating role.

Although some studies have explored and verified the positive spillover effect of financial incentives on free knowledge contribution, the quantity dimension is ignored. This study examines the hindering influence of the quantity of monetary income from the perspective of opportunity cost. By taking the characteristic of knowledge suppliers and platforms as moderators, this study deepens the understanding of the influence of monetary income on free knowledge contribution in the hybrid knowledge-sharing platform.

The aim of this paper is twofold: to explore the significance and implications of the rise of AI technology for the field of tertiary education in general and, in particular, to answer the question of whether teachers can be replaced by intelligent AI systems such as androids, what that requires in terms of human capabilities and what that might mean for teaching and learning in higher education.

Given the interdisciplinary nature of this conceptual paper, a literature review serves as a methodological tool to access data pertaining to the research question posed in the paper.

This exploratory paper gathers a range of evidence from the philosophy of mind (the mind-body problem), Kahneman’s (2011) System 1 and System 2 models of the mind, Gödel’s (1951) Two Incompleteness Theorems, Polanyi’s (1958, 1966) theory of tacit knowing and Searle’s (1980) Chinese Room thought experiment to the effect that no AI system can ever fully replace a human being because no machine can replicate the human mind and its capacity for intelligence, consciousness and highly developed social skills such as empathy and cooperation.

AI is rising, but there are inherent limits to what machines can achieve when compared to human capabilities. An android can at most attain “weak AI”, that is, it can be smart but lack awareness or empathy. Therefore, an analysis of good teaching at the tertiary level shows that learning, knowledge and understanding go far beyond any quantitative processing that an AI machine does so well, helping us to appreciate the qualitative dimension of education and knowledge acquisition. ChatGPT is robotic, being AI-generated, but human beings thrive on the human-to-human interface – that is, human relationships and meaningful connections – and that is where the true qualitative value of educational attainment will be gauged.

This paper has provided evidence that human beings are irreplaceable due to our unique strengths as meaning-makers and relationship-builders, our capacity for morality and empathy, our creativity, our expertise and adaptability and our capacity to build unity and cooperate in building social structures and civilization for the benefit of all. Furthermore, as society is radically automated, the purpose of human life and its reevaluation will also come into question. For instance, as more and more occupations are replaced by ChatGPT services, more and more people will be freed up to do other things with their time, such as caring for relatives, undertaking creative projects, studying further and having children.

The investigation of the scope and limitations of AI is significant for two reasons. First, the question of the nature and functions of a mind becomes critical to the possibility of replication because if the human mind is like a super-sophisticated computer, then the relationship between a brain and mind is similar (if not identical) to the relationship between a computer as machine hardware and its programme or software (Dreyfus, 1979). [ ] If so, it should be theoretically possible to understand its mechanism and reproduce it, and then it is just a matter of time before AI research and development can replicate the human mind and eventually replace a human teacher, especially if an AI machine can teach just as intelligently yet more efficiently and economically. But if AI has inherent limitations that preclude the possibility of ever having a human-like mind and thought processes, then our investigation can at least clarify in what ways AI/AGI – such as ChatGPT – could support teaching and learning at universities.

Smart cities in India are going to be a reality very soon by turning challenges into opportunities for the society. However, due to rapid increase in population burden, fast urbanization and growing demand of advanced services in the smart cities, the quantity of per capita municipal solid waste (MSW) has escalated. Moreover, the COVID-19 pandemic has further challenged the municipal solid waste management (MSWM) system with the increasing amount of infectious wastes coming from households (HHs), quarantine centers, healthcare facilities, vaccination centers, etc. Therefore, the present study attempts to explore and analyze the various dimensions of sustainable MSWM system in the smart cities.

The study identifies 13 factors of sustainable MSWM system from the literature, field surveys and stakeholders' opinions. Thereafter, stakeholders' opinions are collected and analyzed using total interpretive structural modeling (TISM) approach to explore the interrelationships among the factors of sustainable MSWM system. These relationships are further validated through the empirical investigation of the real-life case study of Rourkela Municipal Corporation (RMC), Odisha, India.

The TISM approach places all 13 factors into six levels in the hierarchical digraph depending upon the inputs received from the various stakeholders on their interrelationships. Study also validates the proposed TISM model by collecting the data of RMC, Odisha, on the development of MSWM system over the period of 2015–2021.

The study also highlights various implications for the other developing cities and stakeholders to set up the roadmap for developing the sustainable MSWM system. Study defines “IT platform” and “awareness among citizens” as the base of the sustainable MSWM system in any smart city.

The present study is the first of its kind to explore the interrelationships among the factors of sustainable MSWM system by using TISM approach. Moreover, the proposed TISM framework is further validated through the empirical journey of one of the smart cities in India.

This paper aims to examine Holacracy as a self-management system and its implications for organizational theory. By exploring its distinctive ontology and epistemology, the paper explains how Holacracy redefines organizational structures and contributes to organizational change.

Adopting a conceptual approach, this essay draws upon existing literature to explore Holacracy’s departure from conventional organizational theories.

The paper discusses how Holacracy challenges traditional organizational concepts, defining its holarchical approach, and emphasizing dynamic hierarchies and role-based organization. It also highlights Holacracy’s focus on emergent intelligence, continuous feedback loops and structured knowledge acquisition through roles and organization spaces. By examining its unique ontology and epistemology, the paper offers insights into how Holacracy aligns with an emergent paradigm known as integral, metamodern or teal.

Further empirical research is needed to assess its practical implementation and long-term effects on organizational performance and employee well-being. Future studies could also explore challenges and limitations associated with adopting Holacracy in different organizational contexts.

Organizations considering alternative ways of working should examine Holacracy. Implementing Holacracy requires understanding its principles and may involve significant changes, with potential benefits such as increased agility and employee engagement.

Holacracy’s departure from traditional structures and approach to power can have broader social implications and contribute to more democratic and participatory cultures.

This paper is a pioneering contribution to the under-researched domain of alternative organizational models. It sheds light on the originality and distinctiveness of Holacracy, highlighting its unique approach to hierarchy, role-based organization and consciousness within organizations.

This chapter presents an exploratory study of specific experiences among Central Asian grandparents who adopt and raise their firstborn grandchild as their own youngest child. The practice, referred to as ‘nebere aluu’, is deemed an ethnonational tradition of the Kyrgyz and Kazakh people and appears to be widely accepted among men and women, young and old. Drawing on in-depth interviews with grandparents themselves, I describe this phenomenon as situated within and dynamically responding to the shifting social, economic and political context of contemporary Central Asia. Drastic transformations in the everyday lives, while destabilizing and disorienting, may have supplanted nebere aluu with unique significance. Contemporary expressions of nebere aluu point to it being a complex social system of intergenerational reciprocal care, continuity and responsibility that provides a meaningful space for reconciling conflicting ideas about family, marriage, love and child-rearing. This discursive space is open for debate and negotiations and raises important questions about power and gender politics inherent to it.

This paper aims to explore product pricing and green promotion effort policies and further analyzes the influences of financing interest rate, green promotion effort and free-riding behavior on the optimal strategies.

Research will be conducted with the aid of Stackelberg game research method, considering that the manufacturer has financial constraints and financing from e-commerce platform, and consumers have dual preferences, based on the two models of no green promotion effort for physical store and green promotion effort for physical store to explore dual-channel green supply chain strategies.

This research puts forward the following findings, in the two models: the rise in financing interest rate leads to an increase in wholesale and selling prices of dual channels and a decrease in demand of dual channels. The green promotion effort has a positive impact on wholesale prices, selling prices and demand of dual channels. The rise of free-riding rate makes offline wholesale and selling prices fall, whereas online wholesale and selling prices rise.

This research results can provide reference for the decision-making in the context of supply chain financing and free-riding.

The boom in live streaming has intensified competition among streamers for viewers' gifts, which makes it meaningful to study the factors that affect the viewers’ gifting behavior. Given the emotional attachment between streamers and viewers, the authors set out to elucidate a new driver on viewer gifting: expressions of the streamer. This research aims to explore the impact of streamer emotions on the viewer gifting behaviors, including free and paid gifting. The loyalty level of the viewers is also introduced as a moderating factor to investigate the heterogeneous effect of streamer emotions on gifting behavior.

The dataset the authors collected consists of two parts, including 1809.69 h of live streaming videos and 358,002 gift giving records. Combined with deep learning methods and regression analysis, the authors performed empirical tests on the 81,110 valid samples. Several robustness checks were also conducted to ensure the reliability of main results.

The empirical results show that streamer emotions do have effects on viewers' free and paid gifting behavior. The authors’ findings show that positive streamer expressions, such as happiness and surprise, have a positive influence on viewer gifting behavior. However, some negative expressions, like sadness, can also have a positive impact. Moreover, the authors discovered that higher viewer loyalty amplifies the positive effect of streamer emotions and reduces the negative effect.

This research contributes to the study about streamer emotions and viewers' consumption behavior, which extends the application of emotion as social information model (EASI model) in the live streaming setting. The authors carefully divide the gifting behavior into two types: free and paid, and study how these two types are affected by streamer emotions. Besides, these effects are analyzed within viewers of different loyalty levels. This study offers practical emotion management strategies for streamers and live streaming platforms to gain more economic profits.

This study aims to examine the technical efficiency of the chemical-free farming system in India using a hybrid combination of data envelopment analysis (DEA) and machine learning (ML) approaches.

The study used a two-stage approach. In the first stage, the efficiency scores of decision-making units’ efficiency (DMUs) are obtained using an input-oriented DEA model under the assumption of a variable return to scale. Based on these scores, the DMUs are classified into efficient and inefficient categories. The 2nd stage of analysis involves the identification of the most important predictors of efficiency using a random forest model and a generalized logistic regression model.

The results show that by using their resources efficiently, growers can reduce their inputs by 34 percent without affecting the output. Orchard's size, the proportion of land, grower's age, orchard's age and family labor are the most important determinants of efficiency. Besides, growers' main occupation and footfall of intermediaries at the farm gate also demonstrate significant influence on efficiency.

The study used only one output and a limited set of input variables. Incorporating additional variables or dimensions like fertility of the land, climatic conditions, altitude of the land, output quality (size/taste/appearance) and per acre profitability could yield more robust results. Although pineapple is cultivated in all eight northeastern states, the data for the study has been collected from only two states. The production and marketing practices followed by the growers in the remaining six northeastern states and other parts of the country might be different. As the growers do not maintain farm records, their data might suffer from selective retrieval bias.

Given the rising demand for organic food, improving the efficiency of chemical-free growers will be a win-win situation for both growers and consumers. The results will aid policymakers in bringing necessary interventions to make chemical-free farming more remunerative for the growers. The business managers can act as a bridge to connect these remote growers with the market by sharing customer feedback and global best practices.

Although many developments have happened to the DEA technique, the present study used a traditional form of DEA. Therefore, future research should combine ML techniques with more advanced versions like bootstrap and fuzzy DEA. Upcoming research should include more input and output variables to predict the efficiency of the chemical-free farming system. For instance, environmental variables, like climatic conditions, degree of competition, government support and consumers' attitude towards chemical-free food, can be examined along with farm and grower-specific variables. Future studies should also incorporate chemical-free growers from a wider geographic area. Lastly, future studies can also undertake a longitudinal estimation of efficiency and its determinants for the chemical-free farming system.

No prior study has used a hybrid framework to examine the performance of a chemical-free farming system.

The author presents new estimates of the probability weighting functions found in rank-dependent theories of choice under risk. These estimates are unusual in two senses. First, they are free of functional form assumptions about both utility and weighting functions, and they are entirely based on binary discrete choices and not on matching or valuation tasks, though they depend on assumptions concerning the nature of probabilistic choice under risk. Second, estimated weighting functions contradict widely held priors of an inverse-s shape with fixed point well in the interior of the (0,1) interval: Instead the author usually finds populations dominated by “optimists” who uniformly overweight best outcomes in risky options. The choice pairs used here mostly do not provoke similarity-based simplifications. In a third experiment, the author shows that the presence of choice pairs that provoke similarity-based computational shortcuts does indeed flatten estimated probability weighting functions.