Asian Institute of Management

With the evolving realm of news propagation and the surge in social media usage, detecting and combatting fake news has become an increasingly important issue. Currently, fake news detection employs three main feature categories: news text, social context, and news images. However, most studies emphasize just one, while only a limited number incorporate image features. This study presents an innovative hybrid fake news detection model amalgamating text mining technology to extract news text features, user information on Twitter to extract social context features, and VGG19 model to extract news image features to increase the model's accuracy. We harness four diverse machine learning algorithms (Logistic Regression, Random Forest, Support Vector Machine, and Extreme Gradient Boosting) to construct models and evaluate their performance via Precision, Recall, F1-Score, and Accuracy metrics. Results indicate the fusion of news text, social context, and image features outperforms their individual application, yielding a noteworthy 92.5% overall accuracy. Significantly, social context attributes, encompassing users, publishers, and distribution networks, contribute crucial insights into detecting early-stage fake news dissemination. Consequently, our study bolsters fact-checking entities by furnishing them with news-content insights for verification and equips social media platforms with a potent fake news detection model—comprising news content, imagery, and user-centric social context data—to discern erroneous information.

Drawing from social role theory, we studied gender differences in employees' perception of gender inequity in human resource management (HRM) practices using samples from three countries. We examined the relationship of employee gender with equity perceptions, as moderated by (a) the gender composition of managers in an organization and (b) country‐level gender egalitarianism. We expected females to perceive more inequality in HRM practices compared to males but instead found that females perceived less inequity. In organizations with predominantly female managers, females perceived less gender inequity in HRM practices than males, whereas in male‐majority manager organizations, both genders perceived similar levels of gender inequity. Our results also indicated that, in less (more) gender‐egalitarian countries, female employees perceived more (less) gender inequity in HRM practices. However, male employees perceived similar levels of gender inequity, slightly favoring men, regardless of country‐level gender egalitarianism.

This research documents that the fall and persistently low level of UK Total Factor Productivity (TFP) following the Great Recession was not caused by static resource misallocation between incumbent firms within industries. To arrive at this conclusion, we empirically measure misallocation using the Hsieh and Klenow methodology, developed in 2009, in conjunction with the FAME micro-level dataset that contains more than 9 million firms within the UK over the 2006–2014 period. The main findings are that, first, service sector TFP drops far more than manufacturing TFP and therefore drives the fall and long-lasting depression in aggregate productivity. Second, within-industry misallocation cannot account for the drop in TFP. And third, preliminary evidence suggests that entry and exit dynamics of firms might play a key role in allocating resources from productivity rich towards productivity poor firms.

The ability of machine learning algorithms to generalize to data shifts is essential in medical imaging due to the high accuracy demands in clinical practice. Continual learning algorithms have been leveraged in various medical imaging tasks, primarily image classification and segmentation. In this study, we explore the capabilities of continual learning using synaptic intelligence (SI) regularization in 3D unsupervised medical image registration. We also proposed SIHDConReg, which incorporates hypergradient descent to SI for improved convergence during model optimization. At each optimization iteration of SIHDConReg, SI regularization, SI parameter importance calculation, and adaptive learning rate update are added to the standard Adam loop. Our ablation experiments on three sequences of various medical image registration datasets demonstrated better learning plasticity for SIHDConReg compared to SI alone. Comparison of experiments with simple fine-tuning, and L2 weight regularization implies further improvement can be made to address the stability-plasticity trade-off. However, the small catastrophic forgetting involved in the datasets in this study shows SIHDConReg as a viable learning algorithm to help medical image registration models adapt to data shifts, such as changes in field-of-view and image quality, while retaining most of the prior task knowledge.

Competition is a very important preconditionwhich affects the effectiveness of development of national economy under the conditions of globalization. In classical economics, the competitiveness of countries is determined through production inputs. In the modern era of globalization, it appears that, besides quantifiable factors, qualitative influences or ‘soft’ factors such as political stability, government policies, quality of education, etc., are all important in determining competiveness. The World Economic Forum’s global competitiveness index and the IMD World Competitiveness Yearbook (WCY) are the two most widely used competitiveness indices. Using the same data as the WCY, Principal Components Analysis (PCA) is used in this analysis to develop indices of countries’ competitiveness. The procedure deals with first transforming the original variables to a new set of uncorrelated variables called Principal Components (PC). The new variables are linear combinations of the original variables, independent, and are derived in order of decreasing importance--the first PC accounts for as much as possible of the variation in the original data. We find that the WCY data collection methods could be simplified without compromising quality--which may encourage more countries to participate in the survey. Moreover, the approach developed in this study does not suffer from the same empirical limitations of past attempts to develop indices of the competitiveness of nations.

This chapter empirically analyzes the link between financial inclusion (SDG 8.10) and economic activity. Instead of following the past literature and approximating financial inclusion by variables only capturing traditional financial services, this chapter considers non-traditional financial services, including mobile money and nonbranch retail agent outlets. With the help of the normalized inverse of the Euclidian distance and a one-way fixed effects panel model, this chapter documents empirically robust results about the positive link between financial inclusion and the level of economic activity. In addition, a break between poverty and financial inclusion is established by regressing the calculated index of financial inclusion on demographic, socioeconomic, and other variables concerning the health and depth of the financial sector. The implications of this finding in this analysis are twofold. First, it highlights the improvements in low, lower-middle, and upper-middle-income countries regarding outreach to financial services in the last decade. Second, it shows that the level of education and the soundness and depth of the local financial sector are essential in reaching higher levels of financial inclusion. Overall, our results emphasize the importance of targeted policies to increase the accessibility, availability, and usage of the financial sector to attain sustainable and long-lasting economic prosperity.

This study investigated the impact of brand equity and the decoy effect on the purchase of insulated water flasks. This was done in order to address a gap in understanding the decoy effect’s effectiveness across sustainable alternatives like insulated water flasks and their influence on consumer purchasing behavior. The research involved 405 participants who completed an internet survey, distributed among different social media platforms. Findings analyzed using higher-order partial least square structural equation modeling with SMART PLS v3.0, revealed that both the marketing mix and the decoy effect significantly influenced actual purchase behavior. Promotion has the most substantial impact within the marketing mix factors while price has the least effect, suggesting that consumers are willing to invest in reusable insulated water flasks due to long-term savings and benefits. Additionally, attitude significantly influences purchasing intentions, highlighting consumers’ role in decision-making. The study underscored the importance of individual attitudes and perceived behavioral control in driving purchasing decisions.

Innovative technology initiated the popularity of online investment platforms (OIPs) among households. However, acceptability and human factors affecting behavioral use has been underexplored despite its popularity. This study’s main objective was to assess consumers’ behavioral intention to invest among investors from the Philippines through purposive sampling. The integration of Social Exchange Theory (SET) and Value-Belief-Norm Theory (VBN) was considered for the holistic assessment in this study. This study was assessed and evaluated holistically through a higher-order partial least square structural equation analysis to determine significant factors affecting investment intention. Analysis using SMART-PLS v4.0, it was seen that social norm was the most significant latent variable, followed by values (ie traditional, egoistic, openness to change, and altruistic), trust, personal norm, perceived safety, beliefs on utility function, and economic benefit. The study highlights the importance of the perception of influential people regarding investment and how crucial it is for consumers to have their values aligned and satisfied with the platform. The study is the first to utilize SET and VBN outside their usual context – sustainability and environmental-related behavioral studies. Thus, the newly contextualized framework could be developed and extended with further implications presented for utilization both in the theoretical and practical aspects.

Disaster town watching comprises a participatory technique by a community to observe, identify, understand, and build resilience to natural or human-induced hazards. Participants walk through their communities, taking notes and imagery for subsequent participatory risk mapping exercises. The goal is to develop a more nuanced and contextualized understanding of the community's state of disaster risk management. This article explores the integration of drones in the acquisition of imagery for disaster town watching. It followed standard practices for conducting disaster town watching research in a community in the Philippines. However, in addition to standard walking teams, one team learned to pilot drone missions, take aerial images, and then interpret them. The imagery they produced elicited more spatially significant relationships between and across features and locations in the community. The types of local knowledge identified differed significantly from that taken from images produced with cellular phones or Google Earth. This study revealed that drone imagery can provide new insights into local knowledge on hazards, vulnerability, and resources and the enhancement of social, economic, and environmental resilience it engenders. Participants build greater confidence in their ability to determine self-help solutions and mutual help countermeasures for enhancing disaster preparedness and community resilience against disaster.

Drawing from status characteristics theory, we develop a multilevel model to explain the relationships between gender composition (e.g., female‐female supervisor‐subordinate dyads, a female majority at the next higher level, and a female majority at the same job level) in the workplace and women's career satisfaction. We hypothesise that working with a female supervisor and a female majority at the same level will be negatively related to women's career satisfaction, while a female majority at the next higher level will be positively related to women's career satisfaction. Moreover, we propose that formal societal (gender‐equality) institutions and informal cultural (gender‐egalitarian) values, each has a moderating effect on the impact of gender compositions on women's career satisfaction. Our results from a multilevel analysis of 2291 women across 35 societies support the three hypothesised main effects. Whereas institutions that support gender equality weaken the positive effect of working with a female majority at the next higher level, they amplify the negative effect of a female majority at the same hierarchical level. Our findings highlight the complex and paradoxical nature of gender composition effects on women's career satisfaction. We discuss the theoretical contributions of our findings and their implications for the diversity management practices of multinational enterprises.

Motivation Close to a third of the world's population and more than 80% of people living in extreme poverty live in contexts of fragility. With agencies such as the OECD and UNDP conceiving of such places in terms of multiple and serious risks, the framing has come to be one of pathology: fragile contexts are defined by deficits with respect to idealized governance and sustainable development goals. In consequence, development options are locked into managing risks—confining opportunities to develop potential. Purpose Can strategic foresight unlock the development potential of fragile societies? Approach and methods Because there is still little documentation of foresight initiatives in contexts of fragility, the approach here is theoretical and conceptual. We draw on literature from the fields of fragility, foresight, and cognition, as well as insights from expert exchanges and roundtables. Findings It is uncertainty, not risk, that lies at the heart of fragility—an insight that challenges standard decision‐making. With the latter being based on analogical reasoning, it cannot be logically applied under conditions of uncertainty. If, instead, we adopt an heuristic for decision‐making that acknowledges uncertainty to not only entail risk but also opportunity, strategic foresight is well‐placed to help revive development. Policy Implications First, fragility has to be reframed to acknowledge the centrality of uncertainty, not risk, in approaching fragility. Whilst evidence from the past is important, scrutinizing past paradigms and envisioning different futures is crucial. Second, strategic foresight can help uncover fragile societies’ capacities and potential. It can help shift from analyses dominated by a concern with lacks and deficits, to analyses which seek relative strengths and opportunities. Just as strong states are not strong in every respect, fragile states may have more to offer than meets the eye. Third, debates need to be more open, less ideologically laden. Dominant thinking on fragility is rife with seemingly imperturbable underpinnings: for example, the mantra that “without peace there can be no development, and without development there can be no peace.” While such propositions contain some truth, treating them as absolute and universally applicable, limits both thinking and policy options. Strategic foresight is well placed to provide a fresh view on fragility.

Governing the growth strategies of social ventures is one of the most significant challenges social entrepreneurs face. While social entrepreneurs strive for growth mainly to increase the social venture’s impact on its key stakeholders, pursuing growth may result in mission drift with a detrimental neglect of dual mission objectives and stakeholder needs. In this conceptual paper, we aim to contribute to an enhanced understanding of how growth unfolds in social ventures and how the governance of growth can prevent divergence from the venture’s raison d’être in tackling stakeholder needs. Building upon the literature on organizational and social impact growth, we theorize four distinct growth strategies: benevolence-driven, collaboration-driven, skills-driven, and consumption-driven social venture growth. For each strategy, we identify underlying growth dynamics and derive principles for the governance of social venture growth. This way, we add to the emergent literature on social venture growth and dual mission management.

Public sector wage research has evolved beyond the estimation of public sector wage premiums to investigating the nature of interrelationships between public and private wage patterns. This article seeks to extend this wage leadership research to investigate intra-industry linkages and spillovers between public and private sector wage growth in Education and Health industries, using Australia as a case study. The research employs vector error correction models to encompass tests for full wage adaptability, long-run and short-run wage leadership. Results show that the public sector is the long-run leader in all models, regardless of industry and alternative jurisdictions’ public sector wage setting policy. Our findings have important policy implications for public sector wage setting practices, demonstrating that wage policies which may be based on political motivations can have widespread effects on the private sector.

Diverse recommendations strongly correlate with increased sales diversity, perceived ease of use, and general user satisfaction with recommendation systems. However, many recommendation models focus only on maximizing recommendation accuracy. This can lead to a lack of diversity due to the feedback loop of recommending already popular items. Retraining the entire model to increase diversity can be expensive, time-consuming, and impractical. To address this, we propose a refinement strategy that uses reinforcement learning objectives to penalize non-diverse behavior. This allows us to improve the diversity of any pre-trained model without retraining it from scratch and needing the original training settings and labels. We evaluate our approach using three deep learning recommendation models on the Yoochoose, RetailRocket, and Movielens datasets. Our refinement scheme improves recommendation diversity by up to 5%$5\%$ while maintaining competitive recommendation ranking performance in metrics such as HitRate and NDCG.

During the pandemic, artificial intelligence was employed and utilized by students around the globe. Students' conduct changed in a variety of ways when schooling returned to regular instruction. This study aimed to analyze the student's behavioral intention and actual academic use of communicational AI (CAI) as an educational tool. This study identified the variables by utilizing an integrated framework based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and self-determination theory. Through the use of an online survey and Structural Equation Modeling, data from 533 respondents were analyzed. The results showed that perceived relatedness has the most significant effect on the behavioral intention of students in using CAI as an educational tool, followed by perceived autonomy. It showed that students use CAI based on the objective and the possibility of increasing their productivity, rather than any other purpose in the education setting. Among the UTAUT2 domains, only facilitating conditions, habit, and performance expectancy provided a significant direct effect on behavioral intention and an indirect effect on actual academic use. Further implications were presented. Moreover, the methodology and framework of this study could be extended and applied to educational technology-related studies. Lastly, the outcome of this study may be considered in analyzing the behavioral intention of the students as the teaching-learning environment is still continuously expanding and developing.

Risk management can be aided enormously by establishing the root causes of the problem, linking cause and effect, and communicating this relationship effectively. Climate change is a marquee example of a typically low-probability but high-impact disaster turning into a high-probability and high-impact one. By backing any GDP growth regardless of its quality, aspects of mainstream growth economics have, ironically, given cover to, and implicitly cheered, high-carbon growth. In the face of anthropogenic (originating in human activities) climate change, risk reduction is not just about people’s exposure and vulnerability but also mitigating the intensity of the hazard. Innovative approaches to resilience are needed as countries face severe shortages in skilled staff and financial resources. The priority for investing in resilience in countries across regions must rise sharply as the price of delayed action mounts.