Empirical Framework Research Articles

New insights into the structure of consumer preferences for natural and artificial sweeteners

AbstractThis study utilizes the Generalized Exact Affine Stone Index demand system to investigate the structure of demand for natural and artificial sweeteners in the United States based on uniquely disaggregate data. Our empirical framework allows for arbitrarily complex Engel curves, unobserved consumer heterogeneity and potential precommitments in sweetener consumption, while addressing price endogeneity. Results from a series of weak separability tests reveal that consumers select from among both sets of natural and artificial sweeteners. In addition, most sweeteners are empirically established to be consumed in precommitted quantities, with the shares thereof varying from 6.3% for molasses to 34.4% for granulated sugar. Based on the unconditional elasticities obtained via a two‐stage budgeting framework, we further examine the implications of hypothetical sweetener tax scenarios for consumer energy intake and bodyweight, as well as assess the consumer welfare consequences thereof.

Information Systems Effect on Enabling Knowledge Management


 
 
 
 Purpose: This research aims to neutralize the impact of information systems on enabling knowledge management in the banking sector in Syria.
 
 Theoretical framework: IT resources positively affect knowledge management capability. As studies (Watson 2007; O'Brien & Marakas 2011; Mao et al., 2016) and. We document a relationship between information systems enabling knowledge management in Syrian banks
 
 Design/methodology/approach: This study of measuring this effect was based on the design of a questionnaire distributed to 369 individuals working in Syrian government banks in 2022.
 
 Findings: This study concluded that there is a direct impact of information systems on enabling knowledge management, and the technological requirements of information systems have a greater impact than the organizational requirements. The study also showed that there were differences between the answers of the sample members in all the research axes, as it showed the lack of information systems or enabling knowledge management in banks for most workers from the second and third job categories, as opposed to workers from the first job category with great job experience, whose answers were positive towards the two research variables.
 
 Research, Practical & Social implications: This article offers empirical support for theory-based claims and helps create a empirical framework for the success of knowledge management activities.
 
 Originality/value: Very few research on management level employees' opinions of knowledge sharing in banks have been done in Syria.
 
 
 

Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach

Recent years have witnessed an increased demand for mobile health (mHealth) platforms owing to the COVID-19 pandemic and preference for doorstep delivery. However, factors impacting user experiences and satisfaction levels across these platforms, using customer reviews, are still largely unexplored in academic research. The empirical framework we proposed in this paper addressed this research gap by analysing unmonitored user comments for some popular mHealth platforms. Using topic-modelling techniques, we identified the impacting factors (predictors) and categorised them into two major dimensions based on strategic adoption and motivational association. Findings from our study suggest that time and money, convenience, responsiveness, and availability emerge as significant predictors for delivering a positive user experience on m-health platforms. Next, we identified substantial moderating effects of review polarity on the predictors related to brand association and hedonic motivation, such as online booking and video consultation. Further, we also identified the top predictors for successful user experience across these platforms. Recommendations from our study will benefit business managers by offering an improved service design leading to higher user satisfaction across these m-health platforms.

Green finance, environmental pollution and high-quality economic development-a study based on China's provincial panel data.

Under the new normal, green finance is inevitably linked to high-quality economic development in China. Based on 30 province panel data sets from China from 2010 to 2019, this research employs an innovative spatial econometric model to integrate green finance, environmental pollution and high-quality economic development into a unified empirical analysis framework. Regional green finance and high-quality economic development have spatial spillover effects in China, according to spatial auto-regressive and spatial error model tests, with the eastern region leading, the central region running in parallel and the western region chasing, because of a strong moderating effect in the eastern region. Green finance contributes greatly to high-quality economic development, but increased environmental pollution impedes high-quality economic development. Green financing can help to mitigate the detrimental effects of pollution on high-quality economic development. According to the mechanism of action analysis, green finance reduces pollution by modifying the industrial structure and boosting scientific and technological growth. Finally, the green finance threshold test demonstrates a nonlinear impact on economic quality development after passing a specified threshold value and has a strong threshold characteristic. This research has policy implications since it improves understanding of the dynamics of high-quality economic development as well as the benefits, mechanisms and heterogeneity of green finance in reducing pollution and empowering high-quality economic development.

Critical Obstacles Affecting Adoption of Industrial Big Data Solutions in Smart Factories

Industrial big data is the key to realize the vision of smart factories. This research aims to identify and explore potential barriers that prevent organizations from deploying industrial big data solutions in the development of smart factories through a socio-technical perspective. The research follows an inductive qualitative approach. Twenty-seven semi-structured interviews were conducted with the CEO, smart factory manager, IT managers, departmental heads, and IS consultants in the selected case company. The interview data were analyzed using a thematic analysis method. Derived from a thematic analysis, six sets of barriers including technical, data, technical support, organization, individual, and social issues were identified, as well as the relationships between them. An empirical framework was developed to highlight the relationship between these barriers. This study contributes to the knowledge of industrial big data in general and provides constructive insight into industrial big data implementation in smart factory development particularly.

SECOND TIME SWEETER? MARITAL MATCHING BETWEEN FIRST AND SECOND MARRIAGES

ABSTRACT We use data from the National Longitudinal Survey of Youth, 1979 (NLSY79) to examine changes in matching patterns between first and second marriages in the US. Using Becker''s (1973, 1974) efficient marriage market hypothesis and following Dalmia and Sicilian (2008), we develop an empirical framework to understand the importance of individual traits in spouse selection. Our structural model estimates indicate that positive assortative mating (degree of homogamy) strengthens in second marriages over first marriages. The share of couples who marry up (hypergamy) and marry down (hypogamy) falls substantially as individuals sort to partners who are more similar to them in many aspects, particularly age, education, and hours worked, in their second marriage. Finally, we find the matching patterns of second marriages to be similar to those of ongoing first marriages, providing further evidence that “likes do marry likes.” Keywords Marital assignments, assortative mating, market efficiency

ROLE OF KNOWLEDGE ECONOMY IN ECONOMIC GROWTH: “AN EMPIRICAL STUDY” ON SELECTED ARAB COUNTRIES

Despite the mounting importance of the knowledge economy in diversifying Arab economies, the knowledge transition in these countries is still in its early stages, with a long road ahead. The study’s main aim is to examine the role of the knowledge economy in economic growth and to recognize the most influential knowledge pillar for a sample of 11 Arab countries during the period 2000–2020, namely: Algeria, Bahrain, Egypt, Jordan, Kuwait, Lebanon, Morocco, Oman, Qatar, Saudi Arabia, and Tunisia. The panel generalized least squares model, with its two methods (fixed-effects model and random-effects model), was performed using an extended version of Chen and Dehlman’s (2004) empirical framework. The fixed regression findings confirmed the positive and significant role of knowledge in stimulating economic growth. The estimated results of the random regression revealed that knowledge pillars like innovation, information and communication technology, economic and institutional regimes, and the socio-economic sustainability index have a positive and significant impact on Arab economic growth, whereas education has a significant negative relationship with GDP. This can be attributed to poor quality, unequal access, and infrastructure, as well as the COVID-19 pandemic, which has had a negative impact on education. The results also showed that, in comparison to the other four pillars, the socio-economic sustainability index, which was added to the benchmark Knowledge Economy Index, was the most notable pillar that participated in the examined relationship. As a result of these findings, this study contributes to broadening the scope of Arab countries through investing in key knowledge pillars that promote economic growth and where policies should place a greater emphasis on developing education. Furthermore, because of their critical and significant role in exploring the knowledge-growth relationship, sustainability dimensions must be explicitly included in the knowledge framework.

Empirical Framework for Two-Player Repeated Games with Random States

Abstract We provide methods for the empirical analysis of a class of two-player repeated games with i.i.d. shocks, allowing for non-Markovian strategies. The number of possible equilibria in these games is large and, usually, theory is silent about which equilibrium will be chosen in practice. Thus, our method remains agnostic about selection among these multiple equilibria, which leads to partial identification of the parameters of the game. We propose a profiled likelihood criterion for building confidence sets for the structural parameters of the game and derive an easily computable upper bound on the critical value. We demonstrate good finite-sample performance of our procedure using a simulation study. We illustrate the usefulness of our method by studying the effect of repealing the Wright Amendment on entry and exit into Dallas airline markets and find that the static game approach overestimates the negative effect of the law on entry into these markets.

An Empirical Framework for Assessing the Digital Technologies Users’ Acceptance in Project Management

The technological advances and trends that have manifested in the direction of globalization have determined unprecedented economic expansion and exponential social development. Organizations have increasingly implemented digital technologies to face new challenges in all activities. Project management has more intensively implemented digitized technologies, such as big data, artificial intelligence, and cloud computing, taking into account the complexity of projects. This study aims to evaluate employees’ perceptions of the organizations involved in implementing the projects adopting digital technologies within the accounting and organizational information systems to digitize the project management process. The empirical study consists of a survey of 486 respondents from among project management specialists in Romania. The research uses the technology acceptance model (TAM) and structural equation modeling to assess user perceptions of digital technologies. The research results show that implementing digital technologies improves all activities in the project management process. Among the characteristics of digital technologies, customization is the most important characteristic that leads to increased efficiency and effectiveness of project management, followed by innovation. Furthermore, the perception of increasing the efficiency and effectiveness of project management exerts a significant and positive influence on the behavioral intention to use digital technologies and their actual use, leading to their easier acceptance.

Agile project portfolio management as a strategic enabler in the Brazilian marketplace: five case studies and one substantive theory

PurposeThe purpose of this study is to present an empirical framework for changes, communication and team restructuring developed through a substantive theory that defines the relationships between Agile adoption and organizational strategies in the Brazilian marketplace, providing assertive propositions.Design/methodology/approachThe research analysed five case studies of private companies in the Brazilian market, adopting the grounded theory (GT) method to examine the relationships between the categories. There was consistency in the reality of 22 participatory interviews with experts in Agile in the five case studies. The excellent integration between the chosen methodological approach and the organizations' characteristics reinforces a strategy focused on mixed methods.FindingsAs a result, the authors deploy an empirical framework, displaying new strategies that generate structural changes, obtaining Agile information technology (IT) project portfolio management (PPM) practices and strategies with superior performance. The necessary responses through organizational structural changes are observed, making it possible to notice changes in routines and contingencies.Research limitations/implicationsSome limitations should be pointed out for this study. The case studies were carried out in private companies in Brazil, and cultural aspects must be considered if one wants to generalize. Furthermore, to underline the effects of time, a longitudinal study would have to be employed to improve the interpretation of the results. Another limitation applies to the framework proposed in our study and its reality-simplifying nature. Models and theories with these visible generic characteristics compromise understanding specific situations.Practical implicationsThe authors strongly recommend that teams focus on communication among stakeholders to increase the ability to adopt Agile and create valuable knowledge inside the organizations, architecting process innovation.Originality/valueThe forged strategic Agile substantive theory contributes to the competitive Brazilian IT company departments. The need for velocity in organizing teams, accomplishing changes and efficient communication challenges connecting value creation with project results.

An Empirical Framework for Assessment of the Effects of Digital Technologies on Sustainability Accounting and Reporting in the European Union

Sustainability accounting and reporting is an emerging area of accounting that is receiving increasing attention as a result of sustainability requirements. In this paper, we examine the effects of implementing digital technology on sustainability accounting and reporting. This research consists of an empirical study at the level of 21 European Union countries using data provided by Eurostat. Transversal research emphasizes the impact of digital technologies (cloud computing, Big Data, the Internet of things, and artificial intelligence) on sustainability accounting and reporting. In this paper, we highlight the relationships between variables using artificial neural network analysis and cluster analysis. The study findings indicate that digital technologies significantly influence the sustainability accounting and reporting and sustainability-oriented culture of the countries included in the empirical study. A cluster analysis reveals a group of countries at the top of the sustainability reporting rankings as a result of advances in digital technologies. This study demonstrates that the digital transformation produced by Industry 4.0 contributes to the potential improvement of sustainability accounting and reporting, with significant links between sustainability and digitization.

College and university students’ attitudes towards democracy in Hungary

The existence of education for democracy has positive impact on citizens’ political knowledgeand the identification with the democratic values. In the process of civic education, theuniversities and high schools play an important role. Many scholars argue that the high schoolshave a civic mission to serve a public good or the university is the civic mission itself. To examinedemocratic citizenship among high school and university students we use a dataset composed ofthree surveys (2011/2012, 2013, 2015) of 4800 Hungarian students. We build on the literatureabout the empirical and theoretical framework of democratic citizenship to answer the questionif 25 years after the collapse of communism we can witness the emergence of a new generationof democrats in Hungary? Have young people successfully come to terms with their countries' authoritarian past and developed a commitment to democracy as a system of rule? Are theyready to defend it in the face of challenges? Based on the empirical framework of citizenship wederive a number of significant lessons from the Hungarian case, with important implicationsabout the ability to teach the norms and responsibilities of democratic citizenship in the world’semerging democracies.

An Empirical Framework for Assessing the Balanced Scorecard Impact on Sustainable Development in Healthcare Performance Measurement.

Performance appraisal has become an essential tool for healthcare managers due to the frequent and rapid changes in the healthcare sector. Sustainable performance management implies increasing organizations' efficiency and effectiveness while considering all vectors of sustainability. This study examines the impact of digital transformation, accounting information systems, and strategic human resource management improvements on organizational performance and sustainable development. The paper uses the balanced scorecard (BSC) for organizational performance assessment. The paper proposes a theoretical model that integrates the traditional and digital information systems and human resources engagement with the BSC dimensions for sustainable organizational development. The theoretical model is tested in an empirical study involving a questionnaire-based survey of 387 employees with management experience in the healthcare sector. Based on structural equation modeling, the research results showed that the efficiency and effectiveness of strategic human resources management and the accounting information system significantly positively affect the BSC dimensions. Furthermore, the use of BSC in measuring organizational performance has significant effects on sustainable development, with the internal process dimension being the most influential.

Learning With Multiclass AUC: Theory and Algorithms.

The Area under the ROC curve (AUC) is a well-known ranking metric for problems such as imbalanced learning and recommender systems. The vast majority of existing AUC-optimization-based machine learning methods only focus on binary-class cases, while leaving the multiclass cases unconsidered. In this paper, we start an early trial to consider the problem of learning multiclass scoring functions via optimizing multiclass AUC metrics. Our foundation is based on the M metric, which is a well-known multiclass extension of AUC. We first pay a revisit to this metric, showing that it could eliminate the imbalance issue from the minority class pairs. Motivated by this, we propose an empirical surrogate risk minimization framework to approximately optimize the M metric. Theoretically, we show that: (i) optimizing most of the popular differentiable surrogate losses suffices to reach the Bayes optimal scoring function asymptotically; (ii) the training framework enjoys an imbalance-aware generalization error bound, which pays more attention to the bottleneck samples of minority classes compared with the traditional O(√{1/N}) result. Practically, to deal with the low scalability of the computational operations, we propose acceleration methods for three popular surrogate loss functions, including the exponential loss, squared loss, and hinge loss, to speed up loss and gradient evaluations. Finally, experimental results on 11 real-world datasets demonstrate the effectiveness of our proposed framework. The code is now available at https://github.com/joshuaas/Learning-with-Multiclass-AUC-Theory-and-Algorithms.

Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity.

Leveraging information in aggregate data from external sources to improve estimation efficiency and prediction accuracy with smaller scale studies has drawn a great deal of attention in recent years. Yet, conventional methods often either ignore uncertainty in the external information or fail to account for the heterogeneity between internal and external studies. This article proposes an empirical likelihood-based framework to improve the estimation of the semiparametric transformation models by incorporating information about the t-year subgroup survival probability from external sources. The proposed estimation procedure incorporates an additional likelihood component to account for uncertainty in the external information and employs a density ratio model to characterize population heterogeneity. We establish the consistency and asymptotic normality of the proposed estimator and show that it is more efficient than the conventional pseudopartial likelihood estimator without combining information. Simulation studies show that the proposed estimator yields little bias and outperforms the conventional approach even in the presence of information uncertainty and heterogeneity. The proposed methodologies are illustrated with an analysis of a pancreatic cancerstudy.

Design Principles for Considering the Participatory Relationship of Students, Teachers, Curriculum, and Place in Project-Based STEM Units

Historically, STEM learning spaces and curriculum have overlooked the strengths and agency of students, teachers, and their communities. Project-based STEM units about environmental issues like water quality offer the possibility to create more expansive, equitable learning experiences. These units can leverage local problems and resources while also including the global dimensions of the issue to provide meaningful opportunities for diverse student sensemaking. However, even project-based STEM learning requires explicit attention to the agency of teachers, students, and place. In order to identify a set of design principles for supporting equitable learning in a project-based STEM curriculum, this manuscript brings together a set of empirical and theoretical frameworks including teacher participatory relationship with the curriculum, culturally sustaining pedagogy, critical pedagogy of place, and equitable sensemaking. The authors use these frameworks to describe a conceptual model of Participatory Relationship of Students, Communities, Teachers, Curriculum and Place. Then, the manuscript outlines a set of seven design principles that connect to the theoretical frameworks to the conceptual model and provide implementation strategies with examples of how we apply these design principles to a project-based STEM unit for 4–8 grade students. The design principles have implications for design of future project-based units and learning opportunities for teachers and students.

The language of opinion change on social media under the lens of communicative action

Which messages are more effective at inducing a change of opinion in the listener? We approach this question within the frame of Habermas’ theory of communicative action, which posits that the illocutionary intent of the message (its pragmatic meaning) is the key. Thanks to recent advances in natural language processing, we are able to operationalize this theory by extracting the latent social dimensions of a message, namely archetypes of social intent of language, that come from social exchange theory. We identify key ingredients to opinion change by looking at more than 46k posts and more than 3.5M comments on Reddit’s r/ChangeMyView, a debate forum where people try to change each other’s opinion and explicitly mark opinion-changing comments with a special flag called delta. Comments that express no intent are about 77% less likely to change the mind of the recipient, compared to comments that convey at least one social dimension. Among the various social dimensions, the ones that are most likely to produce an opinion change are knowledge, similarity, and trust, which resonates with Habermas’ theory of communicative action. We also find other new important dimensions, such as appeals to power or empathetic expressions of support. Finally, in line with theories of constructive conflict, yet contrary to the popular characterization of conflict as the bane of modern social media, our findings show that voicing conflict in the context of a structured public debate can promote integration, especially when it is used to counter another conflictive stance. By leveraging recent advances in natural language processing, our work provides an empirical framework for Habermas’ theory, finds concrete examples of its effects in the wild, and suggests its possible extension with a more faceted understanding of intent interpreted as social dimensions of language.

Dynamic Coupon Targeting Using Batch Deep Reinforcement Learning: An Application to Livestream Shopping

We present an empirical framework for creating dynamic coupon targeting strategies using deep reinforcement learning.

Multiple treatments with strategic substitutes

We develop an empirical framework to identify and estimate the effects of treatments on outcomes of interest when the treatments are the result of strategic interaction (e.g., bargaining, oligopolistic entry, peer effects). We consider a model where agents play a discrete game of complete information and strategic substitutability, whose equilibrium actions (i.e., binary treatments) determine a post-game outcome in a nonseparable model with endogeneity. Due to the simultaneity in the first stage, the model as a whole is incomplete and the selection process fails to exhibit the conventional monotonicity. Without imposing parametric restrictions or large support assumptions, this poses challenges in recovering treatment parameters. To address these challenges, we establish a monotonic pattern of the equilibria in the first-stage game in terms of the number of treatments selected. Based on this finding, we derive bounds on the average treatment effects (ATE’s) under nonparametric shape restrictions and the existence of excluded exogenous variables. We show that the instrument variation that compensates strategic substitution helps solve the multiple equilibria problem. We apply our method to data on airlines and air pollution in cities in the U.S. We find that (i) the causal effect of each airline on pollution is positive, and (ii) the effect is increasing in the number of firms but at a decreasing rate.

Extreme risk connectedness among global major financial institutions: Links to globalization and emerging market fear

Using high frequency data and “realized jumps” to depict extreme risk of stock returns, we propose a novel empirical framework to estimate and visualize the network structure of extreme risk among the global major financial institutions. With this model, we construct a series of indices to describe how the extreme risk connectedness evolves over time at the institution, country and global levels, and study the underlying factors driving the connectedness. Our results show that the degree of globalization, in particular, political globalization plays the most important role in driving a country’s importance in global extreme risk propagation. We also identify the “emerging market fear”, which is evidenced by the increased importance of emerging countries in the global financial extreme risk propagation; the increased proportion of emerging market-based institutions in the systemically important financial institution cohort; and a stronger granular impact of emerging market extreme risk on the global financial market stability. These results have important implications on managing systemic risk of global financial system.