Empirical Framework Research Articles

The aftermath of ballot box success and failure: evidence from land preservation referendums

AbstractState and local governments put hundreds of referendums on the ballot each year. Often, they pass but sometimes they fail. What happens after a successful or failed attempt at the ballot box? Do advocates go back to voters with another request? And if they do, do they tend to succeed? We employ a regression discontinuity empirical framework to causally estimate referendum dynamics in the arena of land conservation. Our results suggest municipalities where a referendum just barely fails hold about 0.5 more referendums and pass about 0.28 more referendums than municipalities that just barely pass, meaning initial defeat is often reversed. We also investigate whether strategic changes are made in election approaches for those that try again. We find no evidence of systematic patterns in strategic revisions for municipalities that fail their first referendum. However, when revisions are made, our evidence suggests that voters appear to respond positively.

L’activité phatique et régulatrice dans la conversation en fulfulde-funangeere : aspects interactionnels et relationnels

Phatic and Feedback Activity in Conversation in Fulfulde-Funangeere: interactional and relational aspects
 This article focuses on the collective construction of discourse through the prism of phatic and feedback activity. The empirical framework is the informal conversation in Fulfulde. The aim of the analysis is to describe the linguistic materials used by the participants to perform the roles of speaker and addressee, respectively. The analysis focuses on the processes of establishing and maintaining communicative contact on the one hand, and on the markers of the addressee's commitment on the other. The empirical data used are the result of a sociolinguistic survey carried out through non-participant observation, a questionnaire, and the recording of five conversations in Fulfulde-Funangeere in the city of Maroua, Cameroon. The study is based on the hypothesis that phatic and backchannel activities are involved in the interactional mechanisms of synchronization of exchanges, but also have relational functions. The analyses take advantage of interctionist epistemology, notably conversational analysis (Sacks et al, 1974, Traverso, 2011) and the interpersonal relationship in discourse in interaction (Goffman, 1974; Kerbrat-Orecchioni, 1992, 2005). The results of the study underline the polyfunctional character of the linguistic units mobilized by the interactants to maintain contact and mark their socio-cognitive commitment to the exchange. It also emerges that the linguistic units identified provide both interactional and relational microfunctions, confirming the basic hypothesis. In fact, the importance given to speech in the interaction forces the participants to engage emotionally, cognitively, and socially in the interaction. The interaction then becomes the place par excellence where the socio-affective stakes are played out: appearing benevolent, suitable, highlighting one's good education, showing deference and altruism, maintaining and consolidating interpersonal ties. In short, this study of the phatic and feedback activity contributes to shed light on some aspects of the conversational style of the peoples of the Sudano-Sahelian area of the Far North of Cameroon.

The Impact of Islamic and Conventional Banking Competition on Financial Stability

The impact of banking competition on financial stability/fragility is an essential issue in the banking literature. There is much research on the association between financial stability/fragility and banking competition in developed countries. However, no prior research examines the comparison of conventional and Islamic banks. The study uses data from nine countries Pakistan, Indonesia, Malaysia, Bangladesh, Egypt, Kuwait, Oman, Iraq and Jorden for the period 2010-2020. This study uses a fixed effects model to examine the influence of competition and income diversity on bank financial stability. We study how these variables affect Islamic and conventional banks differently using an empirical framework. Our findings show a considerable positive association between competition and conventional banks' financial stability; however, this relationship was found to be insignificant for Islamic banks. Furthermore, according to our research, the diversification of income has a positive impact on the financial well-being of both Islamic and conventional banks. Our study highlights the significance of fostering trustworthy competition within the banking sector to enhance overall financial stability. Furthermore, our findings indicate that promoting healthy competition in the industry can contribute to the improvement of financial stability.

A mixed-methods study on the determinants of solar home systems utilization in rural, off-grid Nigeria

This study employs a mixed-methods research approach to scrutinize the varied determinants influencing the willingness of off-grid rural households in Kwara State, Nigeria, to adopt Solar Home Systems (SHS). Integrating quantitative survey data from 400 households, qualitative findings derived from semi-structured interviews, and secondary data, the research provides a robust empirical framework. Through the application of Interval Regression and Tobit models, the analysis pinpoints income and education as key positive drivers towards SHS adoption, yet reveals a contrasting gender divide. Interestingly, a high level of satisfaction with the current energy provision emerges as a stumbling block to SHS acceptance. The research further identifies a distinct trend: households located further away from the grid exhibit a heightened propensity to pay for SHS, signifying a higher value attribution to SHS in these off-grid areas. The findings underscore the need for targeted interventions that encapsulate the diverse characteristics of households to ensure successful SHS promotion. The comprehensive insights garnered from this study offer indispensable guidance for policymakers and energy providers, bolstering strategic efforts to enhance SHS uptake and ultimately contributing to Nigeria's shift towards a more sustainable energy future.

The impact of environmental regulation on productivity with co-production of goods and bads

Governments have implemented various environmental regulation policies to improve environmental performance by reducing undesirable economic outcomes. These policies may shock productivity as they change the input allocation, economic outputs, and pollutant emissions. One widely ignored fact in related literature is that goods and bads are co-produced, in which the latter is jointly generated as the byproduct. However, ignoring the co-production of goods and bads may bias the estimation, especially tend to conclude that environmental regulation and productivity are inversely related. In this paper, by incorporating the co-production of goods and bads into the empirical framework to measure productivity, we estimate the productivity shocks from environmental regulation, especially explore how these shocks take place. Using an exogenous instrumental variable to overcome endogeneity, we found that a country increasing environmental regulation from 25th to 50th quantiles improves its productivity by 28%, yet the magnitude of the effect would be 44% by catching up with the technology frontiers worldwide. The results suggest that environmental regulation improves productivity but depresses technology progress. Using innovation data, we provide further evidence of the finding. Furthermore, productivity shocks from market-based measures are estimated to be more substantial than those from non-market-based measures. We anticipate this paper to provide empirical evidence for a better understanding of the economic implications associated with policy interventions in the context of sustainable development.

Why Circular Business Models Fail And What To Do About It: A Preliminary Framework And Lessons Learned From A Case In The European Union (Eu)

The circular economy aims to decouple economic growth from negative environmental impacts. To achieve this goal, circular economy concepts and policies must be implemented in practice by organizations through new circular business models. However, organizations often fail to implement circular business models on the market at scale. This is a major problem in business innovation practice, while a knowledge gap about the underlying implementation challenges remains in the scientific literature. More research on the subject is needed. The objective of this study is contributing to shed light into the gap with empirical insights. Using an action research method within an EU innovation project, this article proposes a preliminary empirical framework that links the value proposition, creation, delivery, and capture dimensions of a circular business models with specific cultural, regulatory, economic, and technical barriers that might hinder implementation. Based on the framework, four lessons to support managerial action are provided. Future research might build upon this work by systematically collecting and structuring more granular empirical data about the specific reasons why new circular business models fail to be implemented by organizations, across different sectors and geographical areas.

Open Government Data and the Urban–Rural Income Divide in China: An Exploration of Data Inequalities and Their Consequences

Amidst excitement for the data revolution’s potential benefits, concerns mount over its negative impact as unequal data distribution, access, and use widen disparities between individuals and groups, highlighting the urgent need for advanced theoretical and empirical frameworks. This study investigated the impact of open government data (OGD) on the urban–rural income divide in China. Our theoretical analysis shows that the nonrival nature of data initially widens the urban–rural income divide before eventually mitigating it, resulting in an inverted U-shaped relationship. Using a multiperiod difference-in-differences specification, we found that OGD widened the urban–rural income divide between 2010 and 2019. Furthermore, cities with initially wider urban–rural income divides experienced a greater impact from OGD in expanding this divide. These findings provide valuable insights in the role of open data in addressing income inequality, and contribute to our understanding of data inequalities in the context of the data revolution.

Are incentives excessive or insufficient? The impact of R&D fiscal policies on R&D inefficiency in China

The extant literature on evaluating the influence of R&D fiscal policies primarily focused on the advantages of the input or output additionality, neglecting that excessive incentives caused by R&D fiscal policies may lead to R&D inefficiency. From the perspective of resource misallocation, this paper divides R&D inefficiency into input redundancy and output insufficiency, then further measures the impact of three typical R&D fiscal policies on R&D inefficiency based on the firm-level sample of Zhongguancun Science Park in China. A two-step empirical analytical framework is constructed in this paper, namely, the inefficiency measurement step and the influence assessment step. The results prove that R&D fiscal policies may lead to resource misallocation and aggravate R&D inefficiency, and the heterogeneous conclusions cross three policies are found. Specifically, the subsidy policy significantly promotes the increase of R&D inefficiency, which primarily reflects in the rise of input redundancy. The pre-tax additional deduction policy does not show a significant impact on the R&D inefficiency but increases the input redundancy. On the contrary, the preferential tax rate for high-tech enterprises policy effectively inhibits the inefficiency. This paper not only attempts to reveal the formation of R&D inefficiency theoretically from the perspective of resource misallocation, but also takes China as an empirical sample to provide evidence for guiding the design of more effective R&D fiscal policies.

Deciphering Truth: Teaching About the Systemic Nature of Trauma

Our work rests upon empirical and conceptual frameworks of systemically trauma-informed practice (SysTIP) to bring criticality to current conceptualizations of trauma-informed education. This paper presents (1) a description of a case-based exercise with elaborated design considerations that serve to illuminate mundane racial injustices occurring in schools as they intersect with trauma and (2) an analysis of teacher candidates’ learning, using artifacts of their classwork on the case-based exercise and their interactions with their instructors. This paper fortifies educators’ abilities to move beyond color-evasive (Annamma et al., 2017) views of trauma-informed practice and provide models of how individuals can act in anti-racist, systemically trauma-informed ways.

Cross‐category revenue spillover in brand extensions: The role of within‐category experience

AbstractBuilding on signaling theory, the current research proposes an empirical framework to help firms understand the degree to which cross‐category purchases affect the revenue generated for each category and how within‐category purchases influence the cross‐category spillover effects. The framework is applied to novel individual‐level, cross‐sectional, and time‐series transaction details from a leading lifestyle conglomerate in the Middle East. The empirical results provide strong support for the presence of revenue spillover across multiple categories of brand extensions, with the spillover being more pronounced in categories in which customers have infrequently purchased and thus had less within‐category experience. These results add to the ongoing stream of research on brand extensions by offering evidence that both within‐ and cross‐category learning play a significant role in revenue generation from brand extensions for multi‐product or service firms.

Drivers and barriers of implementing a dual VET programme in Nepal

PurposeThe authors analyse drivers and barriers of implementing and scaling-up a pilot project of a dual vocational and education programme in Nepal.Design/methodology/approachThe 5C protocol distinguishes five categories of drivers and barriers of education reforms: commitment, capacity, clients, content and context. The authors build on the 5C protocol to develop a qualitative empirical framework based on semi-structured interviews amongst stakeholders.FindingsThe results show that involved actors are committed. Capacity in terms of available resources also represents an implementation driver, but companies lack information about the programme. Consequently, industry associations should receive a more prominent role in the motivation of companies to provide training places. The content category is the most challenging implementation barrier because the programme represents a substantial change, compared to the current vocational education and training (VET) programmes in Nepal.Originality/valueFirst, the authors propose a framework for a qualitative content analysis to analyse the drivers and barriers of implementing and scaling-up education reforms. Second, the authors provide novel information about drivers and barriers of an education reform in Nepal.

Unconventional monetary policy and the stock market

We use weekly changes in the size of the Federal Reserve’s balance sheet as a policy tool that has largely been ignored in the literature to investigate the relationship between the unconventional monetary policy and stock market returns when the federal funds rate reaches the zero lower bound. Our empirical framework is based on a structural VAR that is identified using heteroscedasticity in weekly data on the components of the Fed’s balance sheet. We find evidence that the unconventional expansionary monetary policy is effective in stimulating the stock market, as it has positive and statistically significant effects on stock returns. In extending our analysis to disaggregate returns, our findings suggest heterogenous and asymmetric responses of disaggregate returns to an unconventional policy shock.

Exploratory Factor Model of Perceived Security in the COVID-19 Era

Public security, as the central axis of the state and citizen agenda, has been approached from different angles from which phases of analysis have been established; rule of law, crime prevention, administration of justice, persecution and social rehabilitation without considering citizen participation beyond complaints or demonstrations. The objective of this work was to review the structure of citizen participation to establish the different types of self-government, government and co-government among the interested parties. The theoretical, conceptual and empirical frameworks around the systematization of data for the assembly of research folders are discussed.

Exploring the association between time series features and forecasting by temporal aggregation using machine learning

When a forecast of the total value over several time periods ahead is required, forecasters are presented with two temporal aggregation (TA) approaches to produce required forecasts: i) aggregated forecast (AF) or ii) aggregate data using non-overlapping temporal aggregation (AD). Often, the recommendation is to aggregate data to a frequency relevant to the decision the eventual forecast will support and then produce the forecast. However, this might not be always the best choice and we argue that both AF and AD approaches may outperform each other in different situations. Moreover, there is a lack of evidence on what indicators may determine the superiority of each approach. We design and execute an empirical experiment framework to first explore the performance of these approaches using monthly time series of M4 competition dataset. We further turn the problem into a classification supervised learning by constructing a database consisting of features of each time series as predictor and model class labelled as AF/AD as response/outcome. We then build machine learning algorithms to investigate the association between time series features and the performance of AF and AD. Our findings suggest that both AF and AD approaches may not consistently generate accurate results for every individual series. AF is shown to be significantly better than AD for the monthly M4 time series, especially for longer horizons. We build several machine learning approaches using a set of extracted time series features as input to predict accurately whether AD or AF should be used. We find out that Random Forest (RF) is the most accurate approach in correctly classifying the outcome assessed both by statistical measures such as misclassification error, F-statistics, area under the curve, and a utility measure. The RF approach reveals that curvature, nonlinearity, seas_pacf, unitroot_pp, mean, ARCHM.LM, Coefficient of Variation, stability, linearity, and max_level_shif are among the most important features in driving the predictions of the model. Our findings indicate that the strength of trend, ARCH.LM, hurst, autocorrelation lag 1, unitroot_pp, and seas_pacf may favour AF approach, while lumpiness, entropy, nonlinearity, curvature, and strength of seasonality may increase the chance of AD performing better. We conclude the study by summarising the findings and present an agenda for further research.

Elements of Schwartz’s Model in the WVS: How Do They Relate to Other Cultural Models?

Recently Kaasa (2021) has developed a Cultural Models Synthesis Scheme (CMSS) merging together the cultural models of Hofstede, Schwartz and Inglehart. However, this theoretical framework still needs to be complemented by an empirical analysis. This exploratory study focuses on the Schwartz’s model using the ten-item battery in the World Values Survey (WVS) inspired by his questionnaire. We empirically position Schwartz’s items into the theoretical CMSS by the means of the empirical framework of Kaasa and Minkov (2022) that already includes Inglehart’s dimensions and Minkov’s (2018) revision of Hofstede’s model. The results support the placements of Schwartz’s dimensions in the CMSS. However, the results also show serious inconsistencies and contradictions regarding the keywords associated to some Schwartz’s dimensions. We highlight the need to be careful about which keywords and question wordings capture the core of those dimensions and to consider the accuracy of the names of dimension. We show that some keywords previously associated with a particular pole of the mastery versus harmony and hierarchy versus egalitarianism, might, in fact, tap aspects of the opposite pole. We also propose using the term ‘conformity’ instead of ‘harmony’. We conclude from these insights that cross-mapping different cultural models is an exercise with significant intellectual payoff.

Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy

We present a hierarchical empirical Bayesian framework for testing hypotheses about neurotransmitters’ concertation as empirical prior for synaptic physiology using ultra-high field magnetic resonance spectroscopy (7T-MRS) and magnetoencephalography data (MEG). A first level dynamic causal modelling of cortical microcircuits is used to infer the connectivity parameters of a generative model of individuals’ neurophysiological observations. At the second level, individuals’ 7T-MRS estimates of regional neurotransmitter concentration supply empirical priors on synaptic connectivity. We compare the group-wise evidence for alternative empirical priors, defined by monotonic functions of spectroscopic estimates, on subsets of synaptic connections. For efficiency and reproducibility, we used Bayesian model reduction (BMR), parametric empirical Bayes and variational Bayesian inversion. In particular, we used Bayesian model reduction to compare alternative model evidence of how spectroscopic neurotransmitter measures inform estimates of synaptic connectivity. This identifies the subset of synaptic connections that are influenced by individual differences in neurotransmitter levels, as measured by 7T-MRS. We demonstrate the method using resting-state MEG (i.e., task-free recording) and 7T-MRS data from healthy adults. Our results confirm the hypotheses that GABA concentration influences local recurrent inhibitory intrinsic connectivity in deep and superficial cortical layers, while glutamate influences the excitatory connections between superficial and deep layers and connections from superficial to inhibitory interneurons. Using within-subject split-sampling of the MEG dataset (i.e., validation by means of a held-out dataset), we show that model comparison for hypothesis testing can be highly reliable. The method is suitable for applications with magnetoencephalography or electroencephalography, and is well-suited to reveal the mechanisms of neurological and psychiatric disorders, including responses to psychopharmacological interventions.

Marketing research agency, creative agency and client relationships: a study of relationship tensions

PurposeTo this date, research on tensions has been carried out on business networks and value co-creation, with no studies exploring tensions within the marketing services context. This study aims to use the three tension categories proposed by Toth et al. (2018) and Pressey and Vanharanta (2006) to address this gap by identifying the tensions experienced in the market research agency (MRA), creative agency (CA) and client relationship, as well as the processes that increase or minimize these tensions.Design/methodology/approachA total of 25 in-depth interviews were carried out with MRA, CA and client employees. NVivo 12 was used to conduct a thematic analysis to identify the overarching processes that influenced tensions.FindingsSix second-order codes were identified, designating processes that exacerbated or minimized each of the three tensions experienced. Two new processes were identified (“adopting governance processes” and “aspects of identity formation”) which have not been previously reported. An empirical framework was developed pinpointing processes that influenced each tension category, also highlighting complex interdependencies between behavioral, emotional and structural tensions.Originality/valueThis study presents the perspectives of all actors within the marketing services triad providing a more nuanced understanding of tensions at the triadic level, as previous literature predominantly focused either on dyads or on networks. Furthermore, this study highlights important interdependencies between tension categories, providing novel contributions, as well as directions for future research.

The nature of actor engagement intensity: a classification scheme

PurposeActor engagement (AE) literature shows inconsistent understandings of engagement intensity. However, a holistic picture of the nature of AE intensity is foundational to advance empirical AE models and measurement frameworks. This paper provides a nuanced understanding of what engagement intensity is and how it unfolds on different network levels.Design/methodology/approachThis conceptual study draws from a literature review and offers a comprehensive classification scheme of AE intensity. The literature review extends beyond marketing and service research and draws from the etymology of AE intensity in management and social science, specifically, the fields of student, employee and civic engagement.FindingsThe classification scheme clarifies that AE intensity at the individual level refers to actors' affective and cognitive tone and varying magnitudes (i.e. efforts, duration, activeness) of resource investments. At the dyad level, AE intensity represents relational strength, and at the network level, it refers to the degree of connectedness in the network.Research limitations/implicationsThe research reconciles conceptual inconsistencies in the AE literature. Our classification scheme goes beyond the individual actor and actor–actor dyad and offers a holistic overview of possible ways to operationalize AE intensity in networks.Practical implicationsThe classification scheme can be used as a strategic checklist to include AE intensities of individual actors (e.g. customers and employees), relationships between these actors and network connectedness, when further developing engagement measurement tools and benchmarks.Originality/valueThis is the first study providing a comprehensive understanding of AE intensity from an individual, dyadic and network perspective.

An empirical framework for detecting speaking modes using ensemble classifier

An empirical framework for detecting speaking modes using ensemble classifier

Investigating digitally mediated temporal experience: From empiricism to ethics

Much recent scholarship in the field of media phenomenology has investigated the role of temporality in shaping our experience of digital media in everyday life, as well as the ethical and political ramifications that flow from it. Consistent with the central phenomenological tenet of locating the present as ontologically prior to any past origin, the idea is that in order to properly understand the texture of day-to-day digital navigation our focus should not be on discrete media texts or objects, but on the way they are experienced first and foremost as presents into which we find ourselves repeatedly thrown. The aim of this article is to establish an empirical framework for scrutinizing the ethical dimensions of digitally mediated temporal experience. The traces of this experience are not paths from a past or causal origin, but traces of mobility experienced as always-already presentness.