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We analyze Lagrange Multiplier (LM) tests for a shift in trend of a univariate time series at an unknown date. We focus on the class of LM statistics based on nonparametric kernel estimates of the long run variance. Extending earlier work for models with nontrending data, we develop a fixed-b asymptotic theory for the statistics. The fixed-b theory suggests that, for a given statistic, kernel, and significance level, there usually exists a bandwidth such that the fixed-b asymptotic critical value is the same for both I(0) and I(1) errors. These “robust” bandwidths are calculated using simulation methods for a selection of well-known kernels. We find when the robust bandwidth is used, the supremum statistic configured with either the Bartlett or Daniell kernel gives LM tests with good power. When testing for a slope change, we obtain the surprising finding that less trimming of potential shift dates leads to higher power, which contrasts the usual relationship between trimming and power. Finite sample simulations indicate that the robust LM statistics have stable size with good power.

This study explores the influence of the coronavirus disease 2019 (COVID-19) career experience on the investment behavior and risk tolerance of chief executive officers (CEOs). Specifically, this study focuses on CEOs' abilities to allocate financial assets and maintain solvency.

This study adopts a comprehensive approach to analyze financial assets and asset-to-liability ratios. Financial data and individual information of CEOs from listed companies are collected from 2020Q1 to 2021Q4, along with statistics on confirmed COVID-19 cases. Instrumental and alternative variables are used to examine the robustness and endogeneity of the research, ensuring a thorough analysis.

A significant positive correlation is revealed between CEOs' COVID-19 career experience and their capacity to effectively allocate financial assets. However, COVID-19 has a negative effect on firm performance in terms of solvency. These findings contribute to the empirical evidence linking the pandemic to company performance, representing part of the initial research in this area.

The study suggests that the implementation of potential policy implications, such as loose monetary policies and tax and fee reduction measures, may alleviate the tax burden on listed companies.

The purpose of this study is to explore and evaluate potential nonmonotonicity in the determinants of profit efficiency, specifically IT and R&D investments in the Indian commercial banking sector.

The study employs an alternative stochastic profit efficiency framework and introduces nonmonotonic effects by parameterizing the location and scale parameters of the inefficiency component on an unbalanced panel data set of 72 commercial banks in the 2008–2019 period. Marginal effects across quartiles are calculated using a bias-corrected and accelerated bootstrap procedure of 500 simulations. The study disaggregates across ownership and size for gauging the impact of structure on the associations between determinants of profit efficiency.

The study partially rejects the productivity paradox as it discovers a negative association of IT and R&D with profit inefficiency. However, the observed nonmonotonicity of IT is of significance for bank managers, as the study concludes that overinvestment in IT is detrimental to a bank’s profit-maximizing interests. Further, bank size, loan default and credit risk depict a nonmonotonic relationship across the sample with large banks, high NPAs and high credit risk associated with reducing profit efficiency. In addition, higher margins and greater diversification are related positively to efficiency, and banks with cost-heavy structures or having high liquidity risk associated negatively with efficiency.

To the best knowledge of the authors, the study is perhaps the first to acknowledge and incorporate nonmonotonic associations of IT investments amidst other exogenous determinants under a stochastic profit efficiency framework.

We develop a multiperiod contest theory model to formulate the role of decentralization in coups decision and outcome. In our model the coup plotter chooses between carrying out a coup and subordination, the central government responds by fighting against the plotter, and the local government chooses whether to confront the military government after a successful coup. The model shows that more decentralized countries will experience longer military regime after a successful coup, but the relationship between decentralization and the risk of coups is nonmonotonic. We suggest that there may exist negative consequences of decentralization: Depending on the initial conditions, decentralization may increase the coup risks and jeopardize political stability.

The purpose of this paper is to describe a novel XML schema language called DTD Schema that solves major limitations of document type definition (DTD) and supports features that XML Schema supports in a simple and concise way.

DTD Schema is designed based on DTD and data definition language of object‐oriented and object‐relational databases. It extends DTD with namespaces, richer built‐in types and user‐defined subtypes, local elements and attributes, complex types with nonmonotonic multiple element and attribute inheritance with overriding, blocking, conflict handling, and polymorphism.

XML Schema is recommended by W3C as the schema language for XML. It uses a set of predefined XML tags to define the schema, which is often a long, intricate specification, full of details and concepts and its verbose syntax often doubles or triples the document length. It is so complicated that even XML experts do not find it human‐readable, mostly due to the XML‐based syntax.

The only limitation is that DTD Schema is not in XML. But for the same reason, it is simple and concise.

DTD schema is halfway between DTD and XML Schema and thus it is less complex and much easier for human to use than XML Schema.

DTD Schema supports all functionalities of XML Schema and also the best of object‐oriented features including multiple inheritance, overriding, blocking, conflict handling and polymorphism. Therefore, it is much more expressive than XML Schema.

Chance models – mechanisms that explain empirical regularities through unsystematic variance – have a long tradition in the sciences but have been historically marginalized in management scholarship, relative to an agentic worldview about the role of managers and organizations. An exception is the work of James G. March and his coauthors, who proposed a variety of chance models that explain important management phenomena, including the careers of top executives, managerial risk taking, and organizational anarchy, learning, and adaptation. This paper serves as a tribute to the beauty of these “little ideas” and demonstrates how they can be recombined to generate novel implications. In particular, we focus on the example of an inverted V-shaped performance association centering around the year when executives were featured in a prominent listing, Barron’s annual list of Top 30 chief executive officers. Our recombination of several chance models developed by March and his coauthors provides a novel explanation for why many of the executives’ exceptional performances did not persist. In contrast to the common accounts of complacency, hubris, and statistical regression, the results show that declines from high performance may result from the way luck interacts with these executives’ slow adaptation, incompetence, and self-reinforced risk taking. We conclude by elaborating on the normative implications of chance models, which address many current management and societal challenges. We further encourage the continued development of chance models to help explain performance differences, shifting from accounts that favor heroic stories of corporate leaders toward accounts that favor their changing fortunes.

This chapter investigates whether, and if so, how particular firms in a transition economy are involved in bribery. Built on pressure theories, we explain how the direct effects of firm characteristics and contextual characteristics determine firm bribery behavior. Entrepreneurs make choices based on perceptions of a specific pressure due to organizational characteristics (internal pressures) or due to context (external pressures). The relationship between firm characteristics, context, and bribery was estimated using unique data from a survey of 606 Vietnamese entrepreneurs. We controlled for various entrepreneurial, organizational, and industrial characteristics. The exploratory findings support firm attributes hypotheses, which is a negative relationship between firm size and bribery and a nonmonotonic U-shaped relationship between firm age and bribery. Besides, the effects of context on bribery are also found. Specifically, the result supports a positive relationship between competition and bribery and a negative relationship between the quality of the government and bribery.

Corporate managers spend on research and development (R&D) for reasons of growth and survival. However, they may be less willing to invest in R&D because of its long-term horizon, high failure rate and uncertain outcomes. This study aims to explore the extent to which managerial ownership influences R&D expenditure decisions.

Apart from the linear regression models, this study uses a semi-parametric quantile regression analysis for a sample of German non-financial firms throughout 2009–2018.

This study finds a nonmonotonic sensitivity of R&D spending to the level of managerial ownership over various quantiles of R&D distribution. That is, managerial ownership increases the expenditure on R&D at low R&D intensity firms. However, it decreases the expenditure on R&D at high R&D intensity firms. These results suggest the presence of a maximum level of R&D expenditure, after which owner-managers would be unwilling to spend on R&D.

The results confirm the importance of corporate ownership structure for firm R&D and innovation activities. It provides an implication for corporate policymakers to reform the corporate ownership structures to encourage corporate managers and owners to invest in R&D projects.

This study offers two distinct contributions study. First, it provides the first German shred of evidence on the nonlinear relationship between managerial ownership and R&D expenditure decisions by distinguishing between high and low R&D intensity firms. Second, unlike prior research, it uses a semi-parametric quantile regression analysis. This method is more efficient than least-squares estimators and produces robust estimators to heteroscedasticity of the residuals.

For metal additive manufacturing, metallic powders are usually produced by vacuum induction gas atomization (VIGA) through the breakup of liquid metal into tiny droplets by gas jets. VIGA is considered a cost-effective technique to prepare feedstock. In VIGA, the quality and the morphology of the produced particles are mainly controlled by the gas pressure used during powder production, keeping the setup configuration constant.

In VIGA process for metallic additive manufacturing feedstock preparation, the quality and morphology of the powder particles are mainly controlled by the gas pressure used during powder production.

In this study, Inconel-625 feedstock was produced using a supersonic nozzle in a close-coupled gas atomization apparatus. Powder size distribution (PSD) was studied by varying the gas pressure.

The nonmonotonic but deterministic relationships were observed between gas pressure and PSD. It was found that the maximum 15–45 µm percentage PSD, equivalent to 84%, was achieved at 29 bar Argon gas pressure, which is suitable for the LPBF process. Following on, the produced powder particles were used to print tensile test specimens via LPBF along XY- and ZX-orientations by using laser power = 475 W, laser scanning speed = 800 mm/s, powder layer thickness = 50 µm and hatch distance = 100 µm. The yield and tensile strengths were 9.45% and 13% higher than the ZX direction, while the samples printed in ZX direction resulted in 26.79% more elongation compared to XY-orientation.

This study aims to empirically investigate how susceptibility to social influence in new product adoption varies with one’s structural location in a social network.

The social network data were collected based on a sociometric network survey with 589 undergraduate students. Social network analysis and ordinary least squares regression analyses were used to test the hypotheses.

This study finds that consumers with high degree centrality (i.e. hubs) who have a large number of connections to others and consumers with high betweenness centrality (i.e. bridges) who connect otherwise distant groups in social networks are both less sensitive to informational influence from others. More importantly, the authors find evidence that consumers with moderate levels of degree/betweenness centrality are more susceptible to normative influence and status competition than those with low or high degree/betweenness centrality. The inverse-U patterns in the above relations are consistent with middle-status conformity and anxiety.

This research complements social influence and new product diffusion research by documenting important contingencies (i.e. network locations) in consumer susceptibility to different types of social influence from a social network perspective.

The findings will assist marketers to leverage social influence by activating relevant social ties with effective messages in their network marketing strategies.

This research provides a better understanding of the mechanisms driving susceptibility to social influence in new product diffusion.