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The purpose of this paper is to assess the viability of the scholarly search engine Microsoft Academic (MA) as a citation source for evaluating/ranking marketing journals.

This study performs a comparison between MA and Google Scholar (GS) in terms of journal coverage, h-index values and journal rankings.

Findings indicate that: MA (vs GS) covers 96.80 percent (vs 97.87 percent) of the assessed 94 marketing-focused journals; the MA-based h-index exhibits values that are 35.45 percent lower than the GS-based h-index; and that the MA-based ranking and the GS-based ranking are highly consistent. Based on these findings, MA seems to constitute a rather viable citation source for assessing a marketing journal’s impact.

This study focuses on one discipline, that is, marketing.

This study identifies some issues that would need to be fixed by the MA’s development team. It recommends some further enhancements with respect to journal title entry, publication year allocation and field classification. It also provides two up-to-date rankings for more than 90 marketing-focused journals based on actual cites (October 2018) of articles published between 2013 and 2017.

The interval multi-objective optimization problems (IMOPs) are universal and vital uncertain optimization problems. In this study, an interval multi-objective grey wolf optimization algorithm (GWO) based on fuzzy system is proposed to solve IMOPs effectively.

First, the classical genetic operators are embedded into the interval multi-objective GWO as local search strategies, which effectively balanced the global search ability and local development ability. Second, by constructing a fuzzy system, an effective local search activation mechanism is proposed to save computing resources as much as possible while ensuring the performance of the algorithm. The fuzzy system takes hypervolume, imprecision and number of iterations as inputs and outputs the activation index, local population size and maximum number of iterations. Then, the fuzzy inference rules are defined. It uses the activation index to determine whether to activate the local search process and sets the population size and the maximum number of iterations in the process.

The experimental results show that the proposed algorithm achieves optimal hypervolume results on 9 of the 10 benchmark test problems. The imprecision achieved on 8 test problems is significantly better than other algorithms. This means that the proposed algorithm has better performance than the commonly used interval multi-objective evolutionary algorithms. Moreover, through experiments show that the local search activation mechanism based on fuzzy system proposed in this study can effectively ensure that the local search is activated reasonably in the whole algorithm process, and reasonably allocate computing resources by adaptively setting the population size and maximum number of iterations in the local search process.

This study proposes an Interval multi-objective GWO, which could effectively balance the global search ability and local development ability. Then an effective local search activation mechanism is developed by using fuzzy inference system. It closely combines global optimization with local search, which improves the performance of the algorithm and saves computing resources.

The transportation of fresh food requires cold chain logistics to maintain a low-temperature environment, which can reduce food waste and ensure product safety. Therefore, temperature control is a major challenge that cold chain logistics face.

This research proposes a prediction model of refrigerated truck temperature and air conditioner status (air speed and air temperature) based on hybrid mayfly algorithm (MA) and extreme learning machine (ELM). To prove the effectiveness of the proposed method, the mayfly algorithm–extreme learning machine (MA-ELM) is compared with the traditional ELM and the ELM optimized by classical biological-inspired algorithms, including the genetic algorithm (GA) and particle swarm optimization (PSO). The assessment is conducted through two experiments, including temperature prediction and air conditioner status prediction, based on a case study.

The prediction method is evaluated by five evaluation indicators, including the mean relative error (MRE), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE) and coefficient of determination (R²). It can be concluded that the biological algorithm, especially the MA, can improve the prediction accuracy. This result clearly proves the effectiveness of the proposed hybrid prediction model in revealing the nonlinear patterns of the cold chain logistics temperature.

The case study illustrates the effectiveness of the proposed temperature prediction method, which helps to keep the product fresh. Even though the performance of MA is better than GA and PSO, the MA has the disadvantage of premature convergence. In the future, the modified MA can be designed to improve the performance of MA-ELM.

In prior studies, many scholars have conducted related research on the subject of temperature monitoring. However, this monitoring method can only identify temperature deviations that have occurred that harmed fresh food. As a countermeasure, research on the temperature prediction of cold chain logistics that can actively identify temperature changes has become the focus. Once a temperature deviation is predicted, temperature control measures can be taken in time to resolve the risk.

Smart card-based E-payment systems are receiving increasing attention as the number of implementations is witnessed on the rise globally. Understanding of user adoption behavior of E-payment systems that employ smart card technology becomes a research area that is of particular value and interest to both IS researchers and professionals. However, research interest focuses mostly on why a smart card-based E-payment system results in a failure or how the system could have grown into a success. This signals the fact that researchers have not had much opportunity to critically review a smart card-based E-payment system that has gained wide support and overcome the hurdle of critical mass adoption. The Octopus in Hong Kong has provided a rare opportunity for investigating smart card-based E-payment system because of its unprecedented success. This research seeks to thoroughly analyze the Octopus from technology adoption behavior perspectives.

Cultural impacts on adoption behavior are one of the key areas that this research posits to investigate. Since the present research is conducted in Hong Kong where a majority of population is Chinese ethnicity and yet is westernized in a number of aspects, assuming that users in Hong Kong are characterized by eastern or western culture is less useful. Explicit cultural characteristics at individual level are tapped into here instead of applying generalization of cultural beliefs to users to more accurately reflect cultural bias. In this vein, the technology acceptance model (TAM) is adapted, extended, and tested for its applicability cross-culturally in Hong Kong on the Octopus. Four cultural dimensions developed by Hofstede are included in this study, namely uncertainty avoidance, masculinity, individualism, and Confucian Dynamism (long-term orientation), to explore their influence on usage behavior through the mediation of perceived usefulness.

TAM is also integrated with the innovation diffusion theory (IDT) to borrow two constructs in relation to innovative characteristics, namely relative advantage and compatibility, in order to enhance the explanatory power of the proposed research model. Besides, the normative accountability of the research model is strengthened by embracing two social influences, namely subjective norm and image. As the last antecedent to perceived usefulness, prior experience serves to bring in the time variation factor to allow level of prior experience to exert both direct and moderating effects on perceived usefulness.

The resulting research model is analyzed by partial least squares (PLS)-based Structural Equation Modeling (SEM) approach. The research findings reveal that all cultural dimensions demonstrate direct effect on perceived usefulness though the influence of uncertainty avoidance is found marginally significant. Other constructs on innovative characteristics and social influences are validated to be significant as hypothesized. Prior experience does indeed significantly moderate the two influences that perceived usefulness receives from relative advantage and compatibility, respectively. The research model has demonstrated convincing explanatory power and so may be employed for further studies in other contexts. In particular, cultural effects play a key role in contributing to the uniqueness of the model, enabling it to be an effective tool to help critically understand increasingly internationalized IS system development and implementation efforts. This research also suggests several practical implications in view of the findings that could better inform managerial decisions for designing, implementing, or promoting smart card-based E-payment system.

The purpose of this paper is to shed light on the age-old trade-and-economic-growth controversy. The authors do so by utilizing the data relating to the G-20 countries between 1988 and 2013.

The authors seek to establish the formal statistical links between openness to trade and economic growth in the context of interactions with financial depth, gross capital formation, and foreign direct investment. The authors use a panel vector autoregressive model to obtain the estimates. The authors check for the robustness of the results.

The authors find that all the variables are cointegrated. That is, there is a long-run equilibrium relationship between the variables. Moreover, trade openness, financial depth, gross capital formation, and foreign direct investment are all causative factors for the economic growth of the G-20 countries in the long run. At the same time, the short-run results demonstrate that there is a myriad of causal links between these variables.

The decision makers in the G-20 countries wishing to encourage economic growth in the long run should pay close attention to trade openness, financial depth, gross capital formation, and foreign direct investment inflows to their countries.

The authors study an important group of countries over a long span of time, using advanced panel data techniques. The results demonstrate that future studies on economic growth that do not simultaneously consider trade openness, financial depth, foreign direct investment, and gross capital formation will offer biased or misguided results.

This study attempts to identify and analyze the pragmatic functions of religious expressions, that is, invocations that include the name of Allah (God), in naturally occurring social interactions in Najdi Arabic, which is spoken in Central Saudi Arabia.

Drawing on the speech act theory and politeness model, an analysis of the data illustrates that religious expressions, in addition to their prototypical religious meanings and uses in everyday interactions, are employed to communicate a wide range of pragmatic functions.

These include signaling the end of a conversation, persuading, mitigating and hedging, showing agreement and approval, reinforcing emphasis, expressing emotions, seeking protection from the evil eye, conveying skepticism and ambiguity, expressing humor and sarcasm, and showing respect and honor. The embedded multifunctional dimension of religious expressions in the present data is interpreted as serving as a politeness marker with which speakers promote both positive politeness (by showing solidarity, claiming common grounds, and building rapport) and negative politeness (by reducing imposition and emphasizing personal autonomy).

This study further highlights the interplay between religion, culture, and language use in Najdi Arabic.

Through a systematic review of the literature, this study analyzes the empirical literature on the adoption of environmental sustainability strategies in order to identify (i) the main conceptual aspects by which environmental sustainability strategies can be conceived of, (ii) the main determinants of the adoption of such strategies, (iii) the expected impacts on the company’s international performance, as well as (iv) the mechanisms that mediate the effect of environmental sustainability strategies on international performance. This study thereby offers propositions about the relationships between environmental sustainability strategies, their determinants (both in relation to the institutional environment and to the company’s domain), and their performance implications.

The gastronomy industry is one of the industries in which consumers use technology most frequently, such as robotics, artificial intelligence and automation systems. Service robots are generally defined as online programs that can act independently or under the control of a human that can carry human characteristics such as speaking, understanding and acting. This study aims to examine and evaluate the studies on service robots in the gastronomy industry regarding quantity or quality, to understand the phenomenon more comprehensively, and to determine a holistic perspective. To this end, a qualitative research approach was adopted. Document technique was used to collect data within a systematic literature review framework, and the data were analysed by descriptive analysis. According to the findings, the papers on service robots in the gastronomy industry are still in their infancy, and these studies have increasingly gained speed in the last five years.

We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

This paper aims to provide an introductory overview and selected annotated bibliography of recent resources on library instruction and information literacy across all library types.

It introduces and annotates English-language periodical articles, monographs, dissertations and other materials on library instruction and information literacy published in 2014.

It provides information about each source, discusses the characteristics of current scholarship and highlights sources that contain unique or significant scholarly contributions.

The information may be used by librarians and interested parties as a quick reference to literature on library instruction and information literacy.