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Recently, various Twitter Sentiment Analysis (TSA) techniques have been developed, but little has paid attention to the microblogging feature – emojis, and few works have been conducted on the multi-class sentiment analysis of tweets. The purpose of this paper is to consider the popularity of emojis on Twitter and investigate the feasibility of an emoji training heuristic for multi-class sentiment classification of tweets. Tweets from the “2016 Orlando nightclub shooting” were used as a source of study. Besides, this study also aims to demonstrate how mapping can contribute to interpreting sentiments.

The authors presented a methodological framework to collect, pre-process, analyse and map public Twitter postings related to the shooting. The authors designed and implemented an emoji training heuristic, which automatically prepares the training data set, a feature needed in Big Data research. The authors improved upon the previous framework by advancing the pre-processing techniques, enhancing feature engineering and optimising the classification models. The authors constructed the sentiment model with a logistic regression classifier and selected features. Finally, the authors presented how to visualise citizen sentiments on maps dynamically using Mapbox.

The sentiment model constructed with the automatically annotated training sets using an emoji approach and selected features performs well in classifying tweets into five different sentiment classes, with a macro-averaged F-measure of 0.635, a macro-averaged accuracy of 0.689 and the MAEM of 0.530. Compared to those experimental results in related works, the results are satisfactory, indicating the model is effective and the proposed emoji training heuristic is useful and feasible in multi-class TSA. The maps authors created, provide a much easier-to-understand visual representation of the data, and make it more efficient to monitor citizen sentiments and distributions.

This work appears to be the first to conduct multi-class sentiment classification on Twitter with automatic annotation of training sets using emojis. Little attention has been paid to applying TSA to monitor the public’s attitudes towards terror attacks and country’s gun policies, the authors consider this work to be a pioneering work. Besides, the authors have introduced a new data set of 2016 Orlando Shooting tweets, which will be made available for other researchers to mine the public’s political opinions about gun policies.

Following the call for awareness of accepted reporting practices by Ringle, Sarstedt, and Straub in 2012, the purpose of this paper is to review and analyze the use of partial least squares structural equation modeling (PLS-SEM) in Industrial Management & Data Systems (IMDS) and extend MIS Quarterly (MISQ) applications to include the period 2012-2014.

Review of PLS-SEM applications in information systems (IS) studies published in IMDS and MISQ for the period 2010-2014 identifying a total of 57 articles reporting the use of or commenting on PLS-SEM.

The results indicate an increased maturity of the IS field in using PLS-SEM for model complexity and formative measures and not just small sample sizes and non-normal data.

Findings demonstrate the continued use and acceptance of PLS-SEM as an accepted research method within IS. PLS-SEM is discussed as the preferred SEM method when the research objective is prediction.

This update on PLS-SEM use and recent developments will help authors to better understand and apply the method. Researchers are encouraged to engage in complete reporting procedures.

Applications of PLS-SEM for exploratory research and theory development are increasing. IS scholars should continue to exercise sound practice by reporting reasons for using PLS-SEM and recognizing its wider applicability for research. Recommended reporting guidelines following Ringle et al. (2012) and Gefen et al. (2011) are included. Several important methodological updates are included as well.

Emoji has become an essential component of any digital communication and its importance can be attested to by its sustained popularity and widespread use. However, research in Emojis is rarely to be seen due to the lack of data at a greater scale. The purpose of this paper is to systematically analyse and compare the usage of Emojis in a cross-cultural manner.

This research conducted an empirical analysis using a large-scale, cross-regional emoji usage data set from Twitter, a platform where the limited 140 characters allowance has made it essential for the inclusion of emojis within tweets. The extremely large textual data set covers a period of only two months, but the 673m tweets authored by more than 2,081,542 unique users is a sufficiently large sample for the authors to yield significant results.

This research discovered that the categories and frequencies of Emojis communicated by users can provide a rich source of data to understand cultural differences between Twitter users from a large range of demographics. This research subsequently demonstrated the preferential use of Emojis complies with Hofstede’s Cultural Dimensions Model, in which different representations of demographics and culture within countries present significantly different use of Emojis to communicate emotions.

This study provides a robust example of how to strategically conduct research using large-scale emoji data to pursue research questions previously difficult. To the best of authors’ knowledge, the present study pioneers the first systematic analysis and comparison of the usage of emojis on Twitter across different cultures; it is the largest, in terms of the scale study of emoji usage to-date.

The purpose of this paper is to understand the value creation mechanisms of open-standard inter-organizational information system (OSIOS), which is a key technology to achieve Industry 4.0. Specifically, this study investigates how the internal assimilation and external diffusion of OSIOS help manufactures facilitate process adaptability and alignment in supply chain network.

A survey instrument was designed and administrated to collect data for this research. Using three-stage least squares estimation, the authors empirically tested a number of hypothesized relationships based on a sample of 308 manufacturing firms in China.

The results of the study show that OSIOS can perform as value creation mechanisms to enable process adaptability and alignment. In addition, the impact of OSIOS internal assimilation is inversely U-shaped where the positive effect on process adaptability will become negative after an extremum point is reached.

This study contributes to the existing literature by providing insights on how OSIOS can improve supply chain integration and thus promote the achievement of industry 4.0. By revealing a U-shaped relationship between OSIOS assimilation and process adaptability, this study fills previous research gap by advancing the understanding on the value creation mechanisms of information systems deployment.

Rigorous applications of analytical tools in information systems (IS) research are important for developing new knowledge and innovations in the field. Emerging tools provide building blocks for future inquiry, practice and innovation. This article summarizes the findings of an analysis of the adoption and reporting of partial least squares structural equation modeling (PLS-SEM) analytical tools by Industrial Management & Data Systems authors in the most recent five-year period.

Selected emerging advanced PLS-SEM analytical tools that have experienced limited adoption are highlighted to broaden awareness of their value to IS researchers.

PLS-SEM analytical tools that facilitate understanding increasingly complex theoretical models and deliver improved prediction assessment are now available. IS researchers should explore the opportunities to apply these new tools to more fully describe the contributions of their research.

Findings demonstrate the increasing acceptance of PLS-SEM as a useful alternative research methodology within IS. PLS-SEM is a preferred structural equation modeling (SEM) method in many research settings and will become even more widely applied when IS researchers are aware of and apply the new analytical tools.

Emerging PLS-SEM methodological developments will help IS researchers examine new theoretical concepts and relationships and publish their work. Researchers are encouraged to engage in more complete analyses by applying the applicable emerging tools.

Applications of PLS-SEM for prediction, theory testing and confirmation have increased in recent years. Information system scholars should continue to exercise sound practice by applying these new analytical tools where applicable. Recommended guidelines following Hair et al. (2019; 2022) are included.

Firms are subject to power from both upstream and downstream partners; those partners may have different or even opposing impacts on supply chain relationships and financial performance. The purpose of this study is to investigate how upstream and downstream dependence structures affect a firm's financial performance through upstream and downstream relational depth (DEP) and relationship extendedness (EXT).

Data representing both upstream and downstream supply chain perspectives was collected using a multiple-respondent survey and was further augmented using financial performance data from an archival database.

Dependence advantages (ADVs) and disadvantages from upstream and downstream partners affect relational mechanisms and firm performance differently. Only downstream ADV will enhance a firm's DEP and EXT and subsequently affect firm's revenue and profit. Contradictory to widely held belief, the results reveal that firms that maintain long-term relationships with buyers and suppliers may experience lower revenue/profit.

This research represents a significant step in understanding the economic ramifications of dependence by (1) highlighting the difference between upstream and downstream supply chain dependence structure and (2) understanding the indirect effects of dependence structure on financial performance.

Drawing on the signaling theory and the relational exchange theory, this study investigates how buyer–supplier trust is influenced through the congruence and incongruence between blockchain and norm of solidarity. The moderating role of technology uncertainty is further examined.

Using a survey data of 110 Chinese firms, this study empirically tests not only the combined effect of blockchain and norm of solidarity on trust, but also how this combined effect is moderated by technology uncertainty. The proposed hypotheses are tested using the polynomial regression analysis and the response surface methodology.

The results suggest that trust increases along with an increasing congruence between blockchain and norm or solidarity, but in a diminishing rate (i.e. an inverted U-shaped relationship). Simultaneously, incongruence between blockchain and norm of solidarity can also guarantee sufficient trust (i.e. a U-shaped relationship). Moreover, technology uncertainty overturns the inverted U-shaped relationship between blockchain and norm of solidarity congruence on trust into a U-shaped relationship and nullifies the U-shaped relationship between blockchain and norm of solidarity incongruence on trust.

This study enriches supply chain governance literature by introducing the emerging blockchain governance and examining the blockchain governance's interplay with a conventional relational norm. The study emphasizes that the combined effects of these two are quite complex. Blockchain and norm of solidarity can offset each other’s limitations when both are at low to moderate levels. But simultaneous pursuit of both high blockchain and norm has only limited marginal benefits. Furthermore, the study also highlights the importance of technology uncertainty under which the combined effects between the two governance mechanisms vary. Collectively, the results provide nuanced insights into the design of supply chain governance portfolios in the digital era.

This study aims to examine the impact of lean manufacturing (LM) on the financial performance of companies affected by emergency situations. It additionally explores the role of advanced manufacturing technologies (AMTs) in complementing LM to enhance financial performance in emergency and non-emergency situations.

Both survey and archival data were collected from 219 manufacturing companies in China. With longitudinal data collected before and after an emergency situation (i.e. Typhoon Rumbia), regression analysis was conducted to investigate the effects of LM and AMTs on financial performance in different contexts.

Our results reveal an inverted U-shaped relationship between LM and financial performance in the context of emergency. We also found that AMTs exerted a positive moderation effect on the inverted U-shaped relationship, indicating high levels of AMTs that mitigated the inefficiency of LM in coping with supply chain emergencies.

Through simultaneous investigation of LM and AMTs as bundles of practices and their fit with different contexts, this study takes a systems approach to fit that advances the application of contingency theory in the Operations Management literature to more complex patterns of fit.

This study illuminates how AMTs support LM practices in facilitating organizational performance in different contexts. Specifically, this study unravels the interaction mechanisms between AMTs and LM in influencing financial performance in emergency and non-emergency situations.