This study investigates the specific factors affecting blockchain or the usage intention of distributed ledger technology (DLT), specifically availability, diversity, and economic value, from the perspective of a unified theory of technology acceptance. Users of DLT in public and private sectors were surveyed. Using a structural equation model, the results indicate that availability and economic value affect performance expectancy, while availability, diversity, and economic value have an influence on effort expectancy. Performance expectancy and transparency have a positive effect on the intention to use DLT, which in turn exerts a positive effect on usage behavior. This study provides implications for researchers in that it attempts to investigate the factors directly (like performance expectancy and transparency) or indirectly (like availability and economic value) affecting the usage intention of DLT based on the extended unified theory of acceptance and encompassing diverse industries that adopt DLT, such as the public, IT, financial, service medical, and logistics sectors.

This paper proposes a long range-frequency hopping spread spectrum (LR-FHSS) transceiver design for the Direct-to-Satellite Internet of Things (DtS-IoT) communication system. The DtS-IoT system has recently attracted attention as a promising non-terrestrial network (NTN) solution to provide high-traffic and delay-tolerant data transfer services, such as wide-area situational awareness (WASA) in smart grids and car share management in automotive applications, to IoT devices in global coverage. In particular, this study provides guidelines for the overall DtS-IoT system architecture and design details that conform to the Long Range Wide-Area Network (LoRaWAN). Furthermore, we also detail various DtS-IoT use cases. Considering low-Earth orbit (LEO) satellites, we develop the LR-FHSS transceiver to improve system efficiency, which is a leading attempt to build practical satellite communication systems using LR-FHSS, excluding commercial products. Moreover, we apply a robust synchronization scheme against the Doppler effect and co-channel interference (CCI) caused by LEO satellite channel environments, including signal detection for the simultaneous reception of numerous frequency hopping signals and an enhanced softoutput-Viterbi-algorithm (SOVA) for the header and payload receptions. Lastly, we present proof-of-concept implementation and testbeds using an application-specific integrated circuit (ASIC) chipset and a fieldprogrammable gate array (FPGA) that verify the performance of the proposed LR-FHSS transceiver design of DtS-IoT communication systems. The laboratory test results reveal that the proposed LR-FHSS-based framework with the robust synchronization technique can provide wide coverage, seamless connectivity, and high-throughput communication links for the realization of future satellite communication networks.

Previous studies have extensively investigated the effects of online word-of-mouth (eWOM) factors such as volume and valence on product sales. However, studies of the effect of eWOM factors on product prices are lacking. It is necessary to examine how various eWOM factors can either explain or affect product prices. The objective of this study is to suggest explanatory and predictive analytics using a regression analysis and ensemble-based machine learning methods for eWOM factors and hotels booking prices. This study utilizes publicly available data from a hotel booking site to build a sample of eWOM factors. The final study sample was comprised of 927 hotels. The important eWOM factors found to affect hotel prices are the review depth and the review rating, which are moderated by a number of reviews to affect prices. The effect of the number of positive words is moderated by the review helpfulness to affect the price. The review depth and rating, along with the number of reviews, should be considered in the design of hotel services, as these provide the rationale for adjusting the prices of various aspects of hotel services. Furthermore, the comparison results when applying various ensemble-based machine learning methods to predict prices using eWOM factors based on a 46-fold cross-validation partition method indicated that ensemble methods (bagging and boosting) based on decision trees outperformed ensemble methods based on k-nearest neighbor methods and neural networks. This shows that bagging and boosting methods are effective ways to improve the prediction performance outcomes when using decision trees. The explanatory and predictive analytics using eWOM factors for hotel booking prices offers a better understanding in terms of how the accommodation prices of hotel services can be explained and predicted by eWOM factors.

With an accelerating increase of business benefits produced from big data analytics (if used appropriately and intelligently by businesses in the private and public sectors), this study focused on empirically identifying the big data analytics (BDA) attributes. These attributes were classified into four groups (i.e., value innovation, social impact, precision, and completeness of BDA quality) and were found to influence the decision-making performance and business performance outcomes. A structural equation modeling analysis using 382 responses from a BDA related to practitioners indicated that the attributes of representativeness, predictability, interpretability, and innovativeness as related to value innovation greatly enhanced the decision-making confidence and effectiveness of decision makers who make decisions using big data. In addition, individuality, collectivity, and willfulness, which are related to social impact, also greatly improved the decision-making confidence and effectiveness of the same decision makers. This shows that the value innovation and social impact, which have received relatively less attention in previous studies, are the crucial attributes for BDA quality as they influence the decision-making performance. Comprehensiveness, factuality, and realism, which are linked to completeness, also have similar results. Furthermore, the higher the decision-making confidence of the decision makers who used big data was, the higher the financial performance of their companies. In addition, high decision-making confidence using big data was found to improve the nonfinancial performance metrics such as customer satisfaction and quality levels as well as product development capabilities. High decision-making effectiveness with big data was also shown to improve the nonfinancial performance metrics.

Covert communications, or covert channels, are commonly exploited to establish a data exfiltration channel from an insider on a trusted network to a malicious receiver outside the network without using normal communication of the network. It is because the malicious receiver is an unauthorized user of the communication network and so he cannot communicate with any entity in the network. In this study, we construct a new covert wireless unidirectional communication mechanism in an IEEE 802.11 environment. Our covert communication is based on a covert timing channel exploiting the beacon interval of a given commercial-like AP. Because the wireless covert channel we proposed can be implemented only with firmware modification to the WLAN MAC protocol, it is very suitable for application in a real public AP environment. In order to dramatically reduce the chance of covert signals being detected by others, a new and simple covert data encoding scheme, called ping-pong covert timing channel (PPCTC), is proposed, and we show that the covertness of the PPCTC is excellent compared to the previous timing-based covert channels. Although this wireless covert communication is unidirectional communication, since PPCTC has recovery characteristics against consecutive 2-bit errors, stable communication is guaranteed. Furthermore, a covert frame structure is presented for providing the confidentiality and integrity of the information transmitted via our covert channel. To the best of our knowledge, this is the first attempt.

Online streaming contents are creating greater service uncertainty, as consumers need to experience such contents before making a decision to continue to purchase them. Few studies have investigated the interaction between eWOM (online word-of-mouth) and online streaming content service characteristics with regard to the performance of online streaming contents and explained how this interaction can promote the role of service characteristics in service performance outcomes or remedy service uncertainty attributable to these characteristics. Thus, in order to test the interaction effects, this paper examines the moderating effects of service (webtoon) characteristics (i.e., author experience, genre (drama or fantasy), completion, transfer to paid service, and publication time (Wednesday)) on the relationship between eWOM and certain online streaming contents’ service performance measures; in this case, the publication period and content gamification. Based on scrawled data from 154 webtoons published on Naver Webtoon, a multivariate regression analysis with interaction terms showed that author experience and genre interact with the number of reviews to affect gamification. The transfer to a paid service interacts crucially with review ratings and the number of reviews to influence both the publication period and gamification. Online streaming content completion and publication times are factors that interact with review ratings and thus affect the publication period. Service providers need to cope with service uncertainties when attempting to further their online streaming content service by considering the service characteristics as well as customers’ responses through eWOM.

This study investigates the specific factors affecting the usage intention of distributed ledger technology (DLT), such as availability, diversity and economic value from the perspective of a unified theory of technology acceptance. Users of DLT in public and private sectors were surveyed. Availability and economic value affect performance expectancy, while availability, diversity, and profitability have an influence on effort expectancy. Performance expectancy and transparency have a positive effect on the intention to use DLT which in turn exerts a positive effect on usage behavior. This study provides implications for researchers in that it attempts to investigate the factors affecting the usage intention of DLT based on the extended unified theory of acceptance and encompassing diverse industries that adopt DLT, such as the public, financial, medial, and logistics sectors.

It has become increasingly important to consider the efficiency of movies in creating box revenue while using fewer movie resources. Further, there is a lack of eWOM (online-word-of-mouth) studies regarding using the production efficiency of movies as a dependent outcome measure replacing box revenue. This study shows that production efficiency can be suggested by comparing movie resources powers, i.e., powers of actors, directors, distributors, and production companies, which are input for movie production, and the box office. For testing the validity of the measure of production efficiency, this study examines the effect of eWOM attributes, i.e., review depth, volume, rating, review sentiment, and helpfulness on production efficiency. Data envelopment analysis is adopted to produce the efficiency of movies. This study provides insights into a current movie study on eWOM by showing the effect of interaction between eWOM (review rating) and helpfulness on production efficiency. Further, this study purports to test the prediction power in predicting production efficiency using decision trees, neural networks, and logistic regression. These results show that k nearest neighbor and automated neural networks outperform the other machine learning methods in classifying efficient movies.

Hardware security primitives, also known as physical unclonable functions (PUFs), perform innovative roles to extract the randomness unique to specific hardware. This paper proposes a novel hardware security primitive using a commercial off-the-shelf flash memory chip that is an intrinsic part of most commercial Internet of Things (IoT) devices. First, we define a hardware security source model to describe a hardware-based fixed random bit generator for use in security applications, such as cryptographic key generation. Then, we propose a hardware security primitive with flash memory by exploiting the variability of tunneling electrons in the floating gate. In accordance with the requirements for robustness against the environment, timing variations, and random errors, we developed an adaptive extraction algorithm for the flash PUF. Experimental results show that the proposed flash PUF successfully generates a fixed random response, where the uniqueness is 49.1%, steadiness is 3.8%, uniformity is 50.2%, and min-entropy per bit is 0.87. Thus, our approach can be applied to security applications with reliability and satisfy high-entropy requirements, such as cryptographic key generation for IoT devices.

This paper presents the structure of terminal ASIC chip with low speed, low power, and small characteristics for satellite IoT transmission. The external connection with the sensor, flash external, and tuner will be made centering on the ASIC chip. Three transmission waveforms, such as the LoRa, I-LoRa, and LoRa-E, will be used to send the collected data to LEO satellite. As a result, the data collected from the sensor is transmitted to the ASIC chip through the sensor interface, and the ASIC chip modulates the collected data into an uplink signal and sends it to LEO satellite through the tuner.

The benefit of a smart manufacturing Industrial Internet of Things (IIoT) platform is that it can provide real-time monitoring, accurate analysis, and reporting for equipment by collecting data throughout the whole manufacturing facility. However, the increased internet connectivity of manufacturing machines or devices leads to various security vulnerabilities. In order to securely operate smart manufacturing IIoT systems in unmanned environments, it is necessary to establish a cryptographic key for protecting exchanged data between IIoT devices and stored data in the devices by using cryptographic algorithms. Especially, since the IIoT system is in an unmanned environment, the following two challenges must be solved: 1) The IIoT device must recover its own secret key without user interaction. 2) The IIoT device must prevent secret key recovery when anomaly situations such as unauthorized physical access occur. In this paper, we present a novel method to protect an IIoT device’s secret key in unmanned smart manufacturing environments, called Two-Factor Device DNA-based Fuzzy Vault scheme. To satisfy the two challenges, our proposed method generates a specific two-factor device DNA through the combination of the IIoT device’s intrinsic factor and its surrounding environments and then creates a vault set to conceal the secret key based on the two-factor device DNA. We also implement a prototype for ensuring the feasibility of our method by utilizing an EPUF and IEEE 802.15.4g receiver in a Raspberry Pi and a laptop, respectively, and then measure their performance. We then conduct experiments in an unmanned environment at the Smart Manufacturing Learning Center at Hanyang University by considering various normal and abnormal situations. Our experiment results show that the proposed method quickly extracts the secret key stored in the device in normal cases, but fails at key extraction in abnormal cases.

This study provides a basis for establishing a new strategy for customer service improvement by deriving user, system, and social-related factors that affect the perceived usefulness for continuance usage intention of mobile device applications (apps). The results indicate that innovativeness and self-efficacy exert significant moderating influence in terms of convenience and interactivity on perceived usefulness. Self-efficacy exerts a moderating effect with reliability, while innovativeness provides a moderating influence with social identity. Confirmation is affected by interactivity and social identity. Mobile service providers should adjust the functionality of their apps to improve convenience and interactivity according to the innovativeness and self-efficacy of users.

This paper aims to identify Wi-SUN devices using physical layer fingerprint. We first extract physical layer features based on the received Wi-SUN signals, especially focusing on device-specific clock skew and frequency deviation in FSK modulation. Then, these physical layer fingerprints are used to train a machine learning-based classifier and the resulting classifier finally identifies the authorized Wi-SUN devices. Preliminary experiments on Wi-SUN certified chips show that the authenticator with the proposed physical layer fingerprints can distinguish Wi-SUN devices with 100 % accuracy. Since no additional computational complexity for authentication is involved on the device side, our approach can be applied to any Wi-SUN based IoT devices with security requirements.

Our paper suggests the maximized extent of hierarchically interrelated balanced scorecard-based organizational goals (e.g., financial, customer, internal business, learning, and growth) by weighting the current status of controls processes. The efficiency of the controls processes was analyzed given the extent of maximized organizational goals to show the adjustment of controls that can proceed given the maximized extent of governance objectives. This paper uses the survey data collected from two IT service companies based in China (n = 96) and Korea (n = 191). Using a genetic algorithm, we found the optimized extent of the hierarchically interrelated organizational goals accomplished from the controls processes' weighted current status. The efficiency of the current status for the controls processes was evaluated using data envelopment analysis to produce IT and enterprise goals and governance objectives. Based on the maximized extent of the goals provided from the current status of the controls, the significantly different average efficiencies are suggested among the five classes of the controls processes. The slack analysis of controls shows the specific controls processes and the extent of controls to be reduced. This provided an indication of the direction of controls design that adjusts the level of each controls processes that can accomplish the same extent of organizational goals. The recommendation of controls design based on each control's efficiency is lacking, especially considering the organizational hierarchy of the balanced scorecard based organizational goals, including their relations with controls processes. This study intends to fill this void by suggesting and comparing the controls efficiency for five classes of controls processes and balanced scorecard-based organizational goals for junior and senior employees. Based on COBIT 5 ' s goals cascade, this research provides a useable tool that can implement an effective and efficient IS security design.

This article provides a secret key extraction aided by wireless transceiver for Internet of Things (IoT) based consumer electronic (CE) devices, which can be a promising alternative to the conventional secret storage with non-volatile memory. Exploiting the wireless transceiver as a source of secret key generation, we propose a new type of static random access memory (SRAM) physically unclonable function (PUF), especially focusing on run-time extraction from most connected CE devices without additional hardware. The response of the proposed PUF is further processed by a fuzzy extractor to reproduce the secure key, where we suggest to use low-complexity convolutional code combined with interleaver for error correction. In addition, run-time multiple readout capability of our wireless transceiver aided PUF enables to provide a helper data-less key reproduction scheme to reduce leaked information and implementation complexity. Experimental results on two platforms equipped with the IEEE 802.11 Wi-Fi and IEEE 802.15.4g Smart Utility Network (SUN) based wireless transceivers confirm that the proposed PUF and key reproduction schemes successfully generate the secret key at any time, which makes our approach easily applicable to various IoT based CE devices with security requirements.

In this article, we propose a new geomagnetic localization scheme, named ILoA, to address error accumulation and global localization. Global localization is a fundamental problem that determines the initial pose under global uncertainty. Moreover, error accumulation using inertial navigation systems (INS) impacts robustness and drift error, making it challenging to achieve reliable estimation. The magnetic field in indoor space generates a unique signature/anomaly, which can be used as a local feature. Earth's magnetic field can be easily influenced by ferromagnetic material from the indoor environment due to its weak intensity. The magnetic field vector measured by a magnetometer depends on the orientation of the sensor, which we term a direction variant. We devise a novel approach to identify location and heading through the direction-variant augmented vector. Since a magnetic field vector under varying poses can produce many different vectors, the geomagnetic map is trained with the transformation. We present experiments in two testbeds, covering open space, showing that the proposed method using the magnetic field vector is efficient for global localization and accuracy compared with a state-of-the-art approach.

While many business intelligence methods have been applied to predict movie box office revenue, the studies using an ensemble approach to predict box office revenue are almost nonexistent. In this study, we propose decision trees, k-nearest-neighbors (k-NN), and linear regression using ensemble methods and the prediction performance of decision trees based on random forests, bagging and boosting are compared with that of k-NN and linear regression based on bagging and boosting using the sample of 1439 movies. The results indicate that ensemble methods based on decision trees (random forests, bagging, boosting) outperform ensemble methods based on k-NN (bagging, boosting) in predicting box office at week 1, 2, 3 after release. Decision trees using ensemble methods provide better prediction performance than ensemble methods based on linear regression analysis in the box office at week 1 after release. This is explained by the results that after comparing the prediction performance between ensemble methods and non-ensemble methods. For decision tree methods, unlike the other methods, the prediction performance of ensemble methods is greater than that of non-ensemble methods. This shows that decision trees using ensemble methods provide better application effectiveness of ensemble methods than k-NN and linear regression analysis.

The enormous volume and largely varying quality of available reviews provide a great obstacle to seek out the most helpful reviews. While Naive Bayesian Network (NBN) is one of the matured artificial intelligence approaches for business decision support, the usage of NBN to predict the helpfulness of online reviews is lacking. This study intends to suggest HPNBN (a helpfulness prediction model using NBN), which adopts NBN for helpfulness prediction. This study crawled sample data from Amazon website and 8699 reviews comprise the final sample. Twenty-one predictors represent reviewer and textual traits as well as product traits of the reviews. We investigate how the expanded list of predictors including product, reviewer, and textual characteristics of eWOM (online word-of-mouth) has an effect on helpfulness by suggesting conditional probabilities of the binned determinants. The prediction accuracy of NBN outperformed that of the k-nearest neighbor (kNN) method and the neural network (NN) model. The results of this study can support determining helpfulness and support website design to induce review helpfulness. This study will help decision-makers predict the helpfulness of the review comments posted to their websites and manage more effective customer satisfaction strategies. When prospect customers feel such review helpfulness, they will have a stronger intention to pay a regular visit to the target website.

The social engagement of eWOM (electronic word-of-mouth) can reduce the threat of adverse selection in e-commerce. As studies that examine the social influence of eWOM are rare, the present work suggests the moderating effect of review or reviewer helpfulness and product type (experience or search goods) on the relationship between eWOM and product sales. The volume of eWOM, which is defined as the multiplication of the average length by the number of reviews, is shown to be moderated by review and reviewer helpfulness and search goods to affect product sales. Review ratings are moderated by reviewer helpfulness, and review extremity is positively (negatively) moderated by search (experience) goods and review helpfulness to affect product sales. As previous studies of differentiated sampling strategies that consider review helpfulness for predicting product sales using eWOM are lacking, this study compares the prediction power of business intelligence methods for different subsamples of products created according to high or low review and reviewer helpfulness levels. The subsample with high review or reviewer helpfulness demonstrates greater prediction performance than the subsample with low review or reviewer helpfulness when eWOM variables are used as predictors of product sales. Hence, preliminary filtering data preprocessing should consider review or reviewer helpfulness as a crucial criterion of the data quality. This will contribute to the sampling or preprocessing strategy used to predict product sales using eWOM.

Mobile social apps have experienced enormous growth as online personal networking media. Social exchange theory (for individual motivation), theories of collective action and social capital theory (for social capital) can be applied in order to understand how an individual’s behavior may exert effects on or receive influences from other users with regard to the continuance usage intention of mobile social apps. This study examines individual motivations and social capital affecting relationship quality in terms of trust in and satisfaction with mobile social apps and how these factors influence continuance usage intentions of mobile social apps. An online survey is used to collect 320 responses from users of mobile social apps. Our results indicate that promotional motivation and innovativeness affect relationship quality levels. Maintaining relational enhancement, social homogeneity, and social identity along with service usefulness have effects on the relationship quality level, which in turn affects continuance usage intention. Given the lack of studies regarding the application of the theories of collective action and social capital to gain a better understanding of continuance usage intentions, this study provides additional insight into how individual motivations and social capital affect continuous usage.