Auction theory has found vital application in cognitive radio to relieve spectrum scarcity by redistributing idle channels to those who value them most. However, countries have been slow to introduce spectrum auctions in the secondary market. This could be in part because a number of substantial conflicts could emerge for leasing the spectrum at the micro level. These representative conflicts include the lack of legislation, interference management, setting a reasonable price, etc. In addition, the heterogeneous nature of the spectrum precludes the true evaluation of non-identical channels. The information abstracted from the initial activity in terms of price paid for specific channels may not be a useful indicator for the valuation of another channel. Therefore, auction mechanisms to efficiently redistribute idle channels in the secondary market are of vital interest. In this paper, we first investigate such leading conflicts and then propose a novel Adaptive and Economically-Robust spectrum slot Group-selling scheme (AERG), for cognitive radio-based networks such as IoT, 5G and LTE-Advanced. This scheme enables group-selling behavior among the primary users to collectively sell their uplink slots that are individually not attractive to the buyers due to the auction overhead. AERG is based on two single-round sealed-bid reverse-auction mechanisms accomplished in three phases. In the first phase, participants adapt asks and bids to fairly evaluate uplink slots considering the dynamics of spectrum trading such as space and time. In the second phase, an inner-auction in each primary network is conducted to collect asks on group slots, and then, an outer-auction is held between primary and secondary networks. In the third phase, the winning primary network declares the winners of the inner-auction that can evenly share the revenue of the slots. Simulation results and logical proofs verify that AERG satisfies economic properties such as budget balance, truthfulness and individual rationality and improves the utilities of the participants.
COVID-19 has not only an impact on health aspects but also other issues, including the economy. At times of crisis that is full of challenges, individuals who prefer to compelled to do what needs to protected from the negativity caused by the disaster. One who managed to save himself in times of crisis caused a panic of buying that happens a lot in retail stores. Companies must study the efforts made by buyers related to panic buying that is happening around them. Companies can explore these insights through sentiments formed from social media user posts on social media platforms. This research explores consumer sentiment related to panic purchases using qualitative analysis with NVivo software. The results of a study of 647 posts on Twitter microblogging revealed that panic buying contained negative attitude. Based on the results of this analysis, there are tactical steps that can be taken by companies in supporting individual efforts to protect themselves from losses caused by the COVID-19 outbreak.
With the rapid diffusion of smartphone use, mobile shopping (m-shopping) has become one of the most common ways to buy and sell products and services through the wireless internet based on mobile devices. Given the importance of long-term use intentions, this paper provides an empirical analysis of factors influencing continued use intentions toward m-shopping. Six m-shopping characteristics were derived from a literature review from three perspectives, and a research model was developed based on the expectation-confirmation model by incorporating trust in conjunction with motivators of anticipation into the original framework. In addition, the moderating effect of m-shopping diffusion was examined to shed some light on the research domain. The results indicate that all m-shopping characteristics except for ubiquity had considerable influence on trust and confirmation and verify differences between early and late adopters of m-shopping.
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