1,042 research outputs found

    Entrepreneurs as influencers: the impact of parasocial interactions on communication outcomes

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    Purpose: Drawing on the example of car manufacturer Tesla and its early investor Elon Musk, the purpose of this paper is to explore the connection between the personal communication activities of influential entrepreneurs on social media, the emergence of parasocial interactions (PSIs) and the related communication outcomes for the company. Design/methodology/approach: This paper conducted an online survey, recruiting 207 participants via purposive sampling. Partial least square path modeling and an independent t-test were conducted to test hypotheses. Findings: The results of this paper show that following entrepreneurs' personal social media activities amplifies PSIs, which in turn positively impact the company's communication outcomes. Organization-public relationships and purchase intentions are improved by PSI. Originality/value: To the best of the authors’ knowledge, this is one of the first studies that connects the personal and the organizational level in exploring entrepreneurial marketing. The results show that Elon Musk acts as an influential entrepreneur to effectively promote communication outcomes for Tesla. This paper illuminates the potential of entrepreneurs' personal social media activities to support the success of their ventures

    A Gravity Theory on Noncommutative Spaces

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    A deformation of the algebra of diffeomorphisms is constructed for canonically deformed spaces with constant deformation parameter theta. The algebraic relations remain the same, whereas the comultiplication rule (Leibniz rule) is different from the undeformed one. Based on this deformed algebra a covariant tensor calculus is constructed and all the concepts like metric, covariant derivatives, curvature and torsion can be defined on the deformed space as well. The construction of these geometric quantities is presented in detail. This leads to an action invariant under the deformed diffeomorphism algebra and can be interpreted as a theta-deformed Einstein-Hilbert action. The metric or the vierbein field will be the dynamical variable as they are in the undeformed theory. The action and all relevant quantities are expanded up to second order in theta.Comment: 28 pages, v2: coefficient in equ. (10.15) corrected, references added, v3: references added, published versio

    Analisis Tekno-Ekonomi Hibrid Sistem PLTD PLTS Di Pulau Gersik, Belitung Menggunakan Perangkat Lunak Homer

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    Pulau Gersik adalah salah satu desa di Kecamatan Selat Nasik yang terletak di Kabupaten Belitung. Sistem pembangkit listrik di Pulau Gersik merupakan sistem terisolasi yang disuplai oleh PLTD. Sulitnya pengiriman bahan bakar PLTD ke Pulau Gersik melatarbelakangi penelitian untuk menggabungkan PLTD berbahan bakar fosil dengan PLTS menjadi suatu sistem pembangkit hibrid. Untuk itu penelitian ini bertujun untuk mensimulasikan sistem pembangkit hibrid dan menganalisis kelayakannya dari sisi teknis dan ekonomis menggunakan perangkat lunak HOMER. Dari hasil simulasi diketahui bahwa sistem pembangkit hibrid layak beroperasi dilihat dari sisi teknis, dengan energi listrik yang dihasilkan adalah 268.101 kWh/tahun. Persentase pembebanan pada sistem hibrid ini adalah 28,9% dari PLTS dan 71,1% dari PLTD. Dilihat dari sisi ekonomi, Net Present Cost dari sistem pembangkit hibrid lebih rendah daripada PLTD eksisting, yaitu sebesar Rp17.184.340.000. Selain itu Levelized Cost of Energy sistem pembangkit hibrid juga lebih rendah daripada PLTD eksisting, yaitu sebesar Rp5.144,68/kWh

    Permanent Voters Card (PVC) not Automated Teller Machine (ATM) the Problem of Cash and Carry Politics in Nigeria: What Role for the Media?

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    Anecdotal evidence has demonstrated that vote buying and selling remain a humongous challenge towards empowering the right people who can properly steer the affairs of any country. In other words, the above statement underscores the fact that when electorates sell their fundamental rights to bring to power the right leaders who ensure good governance, they consequently suffer from the outcome of such mistake. Put differently, when voters who could use their permanent voters card to choose the right people into the government decide to exchange their votes for cash, they end up suffering the consequences of such wrong decision. Vote buying or what this study refers to as "cash and carry politics" has remained an issue of great concern in recent time. Sadly, from 1999 till date, studies have shown that most candidates, both in primary and general elections have been implicated in vote-buying. According to reports, delegates in the recently concluded 2022 primary elections were paid as much as $9000 by the two political parties, (APC and PDP) to persuade them to vote for certain candidates. This type of situation no doubt spells immense doom to the survival of democracy and enthronement of the good governance in Nigeria, the reason being that he who pays the highest dollars apparently gets the highest votes. It is against the foregoing that this paper examines the subject of votebuying in Nigeria and the challenges it has posed to the survival of Nigeria's democracy. The paper also explores the theoretical trajectories of vote-buying in Nigeria and expansively provides not only insights, but probable ways that the Nigerian media can lend their voice towards addressing this problem

    Structuring the Quest for Strategic Alignment of Artificial Intelligence (AI): A Taxonomy of the Organizational Business Value of AI Use Cases

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    The deployment of Artificial Intelligence (AI) in businesses is said to provide significant benefits to organizations. However, many businesses struggle to align single AI use cases with the overall strategic business value contribution. Thus, we investigate the strategic characteristics that determine the business value contribution of AI use cases at an organizational level. We draw on academic literature and 106 AI use cases to develop a conceptually sound and empirically grounded taxonomy of the organizational business value of AI use cases. With the developed taxonomy, decision-makers are presented with a tool to systematically align AI use cases with strategic objectives. Moreover, our findings reveal how an AI use case can generate different business value contributions in different contexts, which provides researchers with a conceptual frame for informing their empirical research endeavors at the organizational level

    Long-Term Volatility Shapes the Stock Market’s Sensitivity to News

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    We show that the S&P 500’s instantaneous response to surprises in U.S. macroeconomic announcements depends on the level of long-term stock market volatility. When long-term volatility is high, stock returns are more sensitive to news, and there is a pronounced asymmetry in the response to good and bad news. We explain this by combining the Campbell-Shiller log-linear present value framework with a two-component volatility model for the conditional variance of cash flow news and allowing for volatility feedback. In our model, innovations to the long-term volatility component are the most important driver of discount rate news. Large announcement surprises lead to upward revisions in future required returns, which dampens/amplifies the effect of good/bad news

    Rain event detection in commercial microwave link attenuation data using convolutional neural networks

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    Quantitative precipitation estimation with commercial microwave links (CMLs) is a technique developed to supplement weather radar and rain gauge observations. It is exploiting the relation between the attenuation of CML signal levels and the integrated rain rate along a CML path. The opportunistic nature of this method requires a sophisticated data processing using robust methods. In this study we focus on the processing step of rain event detection in the signal level time series of the CMLs, which we treat as a binary classification problem. This processing step is particularly challenging, because even when there is no rain, the signal level can show large fluctuations similar to that during rainy periods. False classifications can have a high impact on falsely estimated rainfall amounts. We analyze the performance of a convolutional neural network (CNN), which is trained to detect rainfall-specific attenuation patterns in CML signal levels, using data from 3904 CMLs in Germany. The CNN consists of a feature extraction and a classification part with, in total, 20 layers of neurons and 1.4√ó105 trainable parameters. With a structure inspired by the visual cortex of mammals, CNNs use local connections of neurons to recognize patterns independent of their location in the time series. We test the CNN's ability to recognize attenuation patterns from CMLs and time periods outside the training data. Our CNN is trained on 4 months of data from 800 randomly selected CMLs and validated on 2 different months of data, once for all CMLs and once for the 3104 CMLs not included in the training. No CMLs are excluded from the analysis. As a reference data set, we use the gauge-adjusted radar product RADOLAN-RW provided by the German meteorological service (DWD). The model predictions and the reference data are compared on an hourly basis. Model performance is compared to a state-of-the-art reference method, which uses the rolling standard deviation of the CML signal level time series as a detection criteria. Our results show that within the analyzed period of April to September 2018, the CNN generalizes well to the validation CMLs and time periods. A receiver operating characteristic (ROC) analysis shows that the CNN is outperforming the reference method, detecting on average 76‚ÄČ% of all rainy and 97‚ÄČ% of all nonrainy periods. From all periods with a reference rain rate larger than 0.6‚ÄČmm‚ÄČh‚ąí1, more than 90‚ÄČ% was detected. We also show that the improved event detection leads to a significant reduction of falsely estimated rainfall by up to 51‚ÄČ%. At the same time, the quality of the correctly estimated rainfall is kept at the same level in regards to the Pearson correlation with the radar rainfall. In conclusion, we find that CNNs are a robust and promising tool to detect rainfall-induced attenuation patterns in CML signal levels from a large CML data set covering all of Germany

    Rain event detection in commercial microwave link attenuation data using convolutional neural networks

    Get PDF
    Quantitative precipitation estimation with commercial microwave links (CMLs) is a technique developed to supplement weather radar and rain gauge observations. It is exploiting the relation between the attenuation of CML signal levels and the integrated rain rate along a CML path. The opportunistic nature of this method requires a sophisticated data processing using robust methods. In this study we focus on the processing step of rain event detection in the signal level time series of the CMLs, which we treat as a binary classification problem. This processing step is particularly challenging, because even when there is no rain, the signal level can show large fluctuations similar to that during rainy periods. False classifications can have a high impact on falsely estimated rainfall amounts. We analyze the performance of a convolutional neural network (CNN), which is trained to detect rainfall-specific attenuation patterns in CML signal levels, using data from 3904 CMLs in Germany. The CNN consists of a feature extraction and a classification part with, in total, 20 layers of neurons and 1.4√ó105 trainable parameters. With a structure inspired by the visual cortex of mammals, CNNs use local connections of neurons to recognize patterns independent of their location in the time series. We test the CNN\u27s ability to recognize attenuation patterns from CMLs and time periods outside the training data. Our CNN is trained on 4 months of data from 800 randomly selected CMLs and validated on 2 different months of data, once for all CMLs and once for the 3104 CMLs not included in the training. No CMLs are excluded from the analysis. As a reference data set, we use the gauge-adjusted radar product RADOLAN-RW provided by the German meteorological service (DWD). The model predictions and the reference data are compared on an hourly basis. Model performance is compared to a state-of-the-art reference method, which uses the rolling standard deviation of the CML signal level time series as a detection criteria. Our results show that within the analyzed period of April to September 2018, the CNN generalizes well to the validation CMLs and time periods. A receiver operating characteristic (ROC) analysis shows that the CNN is outperforming the reference method, detecting on average 76‚ÄČ% of all rainy and 97‚ÄČ% of all nonrainy periods. From all periods with a reference rain rate larger than 0.6‚ÄČmm‚ÄČh‚ąí1, more than 90‚ÄČ% was detected. We also show that the improved event detection leads to a significant reduction of falsely estimated rainfall by up to 51‚ÄČ%. At the same time, the quality of the correctly estimated rainfall is kept at the same level in regards to the Pearson correlation with the radar rainfall. In conclusion, we find that CNNs are a robust and promising tool to detect rainfall-induced attenuation patterns in CML signal levels from a large CML data set covering all of Germany
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