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Microsoft Money 2001 may be simple enough to use if all you want to do is keep a checkbook. But if you want to do more, things quickly become complicated. And that's when the Ask the Expert Guide to Microsoft Money 2001 helps. Written by true experts, the Ask the Expert Guide to Microsoft Money 2001 provides easy-to-understand but expert help for people who want to use Money to better manage their financial affairs, more prudently manage their investments, and even more easily run a small business. Arranged in a question-and-answer format, the book supplies practical answers to all of the most common questions about Microsoft Money.

If just thinking about financial management gives you a headache, personal finance software is better than aspirin. Quicken is tops, and Quicken 2009 For Dummies is the quickest and easiest way to put it to work. Here's the plain-English guide to using the newest update of the nation's leading personal finance software. A leading CPA shows you how to track your finances day to day, keep your checkbook and pay bills online, and even plan for tax time with Quicken. Learn as much or as little as you need to know - just how to keep your checkbook straight, or even how Quicken helps manage stocks and the business end of rental property. Install and set up Quicken 2009, or update data files from a previous version Learn to make a budget that's flexible enough to work for your family or your business Keep your checkbook up to date, handle banking transactions online, and use Quicken calculators Print Quicken reports to help you track cash flow, identify missing checks, summarize spending, and more Set up tax-deferred or brokerage accounts and buy and sell securities Track your credit cards and bank accounts as well as mortgages, loans, and other debts Use Quicken's Home & Business or Rental Property Manager versions to keep your business books Handle payroll for business or household employees Track deductions to make tax preparation easier With Quicken 2009 For Dummies, you'll feel like a financial wizard!.

Progress in automated synthesis of organic compounds has been proceeding along parallel tracks. One goal is algorithmic prediction of viable routes to a desired compound; the other is implementation of a known reaction sequence on a platform that needs little to no human intervention. Coley et al. now report preliminary integration of these two protocols. They paired a retrosynthesis prediction algorithm with a robotically reconfigurable flow apparatus. Human intervention was still required to supplement the predictor with practical considerations such as solvent choice and precise stoichiometry, although predictions should improve as accessible data accumulate for training. Science , this issue p. eaax1566 An automated synthesis platform conducts reactions on the basis of a human-devised workflow informed by a retrosynthesis algorithm. The synthesis of complex organic molecules requires several stages, from ideation to execution, that require time and effort investment from expert chemists. Here, we report a step toward a paradigm of chemical synthesis that relieves chemists from routine tasks, combining artificial intelligence–driven synthesis planning and a robotically controlled experimental platform. Synthetic routes are proposed through generalization of millions of published chemical reactions and validated in silico to maximize their likelihood of success. Additional implementation details are determined by expert chemists and recorded in reusable recipe files, which are executed by a modular continuous-flow platform that is automatically reconfigured by a robotic arm to set up the required unit operations and carry out the reaction. This strategy for computer-augmented chemical synthesis is demonstrated for 15 drug or drug-like substances.

Using a data set on individual investments in an online crowdfunding platform for mobile applications, this study examines whether an early investor’s experience within the platform serves as a credible signal of quality for other investors in the crowd and, if so, under what conditions. We find that early investors with experience—particularly investors with app development experience and investors with app investment experience—have a disproportionate influence on later investors in the crowd. Investors with app development experience are likely to have better knowledge of the product and are therefore found to be more influential for “concept apps” (apps in the pre-release stage), while investors with app investment experience with a better knowledge of market performance are found to be more influential for “live apps” (apps that are already being sold in the market). Our findings show that the majority of investors in this market, the crowd, although inexperienced, are rather sophisticated in their ability to identify and exploit nüanced differences in the underlying expertise of the early investors, informational signals that align well with the informational needs they face in the different stages of a venture. In examining the ex post performance of apps, we find that apps with investments from investors with experience are positively associated with ex post app sales. More importantly, we find that investors with experience indeed have the ability to select better apps, making their investment choices credible signals of quality for the crowd. Contrary to popular perceptions of crowdfunding platforms as substitutes for traditional expert-dominated mechanisms, our findings indicate that participation by individuals with experience can be beneficial to these markets.

PurposeThe purpose of this study is to empirically analyse the key factors that influence the adoption of financial technology innovation in the country Germany. The advancement of mobile devices and their usage have increased the uptake of financial technology (FinTech) innovation. Financial sectors and startups see FinTech as a gateway to increase business opportunities, but mobile applications and other technology platforms must be launched to explore such opportunities. Mobile application security threats have increased tremendously and have become a challenge for both users and FinTech innovators. In this paper, the authors empirically inspect the components that influence the expectations of both users and organizations to adopt FinTech, such as customer trust, data security, value added, user interface design and FinTech promotion. The empirical results definitely confirm that data security, customer trust and the user design interface affect the adoption of FinTech. Existing studies have used the Technology Acceptance Model (TAM) to address this issue. The outcomes of this study can be used to improve the performance of FinTech strategies and enable banks to achieve economies of scale for global intensity.Design/methodology/approachIn this paper, the authors empirically consider factors that influence the expectations of both users and organizations in adopting FinTech, such as customer trust, data security, value added, the user design interface and FinTech promotion. The results confirm that customer trust, data security and the user design interface affect the adoption of FinTech. This research proposes a model called “Intention to adopt FinTech in Germany,” constructs of which were developed based on the TAM and five additional components, as identified. The outcomes of this study can be used to improve the performance of FinTech strategies and enable banks to achieve economies of scale for global intensity.FindingsThe authors demonstrated that the number of mobile users in Germany is rapidly increasing; yet the adoption of FinTech is extremely sluggish. It is intriguing to reckon that 99 per cent of respondents had mobile devices, but only 10 per cent recognized FinTech. Further, it is significantly discouraging to perceive that only 10 of the 209 respondents had ever used FinTech services, representing under 1 per cent of the surveyed respondents. It is obvious that the FinTech incubators and banks offering FinTech services need to persuade their customers regarding the usefulness and value added advantages of FinTech. This study has been carried out to determine the key factors that influence and provoke FinTech adoption.Research limitations/implicationsThere are a few limitations in this study. Initially, this study focuses on FinTech implementation in Germany and not the whole of Europe. In addition, demographic and regional factors could be consolidated to inspect their particular impact on the intention to use FinTech services, particularly among younger users with a high interest in technology. Without these constraints, the authors could have gathered additional data for a more robust result and obtained new knowledge to further upgrade polices to enhance the FinTech adoption process. Future analysts can assist exploration of this topic by altering determinants in the unified theory of acceptance and use of technology model. Additionally, because the cluster sampling technique was used, the reported outcomes are not 100 per cent generalized to the German population. To accomplish a complete generalization, a basic random sampling strategy for the whole population is essential. The authors could also alleviate some limitations by examining how online vendors are performing with regard to FinTech to satisfy the needs of customers via case studies.Practical implicationsThis study was conducted in Germany and might have produced different results if held in other countries, as technology acceptance is different in a different environment. For instance, the authors suspect that the results would be somewhat different, were the research to be conducted in the United Kingdom, where take-up of FinTech appears to be far greater than in Germany. Therefore, the authors’ results are only generalized for the country of Germany and not other geographical areas. Furthermore, respondents may have been influenced by past experiences about FinTech usage which might have led them to neglect to answer some questions. In spite of this, this study did not consider the influence of moderating variables such as age, education and FinTech services experience. The authors also neglected social impact and control factors, as their corresponding items disregarded the instrument dependability. Accordingly, the authors could not quantify social impact and control factors on FinTech use.Social implicationsThe outcomes of this study can be used to improve the performance of FinTech strategies and enable banks to accomplish economies of scale for global intensity. The authors do hope that this paper will serve to encourage FinTech innovators in their approach to FinTech and enable FinTech researchers to use past work with more prominent certainty, resulting in rigid hypothesis improvement in the future.Originality/valueA considerable amount of revenue has been invested in the information technology (IT) infrastructure of banks to enhance their performance, but investment in IT remains a substantial risk regarding the return on investment (Carlson, 2015). Most banks and financial organizations around the globe are engaging in an extreme pressure from their customers and competitors to enhance IT.

Data is nonrival: a person’s location history, medical records, and driving data can be used by many firms simultaneously. Nonrivalry leads to increasing returns. As a result, there may be social gains to data being used broadly across firms, even in the presence of privacy considerations. Fearing creative destruction, firms may choose to hoard their data, leading to the inefficient use of nonrival data. Giving data property rights to consumers can generate allocations that are close to optimal. Consumers balance their concerns for privacy against the economic gains that come from selling data broadly.

Many two-sided platforms, such as eBay, iOS, Android, and Twitter, invest in developer integration tools, such as modular interfaces, interactive development environments, application programming interfaces, and help desks, in order to reduce the cost and improve the functionality of third-party content developed for the platform. Although these integration tools are crucial to platform success, they are costly to create, and therefore, managers need to understand where and when to deploy them. In particular, when the necessary integration investment is high, the advice to subsidize one side of a two-sided market while charging the other may not hold. This means that integration investment should be carefully coordinated with market pricing decisions. In general, higher levels of investment by hardware/software platforms into integration become desirable when the platform (1) has access to a large pool of content providers and consumers, (2) is able to develop integration tools that are highly effective in reducing third-party development costs, and (3) operates in high-consumer value markets. However, there are nuances. For example, business to business platforms can make investments in integration to facilitate participation by both sides of the market. We find that such investments are complements, not—as one might expect—substitutes. Many two-sided platforms (for example, eBay, Google, iOS, Android, Twitter, and Amazon) provide integration tools, such as modular interfaces, interactive development environments, application programming interfaces, and help desks, to reduce the costs and improve the functionality of third-party content developed for the platform. The need for such investment is increasing with the rise of major new markets as the result of technologies, such as the “Internet of Things.” Although crucial to platform success, platform integration tools are costly to create. We develop an analytic model to explore the key tradeoffs behind investment in integration tools and how that investment interacts with pricing decisions in a two-sided market. We model these decisions for hardware/software platforms as well as hybrid retail platforms and analyze them under various scenarios, including monopoly and competition. Our results suggest that considering integration investment can create market regimes in which the standard pricing results from the extant platform literature no longer hold. For example, the tendency to reduce prices to one side of a market in response to increasing the benefit of the network to the other side may be suboptimal in the presence of integration investment. Therefore, integration investments must be well coordinated with pricing decisions made for both sides of the market. In general, higher levels of investment by hardware/software platforms into integration become desirable when the platform (1) has access to a large pool of content providers and consumers, (2) is able to develop integration tools that are highly effective in reducing third-party development costs, and (3) operates in a market in which content providers earn a high-enough profit margin creating content that is highly valued by the consumer market. Hybrid retail platforms often show similar behavior. However, there are some nuances. For example, business to business platforms can make investments in integration to facilitate participation by both sides of the market. We find that these investments are complements, not—as one might expect—substitutes. We conclude by discussing this work’s implications for theory and practice.

In this paper, I study the effect of adding large numbers of producers of application software programs (“apps”) to leading handheld computer platforms, from 1999 to 2004. To isolate causal effects, I exploit changes in the software labor market. Consistent with past theory, I find a tight link between the number of producers on platform and the number of software varieties that were generated. The patterns indicate the link is closely related to the diversity and distinct specializations of producers. Also highlighting the role of heterogeneity and nonrandom entry and sorting, later cohorts generated less compelling software than earlier cohorts. Adding producers to a platform also shaped investment incentives in ways that were consistent with a tension between network effects and competitive crowding, alternately increasing or decreasing innovation incentives depending on whether apps were differentiated or close substitutes. The crowding of similar apps dominated in this case; the average effect of adding producers on innovation incentives was negative. Overall, adding large numbers of producers led innovation to become more dependent on population-level diversity, variation, and experimentation—while drawing less on the heroic efforts of any one individual innovator.