Carrying your long tail: Delighting your consumers and managing your operations

The growing ability to sell a wider range of goods, in smaller quantities, while still making a profit, is now widely called a long tail strategy. Profiting from greater product diversity represents a real change in optimal business strategy, which is based on real changes in customer behavior. Many firms want to develop long tail strategies, avoiding competition in mass market fat spots, and harvesting the superior margins available through selling in market sweet spots. Sweet spot offerings resonate with customers, allowing customers to find what they truly want and to avoid compromises; consequently, customers pay more while remaining happier with their purchases, and firms earn more and are more profitable. Evidence from earlier recessions suggests that in an era of excess capacity and pressures on consumers to find the best possible prices, competing through resonance offerings may represent an important source of protected profits. And yet, carrying a long tail and selling into sweet spots requires new skills, both for locating targets of opportunities and for controlling costs.     

The long tail or the short tail: The category-specific impact of eWOM on sales distribution

This paper investigates the impact of electronic word of mouth (eWOM) on sales distribution and challenges the conventional wisdom of the long tail theory. As customers refer to eWOM to evaluate products, and each product type entails a different scheme of evaluation standards, the impact of eWOM may differ by product type. Thus, we propose a new type of product categorization based on evaluation standard objectivity and hypothesize that this categorization gradually differentiates sales distribution patterns, some of which refute the long tail phenomenon. To validate the hypothesis, we collect data from Amazon.com, compare the distribution of eWOM among various product types, and conduct the Wilcoxon signed rank test for statistical significance. All the test results show adequate levels of significance; thus, the three hypotheses are supported. This study sheds new light on eWOM research by developing a new approach to product categorization and by proposing a different use of eWOM in searching for products to explain the different effects of eWOM on sales distribution.     

Success of electronic commerce Web sites: A comparative study in two countries

This study incorporated attitude toward a site (Ast) in the proposed model together with moderating factors like cultural effects and attitude toward online shopping (Ashop). The proposed model and hypotheses were partially supported in Taiwan's and Thailand's data. Information quality affected both user satisfaction and attitude toward the web site and was the dimension common across both countries. Ashop moderated the effects of system quality on user satisfaction in Taiwan, but moderated information quality in the Thailand. National identity moderated the effect of system quality on both user satisfaction and attitude toward the site in the Thai data.     

基于最小二乘与Taylor级数展开的新型混合定位方法

针对广播式自动相关监视系统中的虚假目标识别问题,采用一种到达时间差与到达时间和2种定位方法相结合的新型混合定位方法,获取目标真实空间位置,将其与自动相关监视报文中的位置数据进行比对来判断虚假目标。为使混合定位更加精确与快速,利用最小二乘法对目标初始定位,再将其作为Taylor级数展开迭代的初值,精确估计目标位置。模拟仿真结果表明,混合定位方法解决了Taylor级数展开算法中结果可能不收敛的问题,且在相同地面站布局条件下,其精度和稳定性较传统到达时间差定位方法更高。     

一种抑制非视距传播误差的混合定位算法

为减小蜂窝网定位中影响定位精度的非视距(NLOS)传播误差,提出一种基于两步卡尔曼滤波到达时间差/到达角度的混合定位算法.利用卡尔曼滤波器的估计值计算非视距数据的方差,调节卡尔曼滤波器的参数,减小测量值的NLOS误差,并将经过预处理的测量值输入到扩展卡尔曼滤波器,实现混合定位.实验结果表明,该算法能有效消除NLOS误差,与Chan算法相比,其定位精度更高.     

A method for product form design of integrating interactive genetic algorithm with the interval hesitation time and user satisfaction

Due to the continuous release of new products, manufacturers are paying attention to customer-oriented design of products that meet user needs to minimize the risk of their products being rejected by the market. Due to the ambiguity of user cognition, it is difficult to accurately obtain the user's preference for individual productions. To respond to the challenge, we propose an engineering scientific research method of interactive genetic algorithm with the interval arithmetic based on hesitation and fuzzy kano model(FKM) to explore the emotional needs of users for product forms and drive product modeling evolution design. Through expert interviews, the morphological characteristics and perceptual images factors of the products attracting users are investigated. In order to identify the user's satisfaction relationship with the perceptual images, we use FKM to analyze the product image style that meets the user's kansei needs accurately and selects 5 factors which is attractive attributes. Meanwhile, we attempt to transform this 5 factors into evaluation carrier to guide the evolution direction of product styling in HIIF-IGA, and then optimized four electric bikes with scores over 8.8 so that it could realize user demand-driven product evolution design. To handle users' ambiguity, the FAHP method is used to quantify the user's emotional imagery criterion and create a product evolution design system platform, which can automatically generate product styling design scheme in line with user preferences. This experimental results show that the proposed method can help enterprises effectively improve customer satisfaction and reduce the cost and time of product development.     

On sovereign credit migration: A study of alternative estimators and rating dynamics

Different estimators of rating transition matrices have been proposed in the literature but their behaviour has been studied mainly in the context of corporate ratings. The finite-sample bias and variability of three sovereign credit migration estimators is investigated through bootstrap simulations. These are a discrete multinomial estimator and two continuous-time hazard rate methods, one of which neglects time heterogeneity in the rating process whereas the other accounts for it. Panel logit models and spectral analysis are utilized to study the properties of the rating process. The sample consists of Moody's ratings 1981-2004 for 72 industrialized and emerging economies. Hazard rate estimators yield more accurate default probabilities. The time homogeneity assumption leads to underestimating the default probability and greater migration risk is inferred upon relaxing it. There is evidence of duration dependence and downgrade momentum effects in the rating process. These findings have important implications for economic and regulatory capital allocation and for the pricing of credit sensitive instruments.     

A Cognitive-Affective Model of Perceived User Satisfaction (CAMPUS): The complementary effects and interdependence of usability and aesthetics in IS design

Affective dimensions of human–computer interaction design have the potential to elicit emotions and behaviours. However, there is little research into which affective treatments are systematically tested, let alone assessed in light of additional cognitive dimensions. In this study, we formulate and empirically test a Cognitive-Affective Model of Perceived User Satisfaction (CAMPUS) that displays high explanatory power (R² = .69). CAMPUS offers a comprehensive framework for assessing both direct effects of perceptions of cognitive and affective dimensions on satisfaction and the complex interplay between these two in terms of system design and use. Implications for theory and practice are discussed.     

Improved interval estimation of long run response from a dynamic linear model: A highest density region approach

This paper proposes a new method of interval estimation for the long run response (or elasticity) parameter from a general linear dynamic model. We employ the bias-corrected bootstrap, in which small sample biases associated with the parameter estimators are adjusted in two stages of the bootstrap. As a means of bias-correction, we use alternative analytic and bootstrap methods. To take atypical properties of the long run elasticity estimator into account, the highest density region (HDR) method is adopted for the construction of confidence intervals. From an extensive Monte Carlo experiment, we found that the HDR confidence interval based on indirect analytic bias-correction performs better than other alternatives, providing tighter intervals with excellent coverage properties. Two case studies (demand for oil and demand for beef) illustrate the results of the Monte Carlo experiment with respect to the superior performance of the confidence interval based on indirect analytic bias-correction.     

Measurements of team workload: A time pressure and scenario complexity study for maritime operation tasks

Workload is a critical factor influencing team performance in complex systems. There remains no consensus on the selection of team workload measures. Through an experiment based on simulated maritime operation tasks, team workload was manipulated by time pressure and scenario complexity to investigate the responses of three categories (subjective, performance-based, and physiological) of potential team workload measurements. The results show that time pressure had significant effects on all the three categories of measurements, especially on subjective indicators, miss rate, completion rate, operation accuracy, and eye movements. Significant main effects of scenario complexity on subjective team workload and the selected team performance measurements were also found, such as response latent time, miss rate, completion rate, and operation accuracy. Physiological measurements may not be sensitive for measuring team workload when scenario complexity serves as a major influencing factor of team workload.     

Ruggedized handheld device input effectiveness by generation: A time and error study

The objective of this study is to determine whether ruggedized handheld scanning devices used for industrial purposes should contain one of the most prominent features provided on commercial smart devices: data entry via touchscreen as opposed to a physical keypad. Due to harsh environments, physical keys have been the preferred means of input for rugged handhelds. Advancement in touchscreen technology along with technology expectations brought about by the workforce demographic shift are influencing a notable shift to touch-only input for rugged equipment. Hypotheses expect there to be a difference in usability by worker generation and so 20 Gamers (Millennials) and 20 Baby Boomers performed manual data entry on ruggedized handhelds: one with physical keys and one touchscreen only. All participants took 19% less time on touchscreen than physical keys. Gamers were 31% faster than Boomers on physical keyed devices and 28% faster on touchscreen only. There was no significant difference in errors entered for either device by either age group; however, an 83% increase in permanent errors by Gamers on touchscreen was noted. Transitioning to a rugged device with touch-only input is recommended for industry as it could offer an increase in work productivity. This study presents timely insight into a new tool option for industrial workers.Relevance to industryThis research describes the paradigm shift in the ruggedized handheld device market from physical keys to touchscreen only input and identifies real time productivity savings and error risks that can be expected by different generations of workers in the industrial workforce.     

基于肤色与特征的快速人脸定位方法研究

根据在线考试安全监控的特点,针对视频流图像提出一种基于肤色模型和特征定位的快速入脸定位算法,并建立了系统原型,适用于实际网络在线考试中的身份认证。该方法以普通PC摄像头作为图像采集设备,以采集的视频流为数据源,截取视频流中的单帧图像,通过转换彩色空间、入脸肤色建模、后处理操作实现了入脸定位,通过图像马赛克、边缘提取,显著特征点定位等技术定位入脸图像的各特征点。在此基础上通过入眼定位实现了在视频流中对于人脸的跟踪算法研究。试验结果表明,所实现的入脸检测适用于近距离入脸的检测,定位速度快,误检率低,可以在实时系统中应用。     

Dynamic Location and Entry Mode Selection of Multinational Manufacturing Facilities under Uncertainty: A Chance-constrained Goal Programming Approach

As the global marketplace continues to expand, the firm's location strategy should shift from domestic to international. An international location problem is different from the traditional domestic location problem in that the former is influenced by a greater variety of uncontrollable, unpredictable, and dynamic factors than is the latter. These factors may include political conditions, expropriation risks, trade regulations, currency exchange rates, cultural differences, and global distribution channel structures. Consequently, the international location problem is more diverse, volatile and complex. Nevertheless, a vast majority of the location literature overlooked many important international aspects. To help the multinational firm formulate viable location strategies in the changing world marketplace, this paper proposes multiple-period, multiple-plant, multiple-objective, and stochastic location model.     

Distributed simulation of modular time Petri nets: An approach and a case study exploiting temporal uncertainty

This paper proposes an approach to modular modelling and simulation of complex time-critical systems. The modelling language is represented by Merlin and Farber’s Time Petri Nets (TPNs) augmented with inhibitor arcs and modular constructs borrowed from the Petri Net Markup Language (PNML) interchange format. Analysis techniques depend on Temporal Uncertainty Time Warp (TUTW), a time warp algorithm capable of exploiting temporal uncertainty in general optimistic simulations over a networked context. A key feature of the approach is the fact that TPN models naturally exhibit a certain degree of temporal uncertainty which the TUTW control engine can exploit to achieve good speedup without a loss in the accuracy of the simulation results. The developed TUTW/TPN kernel is demonstrated by modelling and simulation of a real-time system example.A preliminary version of this paper was presented at 38th SCS Annual Simulation Symposium, April 4–6, 2005, San Diego (CA), IEEE Computer Society, pp. 233–240. Franco Cicirelli achieved a PhD in computer science from the University of Calabria (Unical), DEIS—department of electronics informatics and systems science. As a postdoc, he is making research on agent and service paradigms for the development of distributed systems, parallel simulation, Petri nets, distributed measurement systems. He holds a membership with ACM. Angelo Furfaro, PhD, is a computer science assistant professor at Unical, DEIS, teaching object-oriented programming. His research interests are centred on: multi-agent systems, modeling and analysis of time-dependent systems, Petri nets, parallel simulation, verification of real-time systems, distributed measurement systems. He is a member of ACM. Libero Nigro is a full professor of computer science at Unical, DEIS, where he teaches object-oriented programming, software engineering and real-time systems courses. He directs the Software Engineering Laboratory (www.lis.deis.unical.it). His current research interests include: software engineering of time-dependent and distributed systems, real-time systems, Petri nets, modeling and parallel simulation of complex systems, distributed measurement systems. Prof. Nigro is a member of ACM and IEEE.     

Judging online peer-to-peer lending behavior: A comparison of first-time and repeated borrowing requests

The past decade has witnessed a growing number of business models that facilitate economic exchanges between individuals with limited institutional mediation. One of the important innovative business models is online peer-to-peer lending, which has received wide attention from government, industry, and researchers. Using the signaling theory, we compare the effects of various signals on the likelihood of successful funding in three models (i.e., first-time borrowing, repeated borrowing without prior lending, and repeated borrowing with prior lending). Using data collected from PPDAI.com, we verify the three proposed models by employing logistic regression. Results and implications are analyzed and discussed.     

Requirements analysis for customization using subgroup differences and large sample user testing: A case study of information retrieval on handheld devices in healthcare

Requirements analysis defines the goals and evaluation criteria of system design. We introduce a methodology for requirements analysis for customization based on large sample interactive testing, with the premise that analysis of user behaviour with prototypes leads to requirements discoveries. The methodology uses a relatively large sample (1) to identify relevant user subgroups, (2) to observe significant empirically determined group differences in the context of task and tool use and (3) to estimate the groups’ different requirements and derive design implications. Between 20 and 50 participants are used per test, rather than the three to five often recommended for user testing. Statistical relationships are investigated between subgroups in terms of background variables, questionnaire items, performance data, and coded verbal statements. Customization requirements are inferred from the significant differences observed between empirically determined groups. The methodological framework is illustrated in a case study involving the use of clinical resources on handheld devices by three groups of physicians. The groups were found to have different needs and preferences for evidence-based resources and device form factor, implying opportunities and necessities for group customization requirements.

Relevance to industry

In safety-critical domains such as health care, it is essential to assess user needs and preferences regarding devices and systems to inform appropriate customizations. We present a methodological framework and case study that demonstrates how large sample user testing can supplement typical methods of requirements analysis to provide contextualized, quantitative accounts of group differences and customization requirements.     

Investigating Architectural Technical Debt accumulation and refactoring over time: A multiple-case study

ContextA known problem in large software companies is to balance the prioritization of short-term with long-term feature delivery speed. Specifically, Architecture Technical Debt is regarded as sub-optimal architectural solutions taken to deliver fast that might hinder future feature development, which, in turn, would hinder agility.ObjectiveThis paper aims at improving software management by shedding light on the current factors responsible for the accumulation of Architectural Technical Debt and to understand how it evolves over time.MethodWe conducted an exploratory multiple-case embedded case study in 7 sites at 5 large companies. We evaluated the results with additional cross-company interviews and an in-depth, company-specific case study in which we initially evaluate factors and models.ResultsWe compiled a taxonomy of the factors and their influence in the accumulation of Architectural Technical Debt, and we provide two qualitative models of how the debt is accumulated and refactored over time in the studied companies. We also list a set of exploratory propositions on possible refactoring strategies that can be useful as insights for practitioners and as hypotheses for further research.ConclusionSeveral factors cause constant and unavoidable accumulation of Architecture Technical Debt, which leads to development crises. Refactorings are often overlooked in prioritization and they are often triggered by development crises, in a reactive fashion. Some of the factors are manageable, while others are external to the companies. ATD needs to be made visible, in order to postpone the crises according to the strategic goals of the companies. There is a need for practices and automated tools to proactively manage ATD.