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Journal articles on the topic 'Software Pricing'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Mehta, Sanjay S., and Gurinderjit B. Mehta. "Software Development Project Pricing:." Journal of Customer Service in Marketing & Management 1, no. 3 (1995): 119–33. http://dx.doi.org/10.1300/j127v01n03_10.

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2

Lehmann, Sonja, and Peter Buxmann. "Pricing Strategies of Software Vendors." Business & Information Systems Engineering 1, no. 6 (2009): 452–62. http://dx.doi.org/10.1007/s12599-009-0075-y.

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3

Kephart, Jeffrey O., James E. Hanson, and Amy R. Greenwald. "Dynamic pricing by software agents." Computer Networks 32, no. 6 (2000): 731–52. http://dx.doi.org/10.1016/s1389-1286(00)00026-8.

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4

Xin, Mingdi, and Arun Sundararajan. "Nonlinear Pricing of Software with Local Demand Inelasticity." Information Systems Research 31, no. 4 (2020): 1224–39. http://dx.doi.org/10.1287/isre.2020.0940.

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Nonlinear usage-based pricing is applied extensively in software markets. Customers of software products usually cannot vary their required usage volume, a property we label local demand inelasticity. For instance, a client firm that needs a sales force automation software either buys one user license for every salesperson or does not buy at all. It is unlikely to buy licenses for some but not all salespersons. This demand feature violates a critical assumption of the standard nonlinear pricing literature that consumers are flexible with their usage volume, and their valuation changes smoothly

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5

Cusumano, Michael A. "The changing labyrinth of software pricing." Communications of the ACM 50, no. 7 (2007): 19–22. http://dx.doi.org/10.1145/1272516.1272531.

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6

Sun, Hong, Qian Wei Tu, Xiao Wan Wang, Jian Hong Zhang, Qian Zhong Wu, and Shou Wen Qin. "The Pricing and Charging of Cloud Computing SaaS." Advanced Materials Research 798-799 (September 2013): 703–7. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.703.

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In this paper, different mathematics models are applied to research the reasonable pricing of the cloud computing software products on the SaaS platform and explore the sales price of the cloud computing software products with the maximum total profits. These information can be the reference for decision-makers to determine the pricing strategy. The aim of this paper is for the cloud computing company to provide the majority of users with the inexpensive and excellent softwares and services, which can make the company dominate the cloud computing market quickly, gain the huge profits in the su

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7

Aggarwal, Ajay K., and Dinesh S. Dave. "Decision maker friendly approach for pricing software." International Journal of Computer Applications in Technology 36, no. 1 (2009): 60. http://dx.doi.org/10.1504/ijcat.2009.026671.

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8

Benaroch, Michel, and Ajit Appari. "Financial Pricing of Software Development Risk Factors." IEEE Software 27, no. 5 (2010): 65–73. http://dx.doi.org/10.1109/ms.2010.28.

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9

Tziasiou, Georgia T., George A. Orfanos, Pavlos S. Georgilakis, and Nikos D. Hatziargyriou. "Transmission pricing software for power engineering education." Computer Applications in Engineering Education 22, no. 3 (2011): 410–28. http://dx.doi.org/10.1002/cae.20565.

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10

Gatheral, J., Y. Epelbaum, Jining Han, et al. "Implementing option-pricing models using software synthesis." Computing in Science & Engineering 1, no. 6 (1999): 54–64. http://dx.doi.org/10.1109/5992.805136.

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11

Dey, Debabrata, Abhijeet Ghoshal, and Atanu Lahiri. "Support Forums and Software Vendor’s Pricing Strategy." Information Systems Research 32, no. 2 (2021): 653–69. http://dx.doi.org/10.1287/isre.2020.0988.

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12

Ceran, Yasin, Milind Dawande, Dengpan Liu, and Vijay Mookerjee. "Optimal Software Reuse in Incremental Software Development: A Transfer Pricing Approach." Management Science 60, no. 3 (2014): 541–59. http://dx.doi.org/10.1287/mnsc.2013.1757.

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13

Gurnani, H., and K. Karlapalem. "Optimal pricing strategies for Internet-based software dissemination." Journal of the Operational Research Society 52, no. 1 (2001): 64–70. http://dx.doi.org/10.1057/palgrave.jors.2601046.

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14

Bala, Ram, and Scott Carr. "Usage-based pricing of software services under competition." Journal of Revenue and Pricing Management 9, no. 3 (2010): 204–16. http://dx.doi.org/10.1057/rpm.2010.12.

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15

August, Terrence, Duy Dao, and Kihoon Kim. "Market Segmentation and Software Security: Pricing Patching Rights." Management Science 65, no. 10 (2019): 4575–97. http://dx.doi.org/10.1287/mnsc.2018.3153.

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The patching approach to security in the software industry has been less effective than desired. One critical issue with the status quo is that the endowment of “patching rights” (the ability for a user to choose whether security updates are applied) lacks the incentive structure to induce better security-related decisions. However, producers can differentiate their products based on the provision of patching rights. By characterizing the price for these rights, the optimal discount provided to those who relinquish rights and have their systems automatically updated in a timely manner, and the

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16

Zhang, Zan. "Competitive Pricing Strategies for Software and SaaS Products." Information & Management 57, no. 8 (2020): 103367. http://dx.doi.org/10.1016/j.im.2020.103367.

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17

仲, 琴. "Pricing Strategy for Perpetual License Based Software Product." Modern Management 06, no. 05 (2016): 170–78. http://dx.doi.org/10.12677/mm.2016.65022.

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18

Dakin, K. "The transaction-based pricing trend [for software packages]." IEEE Software 13, no. 3 (1996): 96–97. http://dx.doi.org/10.1109/52.493027.

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19

Luo, Suizhi, Witold Pedrycz, and Lining Xing. "Pricing of satellite image data products: Neutrosophic fuzzy pricing approaches under different game scenarios." Applied Soft Computing 102 (April 2021): 107106. http://dx.doi.org/10.1016/j.asoc.2021.107106.

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20

MIHRIBAN, KALKANCI, and ÖZER İHSAN. "Developing a software calculating fabric consumption of various bathrobe models." Industria Textila 69, no. 05 (2018): 406–11. http://dx.doi.org/10.35530/it.069.05.1550.

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Considering the customer requests and speed in the textile sector, very fast and accurate pricing should be done when the customers ask for very urgent prices for different styles. Customers often ask for sample pricing from apparel manufacturers. In this case, the garment manufacturer has to move quickly and accurately in determining the unit consumption of the garment. It is very important to know the correct fabric cost in pricing the garment to be produced. In general, the cost of fabric in garments accounts for 60–70% of the total cost. Manufacturers take risks when pricing the garment wi

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21

Alekseev, Alexander, and Valeriya Gladkih. "SOFTWARE FOR PRICE FORMATION AND ESTIMATE RATING IN CONSTRUCTION." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2021, no. 1 (2021): 49–60. http://dx.doi.org/10.24143/2072-9502-2021-1-49-60.

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The article gives a review of modern pricing and cost estimating processes in construction and considers the existing software products aimed at automating the processes. According to the results of comparative analysis, none of the software products reviewed support the function of editing the prices currently in use. Editing may be rendered possible if a detailed list of construction operations is available. The list may be adjusted on the fly as cutting-edge building technologies are employed and at the same time taking into account operation scenarios in space-limited environment. The impo

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22

Ji, Shou Wen, Qian Feng, Yu Zheng Wang, and Xue Song Wang. "The Research on Pricing Customizing Services of Logistics Information Platform." Advanced Materials Research 774-776 (September 2013): 2004–7. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.2004.

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In recent years, the rapid development of logistics has become the driving force of the construction of Logistics Information Platforms, nevertheless, most of which are still underdeveloped, especially the charging and pricing mechanism of the platform is inadequate. Based on the bargain method of the game theory, this essay will build a pricing model and conduct a three-round bargaining reverse regression calculation towards logistics software customized services, resulting in its optimal pricing strategy. Taking National Transport Logistics Public Information Platform as the analyzing exampl

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23

Katsamakas, Evangelos. "Effects of Quality Improvement and Upgrading on Software Market Disruption." International Journal of Strategic Decision Sciences 9, no. 4 (2018): 1–15. http://dx.doi.org/10.4018/ijsds.2018100101.

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Digital goods, such as software, are significant elements of the contemporary digital economy. The authors propose a model that characterizes dynamic profit-maximizing competitive pricing strategies of digital goods with network effects. In a two-period game theory model, an incumbent firm has a quality advantage in period 1, but the potential disrupter has a quality advantage in period 2. They analyze pricing strategies and characterize conditions under which the potential disrupter becomes an actual disrupter. They discuss implications for user adoption of digital goods and opportunities for

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24

Jia, Kunhao, Xiuwu Liao, and Juan Feng. "Selling or leasing? Dynamic pricing of software with upgrades." European Journal of Operational Research 266, no. 3 (2018): 1044–61. http://dx.doi.org/10.1016/j.ejor.2017.10.063.

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25

Kola, Kiran, Ruppa K. Thulasiram, and Parimala Thulasiraman. "A software architecture framework for on-line option pricing." Journal of Supercomputing 47, no. 2 (2008): 146–70. http://dx.doi.org/10.1007/s11227-008-0182-4.

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26

Shen, YuSong, Ye Yang, Yong Wang, and DeLin Chang. "Software crowdsourcing task pricing based on topic model analysis." IET Software 14, no. 7 (2020): 759–67. http://dx.doi.org/10.1049/iet-sen.2019.0168.

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27

Spruit, Marco, and Nizar Abdat. "The Pricing Strategy Guideline Framework for SaaS Vendors." International Journal of Strategic Information Technology and Applications 3, no. 1 (2012): 38–53. http://dx.doi.org/10.4018/jsita.2012010103.

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Software as a Service has become a dominant IT news topic over the last few years. Especially in these current recession times, adopting SaaS solutions is increasingly becoming the more favourable alternative for customers rather than investing on brand new on-premise software or outsourcing. This fact has inevitably stimulated the birth of numerous SaaS vendors. Unfortunately, many small-to-medium vendors have emerged only to disappear again from the market. A lack of maturity in their pricing strategy often becomes part of the reason. This paper presents the ‘Pricing Strategy Guideline Frame

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28

Aggarwal, Ajay K., and Dinesh S. Dave. "Statistical software pricing analysis through artificial intelligence and statistical methods." International Journal of Information Technology and Management 9, no. 2 (2010): 132. http://dx.doi.org/10.1504/ijitm.2010.030457.

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29

Kim, Sangphil, Kook-Hyun Choi, Jin-Young Park, and Jong-Bae Kim. "A Study of Pricing Decision for Open Source Software Value." International Journal of u- and e- Service, Science and Technology 10, no. 6 (2017): 15–24. http://dx.doi.org/10.14257/ijunesst.2017.10.6.02.

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30

Shenker, S., D. Clark, D. Estrin, and S. Herzog. "Pricing in computer networks." ACM SIGCOMM Computer Communication Review 26, no. 2 (1996): 19–43. http://dx.doi.org/10.1145/231699.231703.

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31

Dolgui, Alexandre, and Jean-Marie Proth. "Pricing strategies and models." Annual Reviews in Control 34, no. 1 (2010): 101–10. http://dx.doi.org/10.1016/j.arcontrol.2010.02.005.

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32

August, Terrence, and Marius Florin Niculescu. "The Influence of Software Process Maturity and Customer Error Reporting on Software Release and Pricing." Management Science 59, no. 12 (2013): 2702–26. http://dx.doi.org/10.1287/mnsc.2013.1728.

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33

Rohitratana, Juthasit, and Jörn Altmann. "Impact of pricing schemes on a market for Software-as-a-Service and perpetual software." Future Generation Computer Systems 28, no. 8 (2012): 1328–39. http://dx.doi.org/10.1016/j.future.2012.03.019.

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34

Ifrach, Bar, and Rameh Johari. "Pricing a bestseller." ACM SIGMETRICS Performance Evaluation Review 41, no. 4 (2014): 51. http://dx.doi.org/10.1145/2627534.2627551.

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35

Huang, Jianwei, and Lin Gao. "Wireless Network Pricing." Synthesis Lectures on Communication Networks 6, no. 2 (2013): 1–176. http://dx.doi.org/10.2200/s00505ed1v01y201305cnt013.

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36

Temple, William G., and Richard T. B. Ma. "Monotonic marginal pricing." ACM SIGMETRICS Performance Evaluation Review 41, no. 3 (2014): 65–70. http://dx.doi.org/10.1145/2567529.2567551.

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37

Borgs, Christian, Jennifer T. Chayes, Sherwin Doroudi, Mor Harchol-Balter, and Kuang Xu. "Pricing and queueing." ACM SIGMETRICS Performance Evaluation Review 40, no. 3 (2012): 71–73. http://dx.doi.org/10.1145/2425248.2425266.

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38

McLean, Richard P., and William W. Sharkey. "Probabilistic value pricing." Mathematical Methods of Operations Research 43, no. 1 (1996): 73–95. http://dx.doi.org/10.1007/bf01303435.

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39

Cheng, Kuei-Yuan, Yao-Hsien Lee, and Mei-Yu Lee. "Price competition between shrink-wrap software and cloud service firms under a stochastic model." Problems and Perspectives in Management 14, no. 2 (2016): 272–76. http://dx.doi.org/10.21511/ppm.14(2-2).2016.02.

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The authors establish a stochastic model of the price competition between shrink-wrap software and cloud service firms. They show that cloud service firms earn higher profits, but face higher risks compared to shrink-wrap software firms. In order to attract customers and earn higher profits, the authors obtain the result that shrink-wrap software firms need to focus on pricing strategies, by contrast, cloud service firms need to focus on quantity strategies

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40

Kozlova, Marina, Dmitriy Kozhemyakin, Olga Sergacheva, and Alexandr Bortenev. "The influence of digital platforms and algorithms on legal regulation of competition." SHS Web of Conferences 109 (2021): 01020. http://dx.doi.org/10.1051/shsconf/202110901020.

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The antitrust regulation faces challenges in the context of digitalization and algorithmization; several of them are analyzed in the article. The authors explore the influence of digital platforms and pricing algorithms on competitive environment, the practice of their application in view of monopolizing the market and the possibilities to resist such practice from the point of view of the antitrust legislation. They examine the approaches to regulating digital instruments of pricing in order to ensure fair competition. One of the approaches is based on the fact that there is no need to develo

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41

Lowe, Devesh, and Bhavna Galhotra. "An Overview of Pricing Models for Using Cloud Services with analysis on Pay-Per-Use Model." International Journal of Engineering & Technology 7, no. 3.12 (2018): 248. http://dx.doi.org/10.14419/ijet.v7i3.12.16035.

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Emergence of Cloud computing in recent years has provided various options to end-users w.r.t. cloud services. Different end users have different requirements for cloud services such as IAAS, PAAS & SAAS, but these services can be availed using different pricing mechanisms such as PPU, PFR, leased based, subscription based and dynamic pricing based on factors such as initial cost, lease period, QoS, Age of resources and cost of maintenance. The authors work focusses on ‘pay-per-use’ model of cloud pricing by studying various aspects of this model and comparing the current pricing rates of l

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42

Pandey, Neeraj, Nikhil Mehta, and Shreya Basu Roy. "Semiconductor Pricing Strategy in USB Market: A Market Leader’s Dilemma." Business Perspectives and Research 5, no. 1 (2016): 1–10. http://dx.doi.org/10.1177/2278533716671614.

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The semiconductor market has become more competitive than ever before with new players joining the industry. There is pressure for innovation and differentiation in this industry to maintain leadership. The resultant innovative products have wide application but are sold in hypercompetitive market. The industry requires price management at transaction level to achieve efficiency and excellence with each of the diverse customers. The pricing in the semiconductor industry is done more scientifically as compared to other industries like FMCG, consumer durables, and health care. Pricing software a

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43

Denne, Mark. "Pricing Utility Computing Services." International Journal of Web Services Research 4, no. 2 (2007): 114–27. http://dx.doi.org/10.4018/jwsr.2007040105.

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44

Kim, Sangphil, Chang-Hong Kim, and Jong-Bae Kim. "A Study of Open Source Software Pricing Decision for Value Valuation." International Journal of Software Engineering and Its Applications 10, no. 7 (2016): 27–38. http://dx.doi.org/10.14257/ijseia.2016.10.7.04.

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45

Dave, Dinesh S., Ajay K. Aggarwal, and Chandrashekar D. Challa. "Pricing analysis of decision-making software: modelling and Artificial Intelligence approaches." International Journal of Computer Applications in Technology 26, no. 4 (2006): 175. http://dx.doi.org/10.1504/ijcat.2006.010762.

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46

Appari, Ajit, and Michel Benaroch. "Monetary pricing of software development risks: A method and empirical illustration." Journal of Systems and Software 83, no. 11 (2010): 2098–107. http://dx.doi.org/10.1016/j.jss.2010.06.012.

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47

LARKIN, IAN. "BARGAINS-THEN-RIPOFFS: INNOVATION, PRICING AND LOCK-IN IN ENTERPRISE SOFTWARE." Academy of Management Proceedings 2008, no. 1 (2008): 1–6. http://dx.doi.org/10.5465/ambpp.2008.33624246.

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48

Rahmandad, Hazhir, and Soheil Sibdari. "Joint pricing and openness decisions in software markets with reinforcing loops." System Dynamics Review 28, no. 3 (2012): 209–29. http://dx.doi.org/10.1002/sdr.1473.

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49

Gallaugher, John M., and Yu-Ming Wang. "Understanding Network Effects in Software Markets: Evidence from Web Server Pricing." MIS Quarterly 26, no. 4 (2002): 303. http://dx.doi.org/10.2307/4132311.

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50

Kundu, Jagadish, and Abhik Mukherjee. "Pricing model for eliminating productivity concerns of outsourced software maintenance services." International Journal of Industrial and Systems Engineering 11, no. 1/2 (2012): 167. http://dx.doi.org/10.1504/ijise.2012.046662.

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