@inproceedings{35660, abstract = {{Effective customer loyalty programs are essential for every company. Small and medium sized brick-and- mortar stores, such as bakeries, butcher and flower shops, often share a common overarching loyalty program, organized by a third-party provider. Furthermore, these small shops have limited resources and often cannot afford complex BI tools. Out of these reasons we investigated how traditional brick-and- mortar stores can benefit from an expansion of service functionalities of a loyalty card provider. To answer this question, we cooperated with a cross-industry customer loyalty program in a polycentric region. The loyalty program was transformed from simple card-based solution to a mobile app for customers and a web- application for shop owners. The new solution offers additional BI services for performing data analytics and strengthening the position of brick-and-mortar stores. Participating shops can work together in order to increase sales and align marketing campaigns. Therefore, shopping data from 12 years, 55 shops, and 19,000 customers was analyzed.}}, author = {{Kucklick, Jan-Peter and Kamm, Michael Reiner and Schneider, Johannes and vom Brocke, Jan}}, booktitle = {{Proceedings of the 53rd Hawaii International Conference on System Sciences}}, keywords = {{brick-and-mortar stores, business intelligence, case study, loyalty program}}, title = {{{Extending Loyalty Programs with BI Functionalities A Case Study for Brick-and-Mortar Stores}}}, year = {{2020}}, } @article{35662, abstract = {{While the analysis and usage of data are increasing in importance, the application of sophisticated BI solutions in small stores is limited by available technical capabilities and financial resources. This study investigates how brick-and-mortar stores can benefit from an expansion of service functionalities of a cross-industry loyalty card provider. Digitalizing the loyalty program created new opportunities, while the analysis of shopping data of 13 years, 19,000 customers, and 55 shops empowered data-based decision support.}}, author = {{Kamm, Michael Reiner and Kucklick, Jan-Peter and Schneider, Johannes and vom Brocke, Jan}}, issn = {{1058-0530}}, journal = {{Information Systems Management}}, keywords = {{Customer loyalty, case study, brick-and-mortar stores, business intelligence, loyalty programs}}, number = {{4}}, pages = {{270--286}}, publisher = {{Informa UK Limited}}, title = {{{Data mining for small shops: Empowering brick-and-mortar stores through BI functionalities of a loyalty program1}}}, doi = {{10.1080/10580530.2020.1855486}}, volume = {{38}}, year = {{2020}}, } @inproceedings{21563, abstract = {{Historically, the field of financial forecasting almost exclusively relied on so-called hard information – i.e., numerical data with well-defined and unambiguous meaning. Over the last few decades, however, researchers and practitioners alike have, following the advances in natural language understanding, started recognizing the benefits of integrating soft information into financial modelling. In line with the above, this paper examines whether contemporary attention-based sequence-to-sequence models, known as Transformers, can help improve stock return volatility prediction when applied to corporate annual reports. Using a publicly available benchmark dataset, we show, in an empirical analysis, that out-of-the-box Transformer models have the ability to outmatch current state-of-the-art results and, more importantly, that our proposed feature-based Transformer approach can outperform a robust numerical baseline. To the best of our knowledge, this is the first empirical study focusing on stock return volatility prediction (1) to ever experiment with state-of-the-art Transformer architectures and (2) to demonstrate that a model based solely on soft information can surpass its numerical counterpart. Furthermore, we show that by including an additional numerical feature into our best text-only model, we can push the performance of our model even further, suggesting that soft and hard information contain different predictive signals.}}, author = {{Caron, Matthew and Müller, Oliver}}, booktitle = {{2020 IEEE International Conference on Big Data (Big Data)}}, location = {{Online}}, pages = {{4383--4391}}, title = {{{Hardening Soft Information: A Transformer-Based Approach to Forecasting Stock Return Volatility}}}, doi = {{10.1109/BigData50022.2020.9378134}}, year = {{2020}}, } @article{17156, abstract = {{Business Process Management is a boundary-spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, information systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business processes. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field.}}, author = {{Beverungen, Daniel and Buijs, Joos C. A. M. and Becker, Jörg and Di Ciccio, Claudio and van der Aalst, Wil M. P. and Bartelheimer, Christian and vom Brocke, Jan and Comuzzi, Marco and Kraume, Karsten and Leopold, Henrik and Matzner, Martin and Mendling, Jan and Ogonek, Nadine and Post, Till and Resinas, Manuel and Revoredo, Kate and del-Río-Ortega, Adela and La Rosa, Marcello and Santoro, Flávia Maria and Solti, Andreas and Song, Minseok and Stein, Armin and Stierle, Matthias and Wolf, Verena}}, issn = {{2363-7005}}, journal = {{Business & Information Systems Engineering}}, keywords = {{Business process management (BPM), Social computing, Smart devices, Big data analytics, Real-time computing, BPM life-cycle}}, pages = {{145--156}}, publisher = {{SpringerNature}}, title = {{{Seven Paradoxes of Business Process Management in a Hyper-Connected World}}}, doi = {{10.1007/s12599-020-00646-z}}, volume = {{63}}, year = {{2020}}, } @article{53903, author = {{Hajipour, Vahid and Tavana, Madjid and Santos-Arteaga, Francisco J and Alinezhad, Alireza and Di Caprio, Debora}}, issn = {{2288-5048}}, journal = {{Journal of Computational Design and Engineering}}, number = {{4}}, pages = {{469--488}}, publisher = {{Oxford University Press (OUP)}}, title = {{{An efficient controlled elitism non-dominated sorting genetic algorithm for multi-objective supplier selection under fuzziness}}}, doi = {{10.1093/jcde/qwaa039}}, volume = {{7}}, year = {{2020}}, } @article{53902, author = {{Aziz, Azmin Azliza and Mousavi, Seyed Mohsen and Tavana, Madjid and Niaki, Seyed Taghi Akhavan}}, issn = {{2330-2674}}, journal = {{International Journal of Systems Science: Operations & Logistics}}, number = {{2}}, pages = {{172--181}}, publisher = {{Informa UK Limited}}, title = {{{An investigation of the robustness in the Travelling Salesman problem routes using special structured matrices}}}, doi = {{10.1080/23302674.2018.1551584}}, volume = {{7}}, year = {{2020}}, } @article{53905, author = {{Santos-Arteaga, Francisco Javier and Tavana, Madjid and Torrecillas, Celia and Di Caprio, Debora}}, issn = {{2029-4913}}, journal = {{Technological and Economic Development of Economy}}, number = {{6}}, pages = {{1366--1398}}, publisher = {{Vilnius Gediminas Technical University}}, title = {{{INNOVATION DYNAMICS AND FINANCIAL STABILITY: A EUROPEAN UNION PERSPECTIVE}}}, doi = {{10.3846/tede.2020.13521}}, volume = {{26}}, year = {{2020}}, } @article{53898, author = {{Jafari Songhori, Mohsen and Tavana, Madjid and Terano, Takao}}, issn = {{1381-298X}}, journal = {{Computational and Mathematical Organization Theory}}, number = {{1}}, pages = {{88--122}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Product development team formation: effects of organizational- and product-related factors}}}, doi = {{10.1007/s10588-019-09302-8}}, volume = {{26}}, year = {{2020}}, } @article{53904, author = {{Kaviani, Mohamad Amin and Tavana, Madjid and Kowsari, Fatemeh and Rezapour, Roghayeh}}, issn = {{1463-5771}}, journal = {{Benchmarking: An International Journal}}, number = {{6}}, pages = {{1929--1949}}, publisher = {{Emerald}}, title = {{{Supply chain resilience: a benchmarking model for vulnerability and capability assessment in the automotive industry}}}, doi = {{10.1108/bij-01-2020-0049}}, volume = {{27}}, year = {{2020}}, } @article{53906, author = {{Tavana, Madjid and Shaabani, Akram and Javier Santos-Arteaga, Francisco and Raeesi Vanani, Iman}}, issn = {{1996-1073}}, journal = {{Energies}}, number = {{15}}, publisher = {{MDPI AG}}, title = {{{A Review of Uncertain Decision-Making Methods in Energy Management Using Text Mining and Data Analytics}}}, doi = {{10.3390/en13153947}}, volume = {{13}}, year = {{2020}}, } @article{53900, author = {{Tavana, Madjid and Hajipour, Vahid and Oveisi, Shahrzad}}, issn = {{2542-6605}}, journal = {{Internet of Things}}, publisher = {{Elsevier BV}}, title = {{{IoT-based enterprise resource planning: Challenges, open issues, applications, architecture, and future research directions}}}, doi = {{10.1016/j.iot.2020.100262}}, volume = {{11}}, year = {{2020}}, } @article{53899, author = {{Tavana, Madjid and Amoozad Mahdiraji, Hannan and Beheshti, Moein and Abbasi Kamardi, Ali‐Asghar}}, issn = {{0143-6570}}, journal = {{Managerial and Decision Economics}}, number = {{7}}, pages = {{1365--1384}}, publisher = {{Wiley}}, title = {{{Optimal strategic alliance in multi‐echelon supply chains with open innovation}}}, doi = {{10.1002/mde.3181}}, volume = {{41}}, year = {{2020}}, } @article{53901, author = {{Tavana, Madjid and Hajipour, Vahid}}, issn = {{1463-5771}}, journal = {{Benchmarking: An International Journal}}, number = {{1}}, pages = {{81--136}}, publisher = {{Emerald}}, title = {{{A practical review and taxonomy of fuzzy expert systems: methods and applications}}}, doi = {{10.1108/bij-04-2019-0178}}, volume = {{27}}, year = {{2020}}, } @article{53890, author = {{Kaviani, Mohamad Amin and Tavana, Madjid and Kumar, Anil and Michnik, Jerzy and Niknam, Raziyeh and Campos, Elaine Aparecida Regiani de}}, issn = {{0959-6526}}, journal = {{Journal of Cleaner Production}}, publisher = {{Elsevier BV}}, title = {{{An integrated framework for evaluating the barriers to successful implementation of reverse logistics in the automotive industry}}}, doi = {{10.1016/j.jclepro.2020.122714}}, volume = {{272}}, year = {{2020}}, } @article{53894, author = {{Hashemi Petrudi, Seyed Hamid and Tavana, Madjid and Abdi, Mehdi}}, issn = {{2212-4209}}, journal = {{International Journal of Disaster Risk Reduction}}, publisher = {{Elsevier BV}}, title = {{{A comprehensive framework for analyzing challenges in humanitarian supply chain management: A case study of the Iranian Red Crescent Society}}}, doi = {{10.1016/j.ijdrr.2019.101340}}, volume = {{42}}, year = {{2020}}, } @article{53889, author = {{Farughi, Hiwa and Tavana, Madjid and Mostafayi, Sobhan and Santos Arteaga, Francisco J.}}, issn = {{0160-5682}}, journal = {{Journal of the Operational Research Society}}, number = {{11}}, pages = {{1740--1759}}, publisher = {{Informa UK Limited}}, title = {{{A novel optimization model for designing compact, balanced, and contiguous healthcare districts}}}, doi = {{10.1080/01605682.2019.1621217}}, volume = {{71}}, year = {{2020}}, } @article{53891, author = {{Yazdani, Morteza and Tavana, Madjid and Pamučar, Dragan and Chatterjee, Prasenjit}}, issn = {{0360-8352}}, journal = {{Computers & Industrial Engineering}}, publisher = {{Elsevier BV}}, title = {{{A rough based multi-criteria evaluation method for healthcare waste disposal location decisions}}}, doi = {{10.1016/j.cie.2020.106394}}, volume = {{143}}, year = {{2020}}, } @article{53896, author = {{Khanjani Shiraz, Rashed and Tavana, Madjid and Fukuyama, Hirofumi}}, issn = {{1432-7643}}, journal = {{Soft Computing}}, number = {{22}}, pages = {{17167--17186}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{A random-fuzzy portfolio selection DEA model using value-at-risk and conditional value-at-risk}}}, doi = {{10.1007/s00500-020-05010-7}}, volume = {{24}}, year = {{2020}}, } @article{53895, author = {{Ebrahimi, Bohlool and Dellnitz, Andreas and Kleine, Andreas and Tavana, Madjid}}, issn = {{0957-4174}}, journal = {{Expert Systems with Applications}}, publisher = {{Elsevier BV}}, title = {{{A novel method for solving data envelopment analysis problems with weak ordinal data using robust measures}}}, doi = {{10.1016/j.eswa.2020.113835}}, volume = {{164}}, year = {{2020}}, } @article{53893, author = {{Ebrahimi, Bohlool and Tavana, Madjid and Toloo, Mehdi and Charles, Vincent}}, issn = {{0360-8352}}, journal = {{Computers & Industrial Engineering}}, publisher = {{Elsevier BV}}, title = {{{A novel mixed binary linear DEA model for ranking decision-making units with preference information}}}, doi = {{10.1016/j.cie.2020.106720}}, volume = {{149}}, year = {{2020}}, }