Enterprise systems, emerging technologies, and the data-driven knowledge organisation

ABSTRACT

Enterprise Systems have become a critical feature of organisations not only for their integrative functions but also because they have become collectors and repositories of organisational data. In the past, this data was subject to relatively simplistic analysis, which could provide important but still basic information for management decision-making. With the emergence of new technologies, both data collection and analysis have become increasingly sophisticated and varied. This has led to significant improvements in the quality and timeliness of the information that can be used by management in decision-making and strategic planning. Even how managers use this information is being shaped by technologies that can augment human knowledge to further improve decision-making. With reviewing the literature, this editorial paper explains the scope of emerging technology, enterprise systems and the impacts on knowledge management and briefly introduces the articles included in this issue.

KEYWORDS:

• Enterprise systems
• emerging technologies
• data analytics
• knowledge management
• organisations

1. Introduction

Enterprise Systems are a collection of applications, extendable modules, and databases, which form the foundation of operations, processes, and decision support in enterprises (Vom Brocke et al., 2018; Wang & Ho, 2006). They have been extensively used by enterprises as they provide integration of functional areas as well as business processes at intra-organisational and inter-organisational levels (Xu, 2011). Through recent advancements in information technology and the increasing ubiquity of technology, along with the availability of various systems such as people-centric and transaction-centric systems, enterprise systems continue to exceed the traditional capacity of organisation-wide information systems (IS; Vom Brocke et al., 2018) and now provide greater opportunities for knowledge and intelligence management.

The evolution of enterprise systems can be examined in five stages (Panetto et al., 2016; Wang et al., 2013). Systems in the first stage are application-centric. These systems are composed of local systems with database technology supporting data processes and decision-making. These systems serve to support organisational departments trying to integrate existing legacy systems. Systems such as Material Resource Planning (MRP) store structured item data for production and procurement planning. The second stage in the lifecycle of enterprise systems is data-centric. Client-server architecture is used in systems such as Enterprise Resource Planning (ERP) to improve efficiency. With integrated data, these systems serve to support the whole enterprise rather than the department only. Systems in the third stage are process-centric, operating in the context of multiple sites. Process management technologies serve to improve effectiveness and support the supply chain using Internet technologies. These systems use integrated data gathered from various subordinates, suppliers, customers, and horizontal collaborators along the supply chain. Systems in the fourth stage are human-centric while those in the fifth stage are things-centric. Using real-time data and cloud computing, these systems focus on resilience. Things-centric systems are composed of smart things and sensors over the cloud, generating and analysing Big Data (Panetto et al., 2016). These systems are intended to be used ubiquitously in the fifth-generation technology standard for broadband cellular networks (5 G) that provide much wider bandwidth for simultaneous communication by many devices. They are considered as plug and play systems and focus on interoperability and internet connectivity. An organisation will use these systems to “manage and leverage on all possible networked connections among people, process, data, things, and services to achieve its strategic goals” (Panetto et al., 2016, p. 57) and accordingly need the appropriate technology and tools to achieve this.

Enterprise systems have become a critical feature of organisations, not only for their integrative functions but also because they have become collectors and repositories of organisational data. In the past, this data was subject to relatively simplistic analysis, which could provide important but still basic information for management decision-making. With the emergence of new technologies, both data collection and analysis have become increasingly sophisticated and varied. This has led to significant improvements in the quality and timeliness of the information that can be used by management in decision-making and strategic planning. Even how managers use this information is being shaped by technologies that can augment human knowledge to further improve decision-making.

This editorial paper for the special issue on Emerging Technology, Enterprise Systems, and Knowledge Management provides a review of the history of enterprise systems, data, analysis, and their applications in organisations with a particular emphasis on how emerging technologies are shaping enterprise systems and leading to knowledge-centric enterprise systems and management perspectives. It involved a careful search of the recent relevant literature, including 1) checking and reading all articles cited in the articles accepted for the special issue; 2) a Google Scholar search of articles published after 2015 (likely to be more relevant to current emerging technologies than earlier studies). This keyword search began with the following individual terms: Emerging (or Emergent) Technologies, Enterprise Systems (ERP, SCM, CRM, general IS), and Knowledge Management (and related words such as knowledge creation, acquisition, sharing, etc.). Next, we searched Emergent Technologies in combination with the following keywords: Knowledge Management and/or Enterprise Systems. In total 263 articles were reviewed, excluding duplicates.

2. The evolution of enterprise systems

ERP systems, as the core of Enterprise Systems, evolved from Material Requirements Planning systems (MRP-I). MRP-1 systems in the 1960s and 1970s were designed to plan and control inventories for organisations. Extended MRP-I systems, called Manufacturing Resource Planning (MRP-II), were popular in the early 1980s and were designed for managing and scheduling production and distribution activities. In the late 1980s, ERP systems extended the limited focus of MRP-II systems and provided an integrated solution for the whole organisation (Gorkhali & Xu, 2019). Based on the master data of the Chart of Account (COA) and material items for business operation, ERP integrated all the essential organisational functions including logistics, human resources, production and/or service, and financial functions. MAPII became a part of the manufacturing functions within EPR. During the evolution of enterprise systems different types of modules have emerged and been used by organisations spurred by advancements in computing technology and changing business needs (Figure 1).

Figure 1. Enterprise systems core – ERP.

In the following decade, enterprise systems further extended the traditional ERP core by adding inter-organisational capabilities and collaboration to these systems (Gorkhali & Xu, 2019; Romero & Vernadat, 2016) in the supply chain context. With ERP, functionalities of the system increased to include other parties or stakeholders such as customers and suppliers. These systems were designed to support managers in strategic decision-making and to improve communication within the organisation and among stakeholders (Boza et al., 2015; Taskin, 2011). However, a prerequisite for this was the strategic alignment between enterprise systems and business strategies (Taskin, 2011). With alignment, organisations, regardless of their business strategy type (e.g., defensiveness, analysis, aggressiveness, risk aversion or futurity), will benefit from these systems even under dynamic environments. The current standards that follow the line of evolution in ERP systems are called Intelligent-ERP (I-ERP; Jenab et al., 2019). These systems are defined as systems with technologies for “borderless enterprises” (Romero & Vernadat, 2016, p. 4) as they integrate collaboration with external parties including customers and other companies (Boza et al., 2015).

ERP or I-ERP systems have advanced capabilities to manage Big Data and Big Data Analytics (BDA). Romero and Vernadat (2016) discuss six enterprise systems (ES) modules that organisations have commonly used with Enterprise Resource Planning (ERP) as the core of the enterprise systems, and the extended modules: Supply Chain Management (SCM), Manufacturing Execution Systems (MES), Customer Relationship Management (CRM), Product Lifecycle Management (PLM) and Business Intelligence (BI). CRM modules emerged from a marketing focus on analysing customer information to improve customer service and increase satisfaction and sales (Romero & Vernadat, 2016). MES was initially developed in the 1980s to link with existing systems such as MRP to address processes such as “manufacturing planning, scheduling, traceability, quality assurance and reporting by providing real-time data visualization for operators and management” (Romero & Vernadat, 2016, p. 5). Extended versions of MES focus on improving control and accuracy in high-tech manufacturing. For example, designers and manufacturers of semiconductors, smartphones, and electrical cars apply advanced MES to the production line to operate precision production, zero dust, and intelligent robot environments.

PLM modules emerged from “Engineering Database” (EDB) concepts that help to manage data from product engineering. EDB evolved in the 1990s to “Engineering Data Management” (EDM), which provided additional functionalities to engineering processes. It further evolved in the mid-1990s to “Product Data Management” (PDM; Xu, 2011) which supports product design functions that link with bills-of-materials and engineering changes. It was finally named PLM in the 2000s, which supports “product data management for the whole product lifecycle” (Romero & Vernadat, 2016, p. 5).

BI modules were initially developed to address the need for improved reporting in the 1980s. These modules became more popular with the increased use of data warehousing technologies by providing quick and effective analysis for management. BI’s strength comes from its power to produce reports and effective visualisation. The next generation of BI-enabled enterprise systems, in the 2000s, had improved processing power as well as more analysis techniques including descriptive, predictive, and prescriptive analytics.

One of the most widely used modules in enterprise systems is SCM (Boza et al., 2015). SCM is broadly defined as:

The systemic, strategic coordination of the traditional business functions and the tactics across these business functions within a particular company and across businesses within the supply chain, to improve the long-term performance of the individual companies and the supply chain as a whole. (Mentzer et al., 2001, p. 18)

SCM systems ensure that all the parties involved in the supply chain follow required standards to produce products and services with the quality expected to satisfy the customers (Baymout, 2014). In a large commercial group, the SCM module of the enterprise systems can be used to integrate multiple subordinates and even multiple companies along the supply chain, for new systems adoption and in situations of mergers and acquisitions (Wang et al., 2013). The SCM module uses integrated multisite business processes. The functions are also effective in coordinating the flow of information among SCM parties, as well as decisions from the top management in a more efficient way.

As mentioned, enterprise systems encompass the whole of organisational information systems, integrating functional areas such as finance, accounting, marketing and sales, human resources, and production while optimising business processes (Boza et al., 2015, p. 257). While ES integrate all the functional areas in an organisation, they provide a single platform to coordinate all business processes (Baymout, 2014). With linkages to interorganisational modules, these systems further support process automation of interorganisation-wide operations, data collection, and analysis (Jenab et al., 2019; Taskin, 2011). Other benefits that enterprise systems provide include cost reduction, reduced turnover, and improved quality of processes and services (Jenab et al., 2019). As an extended enterprise system module, SCM aims to integrate and optimise business processes and information flow, and provide real-time and accurate data to organisations that improves and manages interaction with other parties in the supply chain (Baymout, 2014).

Sample data and information flows are demonstrated in Figure 2 which indicates how those modules can share data and processes with each other. For example, PLM can help to design a product with the listed bill of materials (BOM) that facilitates the design of the production routing and MRP calculation. The information of the raw items, work-in-progresses, and final products are then saved in the item master. The items listed in BOM can also be used for budget estimation as well as link to the finance module for estimating the cost of goods sold and overheads. The extracted data from these modules are then presented as information via the BI module. This information forms part of an emerging knowledge management system that can inform management decision-making (Pauleen & Wang, 2017).

Figure 2. Extended interorganisational enterprise systems.

2.1. Data in organisations

As organisations in the aforementioned areas implemented and used enterprise systems, they generated huge amounts of data. From these five areas and more, earlier versions of ERP systems generated data, mostly through legacy systems. This data was in a structured form. Data was stored and managed in relational database management systems (H. Chen et al., 2012). These systems generated data through applications such as orders, sales, inventory, production/manufacturing, shipments, personnel information, work logs, etc. (Appelbaum et al., 2017). Some modules in the enterprise systems can serve with other modules and link to other external systems (e.g., accounting data and material data), while other specific areas generate, store, manage and analyse specific types of data (e.g., the “Infotypes” as dataset attributes in SAP ERP are used to store human resource data).

In addition to the internal data organisations generate and collect, they also access and store external data. These external data are received in four different types (CDQ, 2021): open data (usually generated by governments, non-profit organisations, and commercial players such as universities, banks, trade groups, etc.), shared data – including internal data from other companies made available, paid data – purchased from professional data providers or portals, and social media data – generated by users. While paid data is usually in a structured form, social media data is usually unstructured. Open data and shared data can be any type: structured, semi-structured, or unstructured. News data, social media data, data from the Internet of Things and census data are among the examples of external data organisations collect (Appelbaum et al., 2017).

Organisations collect data from these internal and external sources to transform it into information and then knowledge to make decisions. This relationship between data, information, and knowledge is represented by a hierarchy called the Data-Information-Knowledge (DIK) pyramid (Rowley, 2007). Different studies added wisdom (Dalal & Pauleen, 2019) or experience (El Houari et al., 2015) to the hierarchy to explain how it is used in decision-making.

Data generated and acquired were stored in relational databases and managed with database management systems (DBMS; Appelbaum et al., 2017; Che et al., 2013). Recently, distributed systems and NoSQL databases have become popular as well (e.g., Microsoft Azure NoSQL). Archival data was stored in data marts and data warehouses for further analysis to support decision-making. Analyses done with the data from these systems are usually called Business Intelligence (BI). Various tools such as Online Analytical Processing (OLAP, to amend DBMS for data analytics), Data Mining, Reporting/Queries, Dashboards, and Scorecards are used to transform the data into information. Within these processes, statistical analyses such as multivariate statistics, discriminant analysis, factor analysis, and cluster analysis are used to get insight from the data. In addition to these statistical techniques, some data mining techniques such as association, classification, regression, predictive modelling, anomaly detection, segmentation, and clustering are often used to find hidden and interesting patterns from the data to support decision-making (H. Chen et al., 2012).

With the above tools, organisations can run business analytics and business intelligence modules that use data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to produce information that provides greater insight into business operations, facilitating better and more timely management decisions (Davenport & Harris, 2017). Managers combine the visualised outputs of these analyses with their experiences to achieve knowledge, defined as “awareness or familiarity gained by experience” by the Oxford Dictionary. Managers combined their functional, organisational, and problem-specific knowledge, in the form of tacit knowledge, which is more intuitive, or explicit knowledge, which is more objective or validated knowledge (Nonaka & Takeuchi, 2007; Vernadat et al., 2018) to make more informed decisions.

3. Intelligent enterprise systems

Intelligent enterprise systems (Jenab et al., 2019) effectively integrate PLM, MES, BI, CRM, and SCM with ERP. These components benefit from the Big Data infrastructure, tools, techniques, and analysis specific to their focus. PLM modules have advanced capabilities for managing collaborative environments and information throughout the product development lifecycle (Romero & Vernadat, 2016). PLM can connect with the Internet of Things (IoT) and cloud technologies while performing tasks. MES modules achieve and sustain smart production that will bring more flexibility and detailed planning to production systems (Romero & Vernadat, 2016). Integrating data and analyses is now a key component of MES. The pace of evolution will grow with the use of Big Data. Traditional statistical methods that are mostly based on descriptive analysis will be transformed into more inductive methods (Romero & Vernadat, 2016). BI modules are affected mostly by developments in analytics, which are affected by the availability of data as well as other technologies such as IoT and cloud that process and store the data.

An intelligent enterprise system collects data from various sources. In contrast with traditional ES using relational data, the type of data intelligent enterprise systems would be dealing with is Big Data. Big Data is “datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze” (Manyika et al., 2011, p. 1). They are data from multiple platforms, addressed by traditional databases as well as NoSQL, Data Pipeline, middleware, and data warehouses. Big Data is identified through characteristics called 5Vs (Gressel et al., 2021). These 5Vs refer to volume, velocity, variety, value, and veracity. Building an architecture to support the storage, acquisition, analysis, and management of Big Data is a major challenge regarding the five V’s, but the potential benefits are substantial (Table 1).

Table 1. Big data dimensions, features and benefits

Dimension	Features	Benefits
Volume	Data sets exceed the capacity of DBMS and traditional analytics tools. Data expressed in terabytes, petabytes, and more Data storage Infrastructure, human resources, and processing power are necessary.	Traditional capacity is not a limitation anymore and thus more organisations can utilise big data for analysis and insight.
Velocity	Data collection, analysis, and interpretation are continuous processes. Managing data stream or transfer from various devices and to address the heterogeneous structure of data	Gaps reduced/ overcome in the multiplatform processing for users, system admin and other stakeholders and data can be processed in (near) real time to achieve value.
Variety	Type of data from diverse data sources – often unstructured – from social media, sensors, text, audio, or video files. Some of these formats pose problems for data analysis because of device and data compatibility, e.g., converting the formats, understanding the content of the data, and making them readable to everyone.	Previously structured and unstructured data can both be addressed. Organisations can access data from various sources and compare them. Can provide a better decision basis and create potential and meaningful knowledge.
Veracity	Data sources must be credible and suitable for the organisation to provide reliable results. Involves data sourcing and cleaning; Critical aspect, as data volatility may be out of internal organisational control. Data accuracy can change over time and needs to be accounted for.	Big data analytic tools and NoSQL databases can increase the assurance of accuracy for the various data and reduce ambiguity.
Value	Economic and social benefits are derived from the use of big data. Refers to knowledge and value extraction from data; It can be costly to extract meaning from big data and proper ROI has to be established.	Improving customer experiences by analysing big data and customising services to individuals. Problems can be identified more easily with the extended enterprise systems’ scope with the admin console. Efficiency and efficacy are achieved to reduce cost and generate potential revenues.

Table synthesised from Abbasi et al. (2016) Gressel et al.,2021), McAfee and Brynjolfsson (2012),and Sivarajah et al. (2017)

The big data that intelligent enterprise systems deal with is generated by three main domains (Saggi & Jain, 2018): machines, humans, and businesses. Common sources of machine-generated data are sensors, computer networks, videos (file and streaming), audio, mobile applications, and wearable devices. Human-generated data comes from system entries, files, logs, emails, research, and recently social media. Transactional data are generated from various business processes, corporate data, and data from government agencies (Saggi & Jain, 2018) either by humans, machines, or both of them. The emergent characteristics of Big Data make the data different from traditional data and require special infrastructure and analysis for data to be transformed into information.

Big Data, collected from various domains and sources such as weblogs, social media, and the computer cloud (Lu & Keech, 2015), can be stored in Hadoop Distributed File Systems (HDFS), Distributed Storage Platforms, or an extract, transform load (ETL) platform (B. Chen et al., 2017). After integration, it can be further transferred to the enterprise system for analysis and decision-making. Parallel processing platforms such as Hadoop and similar technologies are used as part of the infrastructure for large data sets in organisations. Commonly used software for Big Data includes Apache Hadoop, Storm, MapReduce, BigQuery, Flume, Mahout, Spark, and WibiData (Mahout, 2021; Saggi & Jain, 2018). Other related Hadoop-based projects include Pig, Hive, ZooKeeper, Cassandra, etc.

3.1. Advanced data analysis

Big Data Analytics (BDA) is concerned with applying modern and advanced techniques to analyse large sets of data that fit the characteristics of Big Data (Suthaharan, 2014). Types of analytics include descriptive, predictive, prescriptive, diagnostic, and autonomous (Davenport & Harris, 2017). Table 2 summarises these types of analytics, their associated technologies, and their relationship to intelligent enterprise systems and knowledge management.

Table 2. Types of Analytics

	Descriptive	Predictive	Prescriptive	Diagnostic	Autonomous
Definition	Describing the current situation through reports generated with past and current data	Forecasting, and evaluating alternative outcomes given different circumstances on the available data	Developing and assessing the optimised best course of action and solutions to business problems	Developing insights from data to find out root causes of problems	Developing models learning from data to minimise human involvement
Tools/Methods	Scorecards Dashboards Data warehousing Reporting Simple statistical methods Observations Cases	Mathematical and statistical (descriptive and inferential) techniques, data mining (e.g., classification, regression, clustering), machine learning, text mining, etc.	Simulation Optimisation Multi-criteria decision modelling Machine learning	Query drills Agile and spatial visualisation	Artificial Intelligence, Cognitive technologies, and Machine Learning
Roles in enterprise systems	They are presented as the dashboards and reports, letting users know in real time what is going on in their business. It offers insights into business situations and customers (e.g., financial strength of an organisation); Helps identify business opportunities and challenges.	Help managers with predicting future conditions; With predictive data analytics, managers can set up strategies, prices, and organisational planning.	Assessing scenarios to find out the optimum solution to business cases to help decision-making. They are tools built-in or attached to the enterprise systems to extract data for optimisation, very often used for cost management, customer satisfaction, and preparing quantified items.	Help managers identify the root causes of any problem; It can help to find out transportation and distribution blockers. In public health information systems context, this can be used to investigate the source of an infectious disease.	Creating models (statistical) with less human input for decision-making. These can be achieved via the other analytics approaches with programmed Artificial Intelligence and machine learning. These can be applied in places such as auto-port systems.
Relationship to Knowledge and Role in KM	Past and present opportunities, i.e., What has happened or is happening now?	Predict future conditions, events by recognising patterns and relationships, i.e., What could happen? What will happen?	The optimised solution, cause-and-effect relationships, i.e., What should happen? Why should that happen?	Root causes discovery and developing insight, i.e., Why did something happen?	Evolution of models based on data with less and less human involvement, i.e., What can be learned from the data? How can the models be improved to learn more from the data?

Table adapted from Gressel et al. (2021) with literature synthesised from Davenport and Harris (2017), Sivarajah et al. (2017)

Big Data Analytics (BDA) is about “analyzing heterogeneous data to mine insightful information through unknown patterns by applying various predictive algorithms, semantic analysis, statistical analysis methods, and technologies” (Saggi & Jain, 2018, p. 771). BDA techniques are related to the four categories of skills (Choi et al., 2018): statistics, optimisation, data mining, and machine learning.

Statistics are commonly used for statistical computing and parallel processing with Big Data. Statistical analysis is used for the generalisability of a sample, identifying patterns, relationships, and correlations from the data. Although the techniques used in statistics are well-established and fairly quick, the validity of the results for Big Data, especially regarding the heterogeneity of the data, has been problematic.

Optimisation is used for finding the optimum solution. With Big Data, optimisation is done in real time using parallel processing on large data sets. While these techniques are well-established in academia and industry, the challenge of optimisation is achieving the quick processing necessary to deal with Big Data generation (Choi et al., 2018).

Data mining techniques are used for finding interesting patterns in the data. Clustering techniques, distributed processing, and segmentation analysis using large data sets are some common techniques associated with data mining. While data mining techniques can deal with different data by combining statistical and machine learning techniques, their major weakness stems from the limitations of the model being tested (Choi et al., 2018).

Machine learning techniques are used for producing solutions through algorithms learning from data. Parallel processing, deep learning, and support vector machines are some commonly used techniques for analysing Big Data. Machine learning techniques and algorithms are capable of analysing different types of data including image, text, and video. While these techniques are good at capturing complex patterns and behaviours from various types of data, training the algorithm with high accuracy and performance is time-consuming (Choi et al., 2018).

Generally, in order to be able to analyse Big Data, organisations need to adopt scalable strategies. After following these strategies, data need to be pre-processed with a high level of preparation for the analysis. The complex algorithms used for analysing the data may require a parallel computing environment – multi-layer computing architecture, sometimes with cloud to support processing resources allocation, information and data passing, downloading and runtime monitoring (i.e., cloud services such as PaaS or IaaS; Che et al., 2013).

Advanced data analytics use specific strategies to analyse data (Choi et al., 2018). These strategies make the analysis of large set data more manageable. One strategy is to divide large data sets into smaller sets to analyse (“divide and conquer”). Multiple processors can also share the load of the computation (“distributed and parallel processing”). Each new case has some role in improving the learning of the algorithm (“incremental learning using new cases”). They are capable of learning from the selected samples to generalise the results to the population (“statistical inference”). The algorithms help identify the most relevant and useful features for a given analysis to represent the main features (“feature selection”). These algorithms use sophisticated methods such as fuzzy methods and neural networks for dealing with data (“addressing uncertainty with learning”). Finally, the techniques provide near-optimal and alternative feasible solutions as well as adopting scalability.

The emergence of Big Data has led to the development of new analytics types including Text Analytics, Web Analytics, Network Analytics, and Mobile Analytics (H. Chen et al., 2012). Text Analytics is broadly used in organisations. It can be used for processing queries, document representation, search-related systems and engines, and information retrieval. Highly used areas of Text Analytics include information extraction (to extract specific information from documents), topic modelling (identifying the main themes within the text), question answering (Q/A) (to answer “factual questions” such as what, who, when, and where), and opinion mining (to extract, classify and understand concepts and opinions within the text; H. Chen et al., 2012).

Web Analytics is built on top of the pillars of data analytics and Natural Language Processing (H. Chen et al., 2012). The main technologies behind Web Analytics are web services, computational linguistics, information retrieval, etc. Web Analytics has benefited from and also contributed to developments in cloud computing and services, social media, Internet security, etc. (H. Chen et al., 2012). Computational intelligence techniques such as Artificial Neural Networks, Fuzzy Systems, Deep Learning, and Swarm Intelligence are widely used with Web Analytics (Ghani et al., 2019). Common techniques used in this area of analytics include sentiment analysis, text mining, social network analysis, and various forms of modelling (Ghani et al., 2019).

Network Analytics is based on bibliometric analysis. Founding technologies of Network Analytics include mathematical modelling and visualisation of networks, citation analysis, and topology. Research about link mining, agent-based modelling, social influence analysis, etc. is closely related to this area of analytics (H. Chen et al., 2012).

Mobile Analytics is built on technologies such as web services and mobile device platforms. This type of analytics works with mobile apps, gamification, mobile networking, etc. (H. Chen et al., 2012).

4. Emerging technology, data, and knowledge

Reconfiguring the capabilities of enterprise systems in light of emerging technologies has been pushing organisations to evolve towards a flatter and more dynamic structure (Bi et al., 2014). This is in response to changes in the business and technological domains, which are resulting in rapidly growing amounts of data that need to be acquired, stored, and analysed. Along with flat and dynamic structures, organisations are leaning towards becoming more decentralised in decision-making. In the digital world, organisations need to react more quickly, with faster, more accurate analyses and decisions (Weichhart et al., 2016). Organisations are now operating in a globally connected environment with dynamically scalable, interconnected, and transparently accessible data (Weichhart et al., 2016).

Enterprise systems will continue to evolve. Technologies such as artificial intelligence (AI), machine learning, IoT, 5 G, and blockchain are or will soon become key components of Industry 4.0 and are critical for the digital transformation of organisations (Büyüközkan & Göçer, 2018). They are probably going to be a natural part of the enterprise and related systems offering a range of functions within the organisation and in upstream and downstream supply chains.

4.1. Artificial and augmented intelligence

A significant part of this evolution will be the increasing adoption of AI and other emerging technologies to augment and improve enterprise systems processes (Vom Brocke et al., 2018). This means that more complex functions, techniques, and algorithms such as Natural Language Processing (NLP) and deep neural networks will be commonplace in enterprise systems. AI was created to mimic human behaviour. Since then, it has been used in various systems such as “expert systems, knowledge-based systems, intelligent decision support systems, intelligent software agent systems, intelligent executive systems” (Duan et al., 2019, p. 67). Managers have benefited from using systems with AI to gain greater insight into their decision-making. Especially with the advancements in Big Data, the use of AI has attracted increasing attention from organisations. AI can potentially be used, along with other cognitive technologies such as ML, to create models and analyse data to provide more insight to managers and stakeholders as well as automate various processes in organisations (Davenport & Harris, 2017).

Augmented technologies including augmented intelligence will be more widely applied by the organisations to their decision-making. Augmented intelligence aims to enhance human capabilities and business operations by integrating mathematics, computation, and visualisation (Brock & Von Wangenheim, 2019). Using deep learning, augmented intelligence can provide accurate and timely data-driven information from Big Data, data from Internet crowds, or data from cross-media (Pan, 2016) to support decision-making (Jarrahi, 2018).

4.2. Internet of things, web 3.0, and blockchain

The Internet of Things (IoT) is related to the connectivity of smart things on the Internet (Fleisch, 2010) and will also significantly affect enterprise systems. As the Internet connects computers, IoT connects a huge number of objects, devices, machines, etc., using sensors and providing data generation, storage, and analytics capabilities (Aryal et al., 2018). IoT is capable of improving efficiency and offering a competitive advantage to organisations (Aryal et al., 2018) when integrated with enterprise systems. Through the smart devices connected over the Internet, organisations can apply various analytics techniques to the data in real time for better decision-making. These devices also provide continuous communication between smart things and the organisation to help improve scheduling, planning, distribution, maintenance, and similar activities (Bi et al., 2014). IoT has been changing the way customers, suppliers, and producers work with each other (Lu, 2017). It is changing the centre of decision-making for specific processes. For example, with IoT, customers will be more involved in producing decisions, which have usually been controlled by manufacturers or retailers.

Hand in hand with IoT, Web 3.0 will also play an important role in emerging intelligent enterprise systems by connecting knowledge and knowledge sources. Web 3.0 is described as “an integrated web experience where the machine will be able to understand and catalog data in a manner similar to humans” (Rudman & Bruwer, 2016, p. 132). Web 3.0 could use AI and web learning for personalised machine-readable content, where organisations will provide and develop an environment for individuals to publish their services (Sharma, 2018; Shivalingaiah & Naik, 2008). This technology will provide great benefits in searching required data in any type from the real world in real time (Rudman & Bruwer, 2016).

4.2.1. Blockchain and security

As the amount of data and devices/things generating data increase and processes are moved to the cloud, data security will be a growing concern. Blockchain, “a distributed data structure that is replicated and shared among the members of a network” (Christidis & Devetsikiotis, 2016, p. 2293) provides a transparent platform for exchanging information securely. Blockchain can be used with IoT to improve the security among things when sharing information. General application areas of blockchain include transferring money in real time, and digital registry for assets and documents, etc. (Swan, 2018). Proprietary knowledge and information can also be protected in blockchains. For example, enterprise systems can utilise the blockchain to uniquely and safely create the identity of valuable assets. It could also be used to trace the logistics flow along the supply chain (Haddara et al., 2021).

As mentioned previously, 5 G mobile technology is now considered the standard for the next generation of mobile connectivity (Gressel et al., 2021; Palattella et al., 2016). 5 G is essential for organisations using IoT as it provides an infrastructure with the high bandwidth required to handle large flows of data.

Figure 3 summarises the role and place of the technologies discussed above as they relate to previous, current and future enterprise systems. Further, it places these technologies on the continuum of data, information, and knowledge and illustrates how they can be accessed and used by organisations. The figure demonstrates how raw data are aggregated from the various modules (left side of the figure) to support decision-makers in the organisations. It further embeds the ideas of data analytics with the concept of intelligent enterprise systems (right side of figure). With new technologies such as IoT and NoSQL, big data are addressed in extended enterprise contexts – the supply chain, and multi-industrial and multinational environments. Working together these technologies and capabilities can create synergies that can extend data collection and analysis into information and knowledge resources. Since the ultimate objective of achieving these synergies is organisational effectiveness, enhanced decision-making, sustainability, and more robust strategic planning, managing and extracting the most value from them should, we suggest, be the focus of knowledge management research and practice.

Figure 3. A DIK framework to integrate emerging technology, data, and knowledge in the enterprise systems.

5. Emerging technology, enterprise systems, and knowledge management articles

The articles accepted for this special issue cover a range of topics and each of them has gone through multiple rounds of review. The authors are from various countries including Australia, China, Malaysia, New Zealand, Taiwan, Turkey, the United States and so on. These papers cover a wide range of issues in emerging technologies and enterprise systems and related aspects of knowledge management. Popular topics in the public and private sectors are covered using a number of research methods.

Chang (2021) reports a Taiwanese case study in knowledge management, green supply chain and Industry 4.0, illustrating the rapid production of intelligent factories to reduce labour demand and raw material costs. The initiative requires the cooperation of industry, government, and academia to assist in balancing the cost reductions and the environmental protections. Likewise, Sanjay’s (2021) case study reveals the use of new data analytic technology to enhance enterprise systems in the design and production of agile manufacturing. It demonstrates how significant improvements in production strategy can be achieved through new initiatives and knowledge management with analytic technologies. Focusing on the auto insurance industry, Liu et al. (2021) show what kinds of data can be collected via IOT for analysis in the Knowledge Management Systems including human driving behaviours, timing, and the road conditions on top of the limited traditional measures of driving age and mileage. Intezari et al. (2021) introduce the concept of Organizational Knowledge Identity (OKI) – encompassing aspects of culture, KM and enterprise systems to explain how data analysts’ knowledge affects the adoption and effective use of analytics in organisations. It also discusses the types of knowledge and sharing flows within these systems.

The paper by Xia et al. (2021), based on the hospitality industry, adopts a survey approach to investigate the factors influencing travellers’ booking decisions, and the impact of online review systems on enterprises’ knowledge management in managing hotels. Li and Dai (2021) apply Natural Language Processing (NLP) tools to analyse the policy comments on a popular social media platform. The research outcome provides practical and research implications for the management of social media enterprises as well as the enactment and change of government policies. Arshad’s article (Arshad et al., 2021) provides insight about Enterprise Content Management (ECM) technologies as knowledge management systems to support sustainable organisational business processes. Several large organisations are investigated with a focus on ECMS during the post-implementation period. With a framework, it provides a guide for future organisational business processes and design. J. C. v. Chen et al. (2021) look into the knowledge sharing processes and performance in more than 50 start-up teams working in the area of emerging technologies. It is an early study using constructive learning theory to examine shared knowledge systems in co-working spaces.

6. Conclusions and Implications – What the Future May Bring

Emergent and often unpredictable circumstances are increasingly confronting organisations (El Kadiri et al., 2016; Yu et al., 2021). Intelligent enterprise systems provide a number of capabilities that can enhance organisational responses in managing these challenging environments. For example, the growing influence of the “Social Matrix” – the social media environment that allows users to comment on, “like”, and share information (El Kadiri et al., 2016) – presents significant challenges to organisations that now find themselves inextricably connected to this environment. Intelligent enterprise systems linked to the internet and social media platforms such as Facebook can help management keep track of both specific social media episodes that may affect individual organisations as well as larger social trends affecting whole industries or societies. With increased internet connectivity from IoT and 5 G technology, organisations have the opportunity for interacting more intensively with current customers and discovering new customers from around the world.

Intelligent enterprise systems can potentially integrate AI and machine learning into the architecture. Advances in enterprise systems based on Big Data and analytics will push organisations’ digital transformations (Jenab et al., 2019). Among numerous implications, an example application will be identifying, predicting, and analysing unexpected behaviour of customers (Jenab et al., 2019). Collecting and analysing data from a variety of sources and providing real-time analytics will soon be the norm for intelligent enterprise systems. Organisations can use this information to both anticipate and respond to local and global events and trends. Already ES can collect data from internal and external sources including devices and through IoT and process it with parallel and large-scale data processing platforms such as Hadoop (Che et al., 2013). Edge computing will be a critical feature of the next iteration of the Internet (Gressel et al., 2021) as it can be used to process data close to or where it is generated (Caprolu et al., 2019). This technology when integrated with relevant technologies such as 5 G, IoT, and mobile and edge devices (Mao et al., 2017), will be able to analyse big data with minimum latency. Soon the integrated data will be analysed using AI, or machine learning techniques to gain insights for decision-making and innovation.

The key challenge for organisations and senior management is to learn how to manage the use and implementation of these technologies as well as how to make the best use of its outputs (Pauleen & Wang, 2017). These tasks require human knowledge, intelligence, and perhaps wisdom. Davenport (2006) suggests that the use of technologies such as big data and analytics will require new ways of managing and deciding, while Pauleen et al. (2016) caution that an over-reliance on such technologies may diminish management wisdom, resilience, and common sense. The challenge, as we see it, does not concern what technologies will be developed and when, but rather how organisations and managers will understand these technologies and how they can be applied most effectively within their organisations. To accomplish this, the management side of organisations must have enough collective knowledge to ask the questions and understand the answers that will guide the purchase, development, and implementation of technologies such as intelligent enterprise systems (Gressel et al., 2021).

Although we do not have a crystal ball to see into the future, in our view emerging technologies will continue to develop and influence the design and applications of intelligent enterprise systems, including new initiatives such as augmented reality and virtual reality. The benefits of technologies such as IoT, cloud computing, 5 G, Artificial Intelligence, and blockchain have already been recognised. They will generate increasing amounts of data and automatically link them with enterprise systems. It will be up to senior management to effect the organisational changes needed to make these technologies work for the organisation and its stakeholders. This will require intelligent planning. With decades of experience researching and managing organisational knowledge for effective planning and decision-making, we believe the KM discipline is well-situated to take the lead in managing the implementation and use of intelligent enterprise systems.

Disclosure statement

No potential conflict of interest was reported by the author(s).