Artificial intelligence and internet of things in small and medium-sized enterprises: A survey

Hansen, Emil Blixt; Bøgh, Simon

doi:10.1016/j.jmsy.2020.08.009

Cited by 223 publications

(138 citation statements)

References 65 publications

Supporting

Mentioning

111

Contrasting

Order By: Relevance

“…Second, this study extends the scope of analytics research by conceptualizing and empirically demonstrating that the conditions leading to analytics applications can be asymmetrical, thus more complex than assumed by most analytics studies based on regressionbased methods (e.g., Gupta and George, 2016). An important implication of this study is the need to unearth the assumptions that underlie analytics studies and to consider the possibility Third, whilst SMEs are traditionally examined as one homogeneous group (e.g., Ferraris et al, 2019, Maroufkhani et al, 2020, Hansen and Bøgh, 2020, Dong and Yang, 2020, Liu et al, 2020, this study provides additional empirical evidence to support the idea that small and medium-sized firms could have distinctive patterns of IS adoption (e.g., Reyes et al, 2016, Gillon et al, 2014, Thong et al, 1996, Haase and Franco, 2011. The implications of this finding are twofold.…”

Section: Theoretical Contributionsmentioning

confidence: 82%

“…Based on the literature review, the following gaps concerning marketing analytics use are identified and will be addressed in this study: First, although studies have established the link between the use of business analytics and improved firm performance such as innovation (e.g., Duan et al, 2020), decision making effectiveness (e.g., Cao et al, 2015), supply chain performance (e.g., Zhan and Tan, 2020), and competitive advantages (e.g., Cao et al, 2019, WangYeoh et al, 2019, the majority of these studies are based on large companies and their findings should not be applied to SMEs directly without further investigation, as SMEs are not smaller versions of larger firms (O'Regan et al, 2005). Aside from only a few studies (e.g., Ferraris et al, 2019, Maroufkhani et al, 2020, Hansen and Bøgh, 2020, Liu et al, 2020, business/marketing analytics use and its effect on the performance of SMEs are yet to be fully investigated (e.g., Maroufkhani et al, 2020, Liu et al, 2020, Wang et al, 2018. Second, although prior studies have used configurational approaches to understand organizational relationships in SMEs (e.g., Hughes et al, 2019, Uwizeyemungu et al, 2018, Poorkavoos et al, 2016, Magistretti et al, 2020, no research has investigated how the configurations of multiple conditions affect business/marketing analytics use in SMEs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…de Haan et al, 2007, Haase andFranco, 2011) and tends to be associated with different patterns of IS investment and use (e.g. Gillon et al, 2014, Thong et al, 1996, Haase and Franco, 2011, Dong and Yang, 2020, almost all prior analytics studies have examined SMEs as one homogeneous group (e.g., Ferraris et al, 2019, Maroufkhani et al, 2020, Hansen and Bøgh, 2020, Liu et al, 2020. Thus, whether or not small and mediumsized firms differ in terms of marketing analytics use remains unexamined in empirical research.…”

Section: Literature Reviewmentioning

confidence: 99%

“…SMEs, because of their small business size, generally lack resources and specialist expertise, thus tend to have difficulties exploiting analytics (Gillon et al, 2014, Liu et al, 2020, Maroufkhani et al, 2020, Hansen and Bøgh, 2020. Nevertheless, there is evidence in the literature to suggest that some SMEs have adopted business analytics to improve decision making and to gain competitive advantages (Ferraris et al, 2019, Dong and Yang, 2020, Liu et al, 2020 Ferraris et al, 2019, Maroufkhani et al, 2020, Hansen and Bøgh, 2020, Dong and Yang, 2020, Liu et al, 2020, there is evidence to suggest that small and medium-sized firms are significantly different (e.g. de Haan et al, 2007) and have distinctive patterns of IS adoption (e.g., Reyes et al, 2016, Gillon et al, 2014, Thong et al, 1996, Haase and Franco, 2011.…”

Section: Theoretical Considerationsmentioning

confidence: 99%

See 3 more Smart Citations

Identifying the configurational conditions for marketing analytics use in UK SMEs

Duan

Tian

2021

View full text Add to dashboard Cite

PurposeWhile marketing analytics can be used to improve organizational decision-making and performance significantly, little research exists to examine how the configurations of multiple conditions affect marketing analytics use. This study draws on configuration theory to investigate marketing analytics use in small and medium-sized enterprises (SMEs).Design/methodology/approachThis research employs a fuzzy-set qualitative comparative analysis using data collected from a survey of 187 managers in UK SMEs.FindingsThe key findings show that (1) configurations of multiple conditions provide alternative pathways to marketing analytics use, and (2) the configurations for small firms are different from those for medium-sized firms.Research limitations/implicationsThe research results are based on several key configurational factors and a single key-informant method to collect subjective data from UK SME managers.Practical implicationsThe study helps SMEs to understand that marketing analytics use is influenced by the interaction of multiple conditions, that there are alternative pathways to marketing analytics use, and that SMEs should choose the configuration that fits best with their organizational contexts.Originality/valueThe study contributes to the literature by addressing an important yet underresearched area, i.e. marketing analytics use in SMEs, applying a configurational approach to the research phenomenon. It highlights different pathways to marketing analytics use in SMEs. The findings provide empirical evidence on the possibility and implication of marketing analytics use being asymmetrical and different between small and medium-sized firms.

show abstract

Section: Theoretical Contributionsmentioning

confidence: 82%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Theoretical Considerationsmentioning

confidence: 99%

See 2 more Smart Citations

Identifying the configurational conditions for marketing analytics use in UK SMEs

Duan

Tian

2021

View full text Add to dashboard Cite

show abstract

“…e selected studies cover the application of IoT in the pain assessment, and some discuss the enabling technologies for management of pain. Hansen and Bøgh [21] presented a detailed investigation and survey on how AI and IoT are among manufacturing SMEs and explored the existing opportunities and limitations for enabling predictive analytics. Initially, the overview of the IoT and AI-enabled system along with four capabilities are provided, and then review of the literature and its analysis is shown.…”

Section: Ai-enabled Sensing and Decision-making For Iot Systemsmentioning

confidence: 99%

AI‐Enabled Sensing and Decision‐Making for IoT Systems

Qinxia

Nazir

et al. 2021

Complexity

View full text Add to dashboard Cite

The influential stage of Internet of Things (IoT) has reformed all fields of life in general but specifically with the emergence of artificial intelligence (AI) has drawn the attention of researchers into a new paradigm of life standard. This revolution has been accepted around the globe for making life easier with the use of intelligent devices such as smart sensors, actuators, and many other devices. AI-enabled devices are more intelligent and capable of doing a specific task which saves a lot of resources and time. Different approaches are available in the existing literature to tackle diverse issues of real life based on AI and IoT systems. The role of decision-making has its own importance in the AI-enabled and IoT systems. In-depth knowledge of the existing literature is dire need of the research community to summarize the literature in effective way by which practitioners and researchers can benefit from the prevailing proofs and suggest new solutions for solving a particular problem of AI-enabled sensing and decision-making for the IoT system. To facilitate research community, the proposed study presents a systematic literature review of the existing literature, organizes the evidences in a systematic way, and then analyzes it for future research. The study reported the literature of the last 5 years based on the research questions, inclusion and exclusion criteria, and quality assessment of the selected study. Finally, derivations are drawn from the included paper for future research.

show abstract

Research Process of Carbon Dots in Memristors

Hao

Yan

Wang

et al. 2023

Adv Elect Materials

View full text Add to dashboard Cite

The number of interconnected equipment is predicted to surge to 75 billion by 2025, and the involved global data will swell to 175 zettabytes. [3,4] The massive data threaten the prevailing von Neumann architecture, whose processing and memory units are physically separated, resulting in low efficiency when handling artificial intelligence (AI) tasks. [5] Process technologies also restrict the development of processors and memorizers along the path of Moore's law, seriously restricting hardware computational power and storage capacity. Although complementary metal-oxide-semiconductor (CMOS) has played a crucial role in modern civilization, it is difficult to satisfy the demand of Big Data and AI era. [6] Therefore, it is inevitable to develop a new chip architecture and device technology. The inspiration for new technology comes from the neural network that is constituted of 10 11 neurons and 10 15 synapses. [7] On this basis, the human brain can not only learn and memorize information autonomously, but also recognize, reason, and process multiple tasks simultaneously, which features low energy consumption, high stability, and parallel processing. Therefore, the computing and memory devices mimicking brain have drawn extensive attention in recent years. [8,9] Among these emerging devices, memristors are expected to realize neuromorphic processors characterizing in situ in-memory computations using networks-on-chip in an event-driven manner. [10] By virtue of the history of the amount of flowing charges, memristors can be applied to artificial neural network (ANN) and data memory. [11][12][13] Flexible memristors can satisfy the requirements of portable, wearable, and implantable applications. [14] The controllability of memristors can ensure data-processing accuracy and stability. Specifically, the memristance modulation can manifest as the mollification of spatial (device-to-device) and temporal (cycle-to-cycle) variability or the realization of interconversions between different resistive switching behaviors. [15,16] The classical memristor consists of top and bottom electrodes and an intermediate functional layer (Figure 1a), which is an easy integration with CMOS and highly extensible two-terminal device. [17][18][19][20] Functional layer is the heart of memristor, and the related materials determine its performance. Since the first physical model of memristor was reported using TiO 2 as a functional layer, many materials, including carbon dots (CDs), have exhibited good memristive performances. [21][22][23] As a kind of carbon material, CDs generally possess remarkable characteristics, including physicochemical stability, The explosive growth of digital communication promotes advanced memory and computing devices in Big Data and artificial intelligence era. In particular, memristors hold great promise for in-memory computing and artificial synapses, expected to break through restrictions on hardware computational power and storage capacity caused by the von Neumann bottleneck and declining Moore's Law. The m...

show abstract

Artificial intelligence and internet of things in small and medium-sized enterprises: A survey

Cited by 223 publications

References 65 publications

Identifying the configurational conditions for marketing analytics use in UK SMEs

Identifying the configurational conditions for marketing analytics use in UK SMEs

AI‐Enabled Sensing and Decision‐Making for IoT Systems

Research Process of Carbon Dots in Memristors

Contact Info

Product

Resources

About